This paper presents applications of Antlion optimization algorithm (ALO) for han- dling optimal economic load dispatch (OELD) problems. Electricity generation cost minimization by controlling power output of all available generating units is a major goal of the problem. ALO is a metaheuristic algorithm based on the hunting process of Antlions. The effect of ALO is investigated by solving a 10-unit system. Each studied case has different objective function and complex level of restraints. Three test cases are employed and arranged according to the complex level in which the first one only considers multi fuel sources while the second case is more complicated by taking valve point loading effects into account. And, the third case is the highest challenge to ALO since the valve effects together with ramp rate limits, prohibited operating zones and spinning reserve constraints are taken into consideration. The comparisons of the result obtained by ALO and other ones indicate the ALO algorithm is more potential than most methods on the solution, the stabilization, and the convergence velocity. Therefore, the ALO method is an effective and promising tool for systems with multi fuel sources and considering complicated constraints.
Coordinated Placement and Setting of FACTS in Electrical Network based on Kal...IJECEIAES
To aid the decision maker, the optimal placement of FACTS in the electrical network is performed through very specific criteria. In this paper, a useful approach is followed; it is based particularly on the use of KalaiSmorodinsky bargaining solution for choosing the best compromise between the different objectives commonly posed to the network manager such as the cost of production, total transmission losses (Tloss), and voltage stability index (Lindex). In the case of many possible solutions, Voltage Profile Quality is added to select the best one. This approach has offered a balanced solution and has proven its effectiveness in finding the best placement and setting of two types of FACTS namely Static Var Compensator (SVC) and Thyristor Controlled Series Compensator (TCSC) in the power system. The test case under investigation is IEEE-14 bus system which has been simulated in MATLAB Environment.
Comparative study of methods for optimal reactive power dispatchelelijjournal
Reactive power dispatch plays a main role in order to provide good facility secure and economic operation
in the power system. In a power system optimal reactive power dispatch is supported to improve the voltage
profile, to reduce losses, to improve voltage stability, to reduce cost etc. This paper presents a brief literature survey of reactive power dispatch and also discusses a comparative study of conventional and evolutionary computation techniques applied for reactive power dispatch. The paper is useful for researchers for further research and study so that it can apply in the various areas of power system
Hybrid method for achieving Pareto front on economic emission dispatch IJECEIAES
In this paper hybrid method, Modified Nondominated Sorted Genetic Algorithm (MNSGA-II) and Modified Population Variant Differential Evolution(MPVDE) have been placed in effect in achieving the best optimal solution of Multiobjective economic emission load dispatch optimization problem. In this technique latter, one is used to enforce the assigned percent of the population and the remaining with the former one. To overcome the premature convergence in an optimization problem diversity preserving operator is employed, from the tradeoff curve the best optimal solution is predicted using fuzzy set theory. This methodology validated on IEEE 30 bus test system with six generators, IEEE 118 bus test system with fourteen generators and with a forty generators test system. The solutions are dissimilitude with the existing metaheuristic methods like Strength Pareto Evolutionary Algorithm-II, Multiobjective differential evolution, Multiobjective Particle Swarm optimization, Fuzzy clustering particle swarm optimization, Nondominated sorting genetic algorithm-II.
An Effectively Modified Firefly Algorithm for Economic Load Dispatch ProblemTELKOMNIKA JOURNAL
This paper proposes an effectively modified firefly algorithm (EMFA) for searching optimal solution of economic load dispatch (ELD) problem. The proposed method is developed by improving the procedure of new solution generation of conventional firefly algorithm (FA). The performance of EMFA is compared to FA variants and other existing methods by testing on four different systems with different types of objective function and constraints. The comparison indicates that the proposed method can reach better optimal solutions than other FA variants and most other existing methods with lower population and lower maximum iteration. As a result, it can lead to a conclusion that the proposed method is potential for ELD problem.
Solving the Power Purchase Cost Optimization Problem with Improved DE AlgorithmIJEACS
Under the deregulation of generation market in China, all distributed generators will particular in electric power bidding. Therefore power purchase cost optimization (PPCO) problem has been getting more attention of power grid Company. However, under the competition principle, they can purchase power from several of power plants, therefor, there exist continuous and integral variables in purchase cost model, which is difficult to solve by classical linear optimization method. An improved differential evolution algorithm is proposed and employed to solve the PPCO problem, which targets on minimum purchase cost, considering the supply and demand balance, generation and transfer capability as constraints. It yields the global optimum solution of the PPCO problem. The numerical results show that the proposed algorithm can solve the PPCO problem and saves the costs of power purchase. It has a widely practical value of application.
Improved ant colony optimization for quantum cost reductionjournalBEEI
Heuristic algorithms play a significant role in synthesize and optimization of digital circuits based on reversible logic yet suffer with multiple disadvantages for multiqubit functions like scalability, run time and memory space. Synthesis of reversible logic circuit ends up with trade off between number of gates, quantum cost, ancillary inputs and garbage outputs. Research on optimization of quantum cost seems intractable. Therefore post synthesis optimization needs to be done for reduction of quantum cost. Many researchers have proposed exact synthesis approaches in reversible logic but focussed on reduction of number of gates yet quantum cost remains undefined. The main goal of this paper is to propose improved ant colony optimization (ACO) algorithm for quantum cost reduction. The research efforts reported in this paper represent a significant contribution towards synthesis and optimization of high complexity reversible function via swarm intelligence based approach. The improved ACO algorithm provides low quantum cost based toffoli synthesis of reversible logic function without long computation overhead.
A Hybrid Formulation between Differential Evolution and Simulated Annealing A...TELKOMNIKA JOURNAL
The aim of this paper is to solve the optimal reactive power dispatch (ORPD) problem.
Metaheuristic algorithms have been extensively used to solve optimization problems in a reasonable time
without requiring in-depth knowledge of the treated problem. The perform ance of a metaheuristic requires
a compromise between exploitation and exploration of the search space. However, it is rarely to have the
two characteristics in the same search method, where the current emergence of hybrid methods. This
paper presents a hybrid formulation between two different metaheuristics: differential evolution (based on a
population of solution) and simulated annealing (based on a unique solution) to solve ORPD. The first one
is characterized with the high capacity of exploration, while the second has a good exploitation of the
search space. For the control variables, a mixed representation (continuous/discrete), is proposed. The
robustness of the method is tested on the IEEE 30 bus test system.
Coordinated Placement and Setting of FACTS in Electrical Network based on Kal...IJECEIAES
To aid the decision maker, the optimal placement of FACTS in the electrical network is performed through very specific criteria. In this paper, a useful approach is followed; it is based particularly on the use of KalaiSmorodinsky bargaining solution for choosing the best compromise between the different objectives commonly posed to the network manager such as the cost of production, total transmission losses (Tloss), and voltage stability index (Lindex). In the case of many possible solutions, Voltage Profile Quality is added to select the best one. This approach has offered a balanced solution and has proven its effectiveness in finding the best placement and setting of two types of FACTS namely Static Var Compensator (SVC) and Thyristor Controlled Series Compensator (TCSC) in the power system. The test case under investigation is IEEE-14 bus system which has been simulated in MATLAB Environment.
Comparative study of methods for optimal reactive power dispatchelelijjournal
Reactive power dispatch plays a main role in order to provide good facility secure and economic operation
in the power system. In a power system optimal reactive power dispatch is supported to improve the voltage
profile, to reduce losses, to improve voltage stability, to reduce cost etc. This paper presents a brief literature survey of reactive power dispatch and also discusses a comparative study of conventional and evolutionary computation techniques applied for reactive power dispatch. The paper is useful for researchers for further research and study so that it can apply in the various areas of power system
Hybrid method for achieving Pareto front on economic emission dispatch IJECEIAES
In this paper hybrid method, Modified Nondominated Sorted Genetic Algorithm (MNSGA-II) and Modified Population Variant Differential Evolution(MPVDE) have been placed in effect in achieving the best optimal solution of Multiobjective economic emission load dispatch optimization problem. In this technique latter, one is used to enforce the assigned percent of the population and the remaining with the former one. To overcome the premature convergence in an optimization problem diversity preserving operator is employed, from the tradeoff curve the best optimal solution is predicted using fuzzy set theory. This methodology validated on IEEE 30 bus test system with six generators, IEEE 118 bus test system with fourteen generators and with a forty generators test system. The solutions are dissimilitude with the existing metaheuristic methods like Strength Pareto Evolutionary Algorithm-II, Multiobjective differential evolution, Multiobjective Particle Swarm optimization, Fuzzy clustering particle swarm optimization, Nondominated sorting genetic algorithm-II.
An Effectively Modified Firefly Algorithm for Economic Load Dispatch ProblemTELKOMNIKA JOURNAL
This paper proposes an effectively modified firefly algorithm (EMFA) for searching optimal solution of economic load dispatch (ELD) problem. The proposed method is developed by improving the procedure of new solution generation of conventional firefly algorithm (FA). The performance of EMFA is compared to FA variants and other existing methods by testing on four different systems with different types of objective function and constraints. The comparison indicates that the proposed method can reach better optimal solutions than other FA variants and most other existing methods with lower population and lower maximum iteration. As a result, it can lead to a conclusion that the proposed method is potential for ELD problem.
Solving the Power Purchase Cost Optimization Problem with Improved DE AlgorithmIJEACS
Under the deregulation of generation market in China, all distributed generators will particular in electric power bidding. Therefore power purchase cost optimization (PPCO) problem has been getting more attention of power grid Company. However, under the competition principle, they can purchase power from several of power plants, therefor, there exist continuous and integral variables in purchase cost model, which is difficult to solve by classical linear optimization method. An improved differential evolution algorithm is proposed and employed to solve the PPCO problem, which targets on minimum purchase cost, considering the supply and demand balance, generation and transfer capability as constraints. It yields the global optimum solution of the PPCO problem. The numerical results show that the proposed algorithm can solve the PPCO problem and saves the costs of power purchase. It has a widely practical value of application.
Improved ant colony optimization for quantum cost reductionjournalBEEI
Heuristic algorithms play a significant role in synthesize and optimization of digital circuits based on reversible logic yet suffer with multiple disadvantages for multiqubit functions like scalability, run time and memory space. Synthesis of reversible logic circuit ends up with trade off between number of gates, quantum cost, ancillary inputs and garbage outputs. Research on optimization of quantum cost seems intractable. Therefore post synthesis optimization needs to be done for reduction of quantum cost. Many researchers have proposed exact synthesis approaches in reversible logic but focussed on reduction of number of gates yet quantum cost remains undefined. The main goal of this paper is to propose improved ant colony optimization (ACO) algorithm for quantum cost reduction. The research efforts reported in this paper represent a significant contribution towards synthesis and optimization of high complexity reversible function via swarm intelligence based approach. The improved ACO algorithm provides low quantum cost based toffoli synthesis of reversible logic function without long computation overhead.
A Hybrid Formulation between Differential Evolution and Simulated Annealing A...TELKOMNIKA JOURNAL
The aim of this paper is to solve the optimal reactive power dispatch (ORPD) problem.
Metaheuristic algorithms have been extensively used to solve optimization problems in a reasonable time
without requiring in-depth knowledge of the treated problem. The perform ance of a metaheuristic requires
a compromise between exploitation and exploration of the search space. However, it is rarely to have the
two characteristics in the same search method, where the current emergence of hybrid methods. This
paper presents a hybrid formulation between two different metaheuristics: differential evolution (based on a
population of solution) and simulated annealing (based on a unique solution) to solve ORPD. The first one
is characterized with the high capacity of exploration, while the second has a good exploitation of the
search space. For the control variables, a mixed representation (continuous/discrete), is proposed. The
robustness of the method is tested on the IEEE 30 bus test system.
Optimum designing of a transformer considering lay out constraints by penalty...INFOGAIN PUBLICATION
Optimum designing of power electrical equipment and devices play a leading role in attaining optimal performance and price of equipments in electric power industry. Optimum transformer design considering multiple constraints is acquired using optimal determination of geometric parameters of transformer with respect to its magnetic and electric properties. As it is well known, every optimization problem requires an objective function to be minimized. In this paper optimum transformer design problem comprises minimization of transformers mean core mass and its windings by satisfying multiple constraints according to transformers ratings and international standards using a penalty-based method. Hybrid big bang-big crunch algorithm is applied to solve the optimization problem and results are compared to other methods. Proposed method has provided a reliable optimization solution and has guaranteed access to a global optimum. Simulation result indicates that using the proposed algorithm, transformer parameters such as core mass, efficiency and dimensions are remarkably improved. Moreover simulation time using this algorithm is quit less in comparison to other approaches.
Use of Evolutionary Polynomial Regression (EPR) for Prediction of Total Sedim...CSCJournals
This study presents the use of Evolutionary Polynomial Regression (EPR) in predicting the total sediment load of ten selected rivers in Malaysia. EPR is a data-driven hybrid technique, based on evolutionary computing. In order to apply the method, the extensive database of the Department of Irrigation and Drainage (DID), Ministry of Natural Resources & Environment, Malaysia was sought, and unrestricted access was granted. The EPR technique produced greatly improved results compared to other previous sediment load methods. A robustness study was performed in order to confirm the generalisation ability of the developed EPR model, and a sensitivity analysis was also conducted to determine the relative importance of model inputs. The performance of the EPR model demonstrates its predictive capability and generalisation ability to solve highly nonlinear problems of river engineering applications, such as sediment.
Hybrid Genetic Algorithms and Simulated Annealing for Multi-trip Vehicle Rout...IJECEIAES
Vehicle routing problem with time windows (VRPTW) is one of NP-hard problem. Multi-trip is approach to solve the VRPTW that looking trip scheduling for gets best result. Even though there are various algorithms for the problem, there is opportunity to improve the existing algorithms in order gaining a better result. In this research, genetic algoritm is hybridized with simulated annealing algoritm to solve the problem. Genetic algoritm is employed to explore global search area and simulated annealing is employed to exploit local search area. Four combination types of genetic algorithm and simulated annealing (GA-SA) are tested to get the best solution. The computational experiment shows that GA-SA1 and GA-SA4 can produced the most optimal fitness average values with each value was 1.0888 and 1.0887. However GA-SA4 can found the best fitness chromosome faster than GA-SA1.
Hybrid Quantum Genetic Particle Swarm Optimization Algorithm For Solving Opti...paperpublications3
Abstract: This paper presents hybrid particle swarm algorithm for solving the multi-objective reactive power dispatch problem. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. Evolutionary algorithm and Swarm Intelligence algorithm (EA, SI), a part of Bio inspired optimization algorithm, have been widely used to solve numerous optimization problem in various science and engineering domains. In this paper, a framework of hybrid particle swarm optimization algorithm, called Hybrid quantum genetic particle swarm optimization (HQGPSO), is proposed by reasonably combining the Q-bit evolutionary search of quantum particle swarm optimization (QPSO) algorithm and binary bit evolutionary search of genetic particle swarm optimization (GPSO) in order to achieve better optimization performances. The proposed HQGPSO also can be viewed as a kind of hybridization of micro-space based search and macro-space based search, which enriches the searching behavior to enhance and balance the exploration and exploitation abilities in the whole searching space. In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other algorithms.
Keywords: quantum particle swarm optimization, genetic particle swarm optimization, hybrid algorithm Optimization, Swarm Intelligence, optimal reactive power, Transmission loss.
Simulation-based Optimization of a Real-world Travelling Salesman Problem Usi...CSCJournals
This paper presents a real-world case study of optimizing waste collection in Sweden. The problem, involving approximately 17,000 garbage bins served by three bin lorries, is approached as a travelling salesman problem and solved using simulation-based optimization and an evolutionary algorithm. To improve the performance of the evolutionary algorithm, it is enhanced with a repair function that adjusts its genome values so that shorter routes are found more quickly. The algorithm is tested using two crossover operators, i.e., the order crossover and heuristic crossover, combined with different mutation rates. The results indicate that the order crossover is superior to the heuristics crossover, but that the driving force of the search process is the mutation operator combined with the repair function.
BINARY SINE COSINE ALGORITHMS FOR FEATURE SELECTION FROM MEDICAL DATAacijjournal
A well-constructed classification model highly depends on input feature subsets from a dataset, which may contain redundant, irrelevant, or noisy features. This challenge can be worse while dealing with medical datasets. The main aim of feature selection as a pre-processing task is to eliminate these features and select the most effective ones. In the literature, metaheuristic algorithms show a successful performance to find optimal feature subsets. In this paper, two binary metaheuristic algorithms named S-shaped binary Sine Cosine Algorithm (SBSCA) and V-shaped binary Sine Cosine Algorithm (VBSCA) are proposed for feature selection from the medical data. In these algorithms, the search space remains continuous, while a binary position vector is generated by two transfer functions S-shaped and V-shaped for each solution. The proposed algorithms are compared with four latest binary optimization algorithms over five medical datasets from the UCI repository. The experimental results confirm that using both bSCA variants enhance the accuracy of classification on these medical datasets compared to four other algorithms.
Feature selection using modified particle swarm optimisation for face recogni...eSAT Journals
Abstract
One of the major influential factors which affects the accuracy of classification rate is the selection of right features. Not all features have vital role in classification. Many of the features in the dataset may be redundant and irrelevant, which increase the computational cost and may reduce classification rate. In this paper, we used DCT(Discrete cosine transform) coefficients as features for face recognition application. The coefficients are optimally selected based on a modified PSO algorithm. In this, the choice of coefficients is done by incorporating the average of the mean normalized standard deviations of various classes and giving more weightage to the lower indexed DCT coefficients. The algorithm is tested on ORL database. A recognition rate of 97% is obtained. Average number of features selected is about 40 percent for a 10 × 10 input. The modified PSO took about 50 iterations for convergence. These performance figures are found to be better than some of the work reported in literature.
Keywords: Particle swarm optimization, Discrete cosine transform, feature extraction, feature selection, face recognition, classification rate.
Recent research in finding the optimal path by ant colony optimizationjournalBEEI
The computation of the optimal path is one of the critical problems in graph theory. It has been utilized in various practical ranges of real world applications including image processing, file carving and classification problem. Numerous techniques have been proposed in finding optimal path solutions including using ant colony optimization (ACO). This is a nature-inspired metaheuristic algorithm, which is inspired by the foraging behavior of ants in nature. Thus, this paper study the improvement made by many researchers on ACO in finding optimal path solution. Finally, this paper also identifies the recent trends and explores potential future research directions in file carving.
Electric distribution network reconfiguration for power loss reduction based ...IJECEIAES
This paper proposes a method for solving the distribution network reconfiguration (NR) problem based on runner root algorithm (RRA) for reducing active power loss. The RRA is a recent developed metaheuristic algorithm inspired from runners and roots of plants to search water and minerals. RRA is equipped with four tools for searching the optimal solution. In which, the random jumps and the restart of population are used for exploring and the elite selection and random jumps around the current best solution are used for exploiting. The effectiveness of the RRA is evaluated on the 16 and 69-node system. The obtained results are compared with particle swarm optimization and other methods. The numerical results show that the RRA is the potential method for the NR problem.
Optimal Distributed Generation Siting and Sizing by Considering Harmonic Limi...Editor IJLRES
Distributed Generation (DG) units are also called as Decentralized Generation and Embedded Generation. The objective is to maximize the DG penetration level, minimization of loss by optimally selecting types, locations and sizes of utility owned DG units. The DG penetration level could be limited by harmonic distortion because of the nonlinear current injected by inverter-based DG units and also protection coordination constraints because of the variation in fault current caused by synchronous-based DG units. Hence the objective is to maximize DG penetration level from both types of DG units, taking into account power balance constraints, total harmonic distortion limits, and protection coordination constraints. The Social Learning Particle Swarm Optimization (SLPSO) algorithm is used to maximize the overall DG penetration level and the proposed system is tested in the IEEE-30 bus system.
THE NEW HYBRID COAW METHOD FOR SOLVING MULTI-OBJECTIVE PROBLEMSijfcstjournal
In this article using Cuckoo Optimization Algorithm and simple additive weighting method the hybrid COAW algorithm is presented to solve multi-objective problems. Cuckoo algorithm is an efficient and structured method for solving nonlinear continuous problems. The created Pareto frontiers of the COAW proposed algorithm are exact and have good dispersion. This method has a high speed in finding the
Pareto frontiers and identifies the beginning and end points of Pareto frontiers properly. In order to validation the proposed algorithm, several experimental problems were analyzed. The results of which indicate the proper effectiveness of COAW algorithm for solving multi-objective problems.
The railway capacity optimization problem deals with the maximization of the number of trains running on
a given network per unit time. In this study, we frame this problem as a typical asymmetrical Travelling
Salesman Problem (ATSP), with the ATSP nodes representing the train arrival /departure events and the
ATSP total cost representing the total time-interval of the schedule. The application problem is then
optimized using the standard Ant Colony Optimization (ACO) algorithm. The simulation experiments
validate the formulation of the railway capacity problem as an ATSP and the ACO algorithm produces
optimal solutions superior to those produced by the domain experts.
A hybrid optimization algorithm based on genetic algorithm and ant colony opt...ijaia
In optimization problem, Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO) have
been known as good alternative techniques. GA is designed by adopting the natural evolution process,
while ACO is inspired by the foraging behaviour of ant species. This paper presents a hybrid GA-ACO for
Travelling Salesman Problem (TSP), called Genetic Ant Colony Optimization (GACO). In this method, GA
will observe and preserve the fittest ant in each cycle in every generation and only unvisited cities will be
assessed by ACO. From experimental result, GACO performance is significantly improved and its time
complexity is fairly equal compared to the GA and ACO.
Finding optimal reactive power dispatch solutions by using a novel improved s...TELKOMNIKA JOURNAL
In this paper, a novel improved Stochastic Fractal Search optimization algorithm (ISFSOA) is
proposed for finding effective solutions of a complex optimal reactive power dispatch (ORPD) problem with
consideration of all constraints in transmission power network. Three different objectives consisting of total
power loss (TPL), total voltage deviation (TVD) and voltage stabilization enhancement index are
independently optimized by running the proposed ISFSOA and standard Stochastic Fractal Search
optimization algorithm (SFSOA). The potential search of the proposed ISFSOA can be highly improved
since diffusion process of SFSOA is modified. Compared to SFSOA, the proposed method can explore
large search zones and exploit local search zones effectively based on the comparison of solution quality.
One standard IEEE 30-bus system with three study cases is employed for testing the proposed method
and compared to other so far applied methods. For each study case, the proposed method together with
SFSOA are run fifty run and three main results consisting of the best, mean and standard deviation fitness
function are compared. The indication is that the proposed method can find more promising solutions for
the three cases and its search ability is always more stable than those of SFSOA. The comparison with
other methods also give the same evaluation that the proposed method can be superior to almost all
compared methods. As a result, it can conclude that the proposed modification is really appropriate for
SFSOA in dealing with ORPD problem and the method can be used for other engineering
optimization problems.
In this study, optimal economic load dispatch problem (OELD) is resolved by
a novel improved algorithm. The proposed modified moth swarm algorithm
(MMSA), is developed by proposing two modifications on the classical moth
swarm algorithm (MSA). The first modification applies an effective formula
to replace an ineffective formula of the mutation technique. The second
modification is to cancel the crossover technique. For proving the efficient
improvements of the proposed method, different systems with discontinuous
objective functions as well as complicated constraints are used. Experiment
results on the investigated cases show that the proposed method can get less
cost and achieve stable search ability than MSA. As compared to other
previous methods, MMSA can archive equal or better results. From this view,
it can give a conclusion that MMSA method can be valued as a useful method
for OELD problem.
Optimization of Corridor Observation Method to Solve Environmental and Econom...ijceronline
This paper presents an optimization of corridor observation method (COM) which is an applicable optimization algorithm based on the evolutionary algorithm to solve an environmental and economic Dispatch (EED) problem. This problem is seen like a bi-objective optimization problem where fuel cost and gas emission are objectives. In this method, the optimal Pareto front is found using the concept of corridor observation and the best compromised solution is obtained by fuzzy logic. The optimization of this method consists to find best parameters (number of corridor, number of initial population and number of generation) which improve solution and reduce a computational time. The simulated results using power system with different numbers of generation units showed that the new parameters ameliorate the solution keep her stability and reduce considerably the CPU time (time is minimum divide by 4) comparatively at parameterization with originals parameters.
Tap changer optimisation using embedded differential evolutionary programming...journalBEEI
Over-compensation and under-compensation phenomena are two undesirable results in power system compensation. This will be not a good option in power system planning and operation. The non-optimal values of the compensating parameters subjected to a power system have contributed to these phenomena. Thus, a reliable optimization technique is mandatory to alleviate this issue. This paper presents a stochastic optimization technique used to fix the power loss control in a high demand power system due to the load increase, which causes the voltage decay problems leading to current increase and system loss increment. A new optimization technique termed as embedded differential evolutionary programming (EDEP) is proposed, which integrates the traditional differential evolution (DE) and evolutionary programming (EP). Consequently, EDEP was for solving optimizations problem in power system through the tap changer optimizations scheme. Results obtained from this study are significantly superior compared to the traditional EP with implementation on the IEEE 30-bus reliability test system (RTS) for the loss minimization scheme.
Optimum designing of a transformer considering lay out constraints by penalty...INFOGAIN PUBLICATION
Optimum designing of power electrical equipment and devices play a leading role in attaining optimal performance and price of equipments in electric power industry. Optimum transformer design considering multiple constraints is acquired using optimal determination of geometric parameters of transformer with respect to its magnetic and electric properties. As it is well known, every optimization problem requires an objective function to be minimized. In this paper optimum transformer design problem comprises minimization of transformers mean core mass and its windings by satisfying multiple constraints according to transformers ratings and international standards using a penalty-based method. Hybrid big bang-big crunch algorithm is applied to solve the optimization problem and results are compared to other methods. Proposed method has provided a reliable optimization solution and has guaranteed access to a global optimum. Simulation result indicates that using the proposed algorithm, transformer parameters such as core mass, efficiency and dimensions are remarkably improved. Moreover simulation time using this algorithm is quit less in comparison to other approaches.
Use of Evolutionary Polynomial Regression (EPR) for Prediction of Total Sedim...CSCJournals
This study presents the use of Evolutionary Polynomial Regression (EPR) in predicting the total sediment load of ten selected rivers in Malaysia. EPR is a data-driven hybrid technique, based on evolutionary computing. In order to apply the method, the extensive database of the Department of Irrigation and Drainage (DID), Ministry of Natural Resources & Environment, Malaysia was sought, and unrestricted access was granted. The EPR technique produced greatly improved results compared to other previous sediment load methods. A robustness study was performed in order to confirm the generalisation ability of the developed EPR model, and a sensitivity analysis was also conducted to determine the relative importance of model inputs. The performance of the EPR model demonstrates its predictive capability and generalisation ability to solve highly nonlinear problems of river engineering applications, such as sediment.
Hybrid Genetic Algorithms and Simulated Annealing for Multi-trip Vehicle Rout...IJECEIAES
Vehicle routing problem with time windows (VRPTW) is one of NP-hard problem. Multi-trip is approach to solve the VRPTW that looking trip scheduling for gets best result. Even though there are various algorithms for the problem, there is opportunity to improve the existing algorithms in order gaining a better result. In this research, genetic algoritm is hybridized with simulated annealing algoritm to solve the problem. Genetic algoritm is employed to explore global search area and simulated annealing is employed to exploit local search area. Four combination types of genetic algorithm and simulated annealing (GA-SA) are tested to get the best solution. The computational experiment shows that GA-SA1 and GA-SA4 can produced the most optimal fitness average values with each value was 1.0888 and 1.0887. However GA-SA4 can found the best fitness chromosome faster than GA-SA1.
Hybrid Quantum Genetic Particle Swarm Optimization Algorithm For Solving Opti...paperpublications3
Abstract: This paper presents hybrid particle swarm algorithm for solving the multi-objective reactive power dispatch problem. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. Evolutionary algorithm and Swarm Intelligence algorithm (EA, SI), a part of Bio inspired optimization algorithm, have been widely used to solve numerous optimization problem in various science and engineering domains. In this paper, a framework of hybrid particle swarm optimization algorithm, called Hybrid quantum genetic particle swarm optimization (HQGPSO), is proposed by reasonably combining the Q-bit evolutionary search of quantum particle swarm optimization (QPSO) algorithm and binary bit evolutionary search of genetic particle swarm optimization (GPSO) in order to achieve better optimization performances. The proposed HQGPSO also can be viewed as a kind of hybridization of micro-space based search and macro-space based search, which enriches the searching behavior to enhance and balance the exploration and exploitation abilities in the whole searching space. In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other algorithms.
Keywords: quantum particle swarm optimization, genetic particle swarm optimization, hybrid algorithm Optimization, Swarm Intelligence, optimal reactive power, Transmission loss.
Simulation-based Optimization of a Real-world Travelling Salesman Problem Usi...CSCJournals
This paper presents a real-world case study of optimizing waste collection in Sweden. The problem, involving approximately 17,000 garbage bins served by three bin lorries, is approached as a travelling salesman problem and solved using simulation-based optimization and an evolutionary algorithm. To improve the performance of the evolutionary algorithm, it is enhanced with a repair function that adjusts its genome values so that shorter routes are found more quickly. The algorithm is tested using two crossover operators, i.e., the order crossover and heuristic crossover, combined with different mutation rates. The results indicate that the order crossover is superior to the heuristics crossover, but that the driving force of the search process is the mutation operator combined with the repair function.
BINARY SINE COSINE ALGORITHMS FOR FEATURE SELECTION FROM MEDICAL DATAacijjournal
A well-constructed classification model highly depends on input feature subsets from a dataset, which may contain redundant, irrelevant, or noisy features. This challenge can be worse while dealing with medical datasets. The main aim of feature selection as a pre-processing task is to eliminate these features and select the most effective ones. In the literature, metaheuristic algorithms show a successful performance to find optimal feature subsets. In this paper, two binary metaheuristic algorithms named S-shaped binary Sine Cosine Algorithm (SBSCA) and V-shaped binary Sine Cosine Algorithm (VBSCA) are proposed for feature selection from the medical data. In these algorithms, the search space remains continuous, while a binary position vector is generated by two transfer functions S-shaped and V-shaped for each solution. The proposed algorithms are compared with four latest binary optimization algorithms over five medical datasets from the UCI repository. The experimental results confirm that using both bSCA variants enhance the accuracy of classification on these medical datasets compared to four other algorithms.
Feature selection using modified particle swarm optimisation for face recogni...eSAT Journals
Abstract
One of the major influential factors which affects the accuracy of classification rate is the selection of right features. Not all features have vital role in classification. Many of the features in the dataset may be redundant and irrelevant, which increase the computational cost and may reduce classification rate. In this paper, we used DCT(Discrete cosine transform) coefficients as features for face recognition application. The coefficients are optimally selected based on a modified PSO algorithm. In this, the choice of coefficients is done by incorporating the average of the mean normalized standard deviations of various classes and giving more weightage to the lower indexed DCT coefficients. The algorithm is tested on ORL database. A recognition rate of 97% is obtained. Average number of features selected is about 40 percent for a 10 × 10 input. The modified PSO took about 50 iterations for convergence. These performance figures are found to be better than some of the work reported in literature.
Keywords: Particle swarm optimization, Discrete cosine transform, feature extraction, feature selection, face recognition, classification rate.
Recent research in finding the optimal path by ant colony optimizationjournalBEEI
The computation of the optimal path is one of the critical problems in graph theory. It has been utilized in various practical ranges of real world applications including image processing, file carving and classification problem. Numerous techniques have been proposed in finding optimal path solutions including using ant colony optimization (ACO). This is a nature-inspired metaheuristic algorithm, which is inspired by the foraging behavior of ants in nature. Thus, this paper study the improvement made by many researchers on ACO in finding optimal path solution. Finally, this paper also identifies the recent trends and explores potential future research directions in file carving.
Electric distribution network reconfiguration for power loss reduction based ...IJECEIAES
This paper proposes a method for solving the distribution network reconfiguration (NR) problem based on runner root algorithm (RRA) for reducing active power loss. The RRA is a recent developed metaheuristic algorithm inspired from runners and roots of plants to search water and minerals. RRA is equipped with four tools for searching the optimal solution. In which, the random jumps and the restart of population are used for exploring and the elite selection and random jumps around the current best solution are used for exploiting. The effectiveness of the RRA is evaluated on the 16 and 69-node system. The obtained results are compared with particle swarm optimization and other methods. The numerical results show that the RRA is the potential method for the NR problem.
Optimal Distributed Generation Siting and Sizing by Considering Harmonic Limi...Editor IJLRES
Distributed Generation (DG) units are also called as Decentralized Generation and Embedded Generation. The objective is to maximize the DG penetration level, minimization of loss by optimally selecting types, locations and sizes of utility owned DG units. The DG penetration level could be limited by harmonic distortion because of the nonlinear current injected by inverter-based DG units and also protection coordination constraints because of the variation in fault current caused by synchronous-based DG units. Hence the objective is to maximize DG penetration level from both types of DG units, taking into account power balance constraints, total harmonic distortion limits, and protection coordination constraints. The Social Learning Particle Swarm Optimization (SLPSO) algorithm is used to maximize the overall DG penetration level and the proposed system is tested in the IEEE-30 bus system.
THE NEW HYBRID COAW METHOD FOR SOLVING MULTI-OBJECTIVE PROBLEMSijfcstjournal
In this article using Cuckoo Optimization Algorithm and simple additive weighting method the hybrid COAW algorithm is presented to solve multi-objective problems. Cuckoo algorithm is an efficient and structured method for solving nonlinear continuous problems. The created Pareto frontiers of the COAW proposed algorithm are exact and have good dispersion. This method has a high speed in finding the
Pareto frontiers and identifies the beginning and end points of Pareto frontiers properly. In order to validation the proposed algorithm, several experimental problems were analyzed. The results of which indicate the proper effectiveness of COAW algorithm for solving multi-objective problems.
The railway capacity optimization problem deals with the maximization of the number of trains running on
a given network per unit time. In this study, we frame this problem as a typical asymmetrical Travelling
Salesman Problem (ATSP), with the ATSP nodes representing the train arrival /departure events and the
ATSP total cost representing the total time-interval of the schedule. The application problem is then
optimized using the standard Ant Colony Optimization (ACO) algorithm. The simulation experiments
validate the formulation of the railway capacity problem as an ATSP and the ACO algorithm produces
optimal solutions superior to those produced by the domain experts.
A hybrid optimization algorithm based on genetic algorithm and ant colony opt...ijaia
In optimization problem, Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO) have
been known as good alternative techniques. GA is designed by adopting the natural evolution process,
while ACO is inspired by the foraging behaviour of ant species. This paper presents a hybrid GA-ACO for
Travelling Salesman Problem (TSP), called Genetic Ant Colony Optimization (GACO). In this method, GA
will observe and preserve the fittest ant in each cycle in every generation and only unvisited cities will be
assessed by ACO. From experimental result, GACO performance is significantly improved and its time
complexity is fairly equal compared to the GA and ACO.
Finding optimal reactive power dispatch solutions by using a novel improved s...TELKOMNIKA JOURNAL
In this paper, a novel improved Stochastic Fractal Search optimization algorithm (ISFSOA) is
proposed for finding effective solutions of a complex optimal reactive power dispatch (ORPD) problem with
consideration of all constraints in transmission power network. Three different objectives consisting of total
power loss (TPL), total voltage deviation (TVD) and voltage stabilization enhancement index are
independently optimized by running the proposed ISFSOA and standard Stochastic Fractal Search
optimization algorithm (SFSOA). The potential search of the proposed ISFSOA can be highly improved
since diffusion process of SFSOA is modified. Compared to SFSOA, the proposed method can explore
large search zones and exploit local search zones effectively based on the comparison of solution quality.
One standard IEEE 30-bus system with three study cases is employed for testing the proposed method
and compared to other so far applied methods. For each study case, the proposed method together with
SFSOA are run fifty run and three main results consisting of the best, mean and standard deviation fitness
function are compared. The indication is that the proposed method can find more promising solutions for
the three cases and its search ability is always more stable than those of SFSOA. The comparison with
other methods also give the same evaluation that the proposed method can be superior to almost all
compared methods. As a result, it can conclude that the proposed modification is really appropriate for
SFSOA in dealing with ORPD problem and the method can be used for other engineering
optimization problems.
In this study, optimal economic load dispatch problem (OELD) is resolved by
a novel improved algorithm. The proposed modified moth swarm algorithm
(MMSA), is developed by proposing two modifications on the classical moth
swarm algorithm (MSA). The first modification applies an effective formula
to replace an ineffective formula of the mutation technique. The second
modification is to cancel the crossover technique. For proving the efficient
improvements of the proposed method, different systems with discontinuous
objective functions as well as complicated constraints are used. Experiment
results on the investigated cases show that the proposed method can get less
cost and achieve stable search ability than MSA. As compared to other
previous methods, MMSA can archive equal or better results. From this view,
it can give a conclusion that MMSA method can be valued as a useful method
for OELD problem.
Optimization of Corridor Observation Method to Solve Environmental and Econom...ijceronline
This paper presents an optimization of corridor observation method (COM) which is an applicable optimization algorithm based on the evolutionary algorithm to solve an environmental and economic Dispatch (EED) problem. This problem is seen like a bi-objective optimization problem where fuel cost and gas emission are objectives. In this method, the optimal Pareto front is found using the concept of corridor observation and the best compromised solution is obtained by fuzzy logic. The optimization of this method consists to find best parameters (number of corridor, number of initial population and number of generation) which improve solution and reduce a computational time. The simulated results using power system with different numbers of generation units showed that the new parameters ameliorate the solution keep her stability and reduce considerably the CPU time (time is minimum divide by 4) comparatively at parameterization with originals parameters.
Tap changer optimisation using embedded differential evolutionary programming...journalBEEI
Over-compensation and under-compensation phenomena are two undesirable results in power system compensation. This will be not a good option in power system planning and operation. The non-optimal values of the compensating parameters subjected to a power system have contributed to these phenomena. Thus, a reliable optimization technique is mandatory to alleviate this issue. This paper presents a stochastic optimization technique used to fix the power loss control in a high demand power system due to the load increase, which causes the voltage decay problems leading to current increase and system loss increment. A new optimization technique termed as embedded differential evolutionary programming (EDEP) is proposed, which integrates the traditional differential evolution (DE) and evolutionary programming (EP). Consequently, EDEP was for solving optimizations problem in power system through the tap changer optimizations scheme. Results obtained from this study are significantly superior compared to the traditional EP with implementation on the IEEE 30-bus reliability test system (RTS) for the loss minimization scheme.
Bi-objective Optimization Apply to Environment a land Economic Dispatch Probl...ijceronline
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Economic Load Dispatch Problem with Valve – Point Effect Using a Binary Bat A...IDES Editor
This paper proposes application of BAT algorithm
for solving economic load dispatch problem. BAT
algorithmic rule is predicated on the localization
characteristics of micro bats. The proposed approach has
been examined and tested with the numerical results of
economic load dispatch problems with three and five
generating units with valve - point loading without
considering prohibited operating zones and ramp rate limits.
The results of the projected BAT formula are compared with
that of other techniques such as lambda iteration, GA, PSO,
APSO, EP, ABC and basic principle. For each case, the
projected algorithmic program outperforms the answer
reported for the existing algorithms. Additionally, the
promising results show the hardness, quick convergence
and potency of the projected technique.
Improved particle swarm optimization algorithms for economic load dispatch co...IJECEIAES
Economic load dispatch problem under the competitive electric market (ELDCEM) is becoming a hot problem that receives a big interest from researchers. A lot of measures are proposed to deal with the problem. In this paper, three versions of PSO method such as conventional particle swarm optimization (PSO), PSO with inertia weight (IWPSO) and PSO with constriction factor (CFPSO) are applied for handling ELDCEM problem. The core duty of the PSO methods is to determine the most optimal power output of generators to obtain total profit as much as possible for generation companies without violation of constraints. These methods are tested on three and ten-unit systems considering payment model for power delivered and different constraints. Results obtained from the PSO methods are compared with each other to evaluate the effectiveness and robustness. As results, IWPSO method is superior to other methods. Besides, comparing the PSO methods with other reported methods also gives a conclusion that IWPSO method is a very strong tool for solving ELDCEM problem because it can obtain the highest profit, fast converge speed and simulation time.
Economic dispatch by optimization techniquesIJECEIAES
The current paper offers the solution strategy for the economic dispatch problem in electric power system implementing ant lion optimization algorithm (ALOA) and bat algorithm (BA) techniques. In the power network, the economic dispatch (ED) is a short-term calculation of the optimum performance of several electricity generations or a plan of outputs of all usable power generation units from the energy produced to fulfill the necessary demand, although equivalent and unequal specifications need to be achieved at minimal fuel and carbon pollution costs. In this paper, two recent meta-heuristic approaches are introduced, the BA and ALOA. A rigorous stochastically developmental computing strategy focused on the action and intellect of ant lions is an ALOA. The ALOA imitates ant lions' hunting process. The introduction of a numerical description of its biological actions for the solution of ED in the power framework. These algorithms are applied to two systems: a small scale three generator system and a large scale six generator. Results show were compared on the metrics of convergence rate, cost, and average run time that the ALOA and BA are suitable for economic dispatch studies which is clear in the comparison set with other algorithms. Both of these algorithms are tested on IEEE-30 bus reliability test system.
Genetic Algorithm for Solving the Economic Load DispatchSatyendra Singh
In this paper, comparative study of two approaches, Genetic Algorithm
(GA) and Lambda Iteration method (LIM) have been used to provide
the solution of the economic load dispatch (ELD) problem. The ELD
problem is defined as to minimize the total operating cost of a power
system while meeting the total load plus transmission losses within
generation limits. GA and LIM have been used individually for solving
two cases, first is three generator test system and second is ten
generator test system. The results are compared which reveals that GA
can provide more accurate results with fast convergence characteristics
and is superior to LIM.
Application of a new constraint handling method for economic dispatch conside...journalBEEI
In this paper, optimal load dispatch problem under competitive electric market (OLDCEM) is solved by the combination of cuckoo search algorithm (CSA) and a new constraint handling approach, called modified cuckoo search algorithm (MCSA). In addition, we also employ the constraint handling method for salp swarm algorithm (SSA) and particle swarm optimization algorithm (PSO) to form modified SSA (MSSA) and modified PSO (MPSO). The three methods have been tested on 3-unit system and 10-unit system under the consideration of payment model for power reserve allocated, and constraints of system and generators. Result comparisons among MCSA and CSA indicate that the proposed constraint handling method is very useful for metaheuristic algorithms when solving OLDCEM problem. As compared to MSSA, MPSO as well as other previous methods, MCSA is more effective by finding higher total benefit for the two systems with faster manner and lower oscillations. Consequently, MCSA method is a very effective technique for OLDCEM problem in power systems.
IMPROVED SWARM INTELLIGENCE APPROACH TO MULTI OBJECTIVE ED PROBLEMSSuganthi Thangaraj
Electrical power industry restructuring has created highly vibrant and competitive market that altered many aspects of the power industry. In this changed scenario, scarcity of energy resources, increasing power generation cost, environment concern, ever growing demand for electrical energy necessitate optimal economic dispatch. Practical economic dispatch (ED) problems have nonlinear, non-convex type objective function with intense equality and inequality constraints. The conventional optimization methods are not able to solve such problems as due to local optimum solution convergence. Metaheuristic optimization techniques especially Improved Particle Swarm Optimization (IPSO) has gained an incredible recognition as the solution algorithm for such type of ED problems in last decade. The application of IPSO in ED problem, which is considered as one of the most complex optimization problem has been summarized in present paper. This paper illustrates successful implementation of the Improved Particle Swarm Optimization (IPSO) to Economic Load Dispatch Problem (ELD). Power output of each generating unit and optimum fuel cost obtained using IPSO algorithm has been compared with conventional techniques. The results obtained shows that IPSO algorithm converges to optimal fuel cost with reduced computational time when compared to PSO and GA for the three, six and IEEE 30 bus system.
Stochastic fractal search based method for economic load dispatchTELKOMNIKA JOURNAL
This paper presents a nature-inspired meta-heuristic, called a stochastic fractal search based
method (SFS) for coping with complex economic load dispatch (ELD) problem. Two SFS methods are
introduced in the paper by employing two different random walk generators for diffusion process in which
SFS with Gaussian random walk is called SFS-Gauss and SFS with Levy Flight random walk is called
SFS-Levy. The performance of the two applied methods is investigated comparing results obtained from
three test system. These systems with 6, 10, and 20 units with different objective function forms and
different constraints are inspected. Numerical result comparison can confirm that the applied approach has
better solution quality and fast convergence time when compared with some recently published standard,
modified, and hybrid methods. This elucidates that the two SFS methods are very favorable for solving
the ELD problem.
Hybrid Particle Swarm Optimization for Solving Multi-Area Economic Dispatch P...ijsc
We consider the Multi-Area Economic Dispatch problem (MAEDP) in deregulated power system environment for practical multi-area cases with tie line constraints. Our objective is to generate allocation to the power generators in such a manner that the total fuel cost is minimized while all operating constraints are satisfied. This problem is NP-hard. In this paper, we propose Hybrid Particle Swarm Optimization (HGAPSO) to solve MAEDP. The experimental results are reported to show the efficiency of proposed algorithms compared to Particle Swarm Optimization with Time-Varying Acceleration Coefficients (PSO-TVAC) and RCGA.
Optimal power flow with distributed energy sources using whale optimization a...IJECEIAES
Renewable energy generation is increasingly attractive since it is non-polluting and viable. Recently, the technical and economic performance of power system networks has been enhanced by integrating renewable energy sources (RES). This work focuses on the size of solar and wind production by replacing the thermal generation to decrease cost and losses on a big electrical power system. The Weibull and Lognormal probability density functions are used to calculate the deliverable power of wind and solar energy, to be integrated into the power system. Due to the uncertain and intermittent conditions of these sources, their integration complicates the optimal power flow problem. This paper proposes an optimal power flow (OPF) using the whale optimization algorithm (WOA), to solve for the stochastic wind and solar power integrated power system. In this paper, the ideal capacity of RES along with thermal generators has been determined by considering total generation cost as an objective function. The proposed methodology is tested on the IEEE-30 system to ensure its usefulness. Obtained results show the effectiveness of WOA when compared with other algorithms like non-dominated sorting genetic algorithm (NSGA-II), grey wolf optimization (GWO) and particle swarm optimization-GWO (PSOGWO).
This paper discusses the possible applications of particle swarm optimization (PSO) in the Power system. One of the problems in Power System is Economic Load dispatch (ED). The discussion is carried out in view of the saving money, computational speed – up and expandability that can be achieved by using PSO method. The general approach of the method of this paper is that of Dynamic Programming Method coupled with PSO method. The feasibility of the proposed method is demonstrated, and it is compared with the lambda iterative method in terms of the solution quality and computation efficiency. The experimental results show that the proposed PSO method was indeed capable of obtaining higher quality solutions efficiently in ED problems.
Similar to Antlion optimization algorithm for optimal non-smooth economic load dispatch (20)
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
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Antlion optimization algorithm for optimal non-smooth economic load dispatch
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 10, No. 2, April 2020, pp. 1187∼1199
ISSN: 2088-8708, DOI: 10.11591/ijece.v10i2.pp1187-1199 Ì 1187
Antlion optimization algorithm for optimal non-smooth
economic load dispatch
Thanh Pham Van1
, V´aclav Sn´aˇsel2
, Thang Trung Nguyen3
1
Faculty of Electrical Engineering and Computer Science, Technical University of Ostrava, Czech Republic
1
European Cooperation Center, Ton Duc Thang University, Viet Nam
2
Technical University of Ostrava, Czech Republic
3
Power System Optimization Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang
University, Viet Nam
Article Info
Article history:
Received May 26, 2019
Revised Oct 5, 2019
Accepted Oct 11, 2019
Keywords:
Antlion optimization
Multi fuel sources
Ramp rate
Spinning reserve
Valve point loading effects
ABSTRACT
This paper presents applications of Antlion optimization algorithm (ALO) for han-
dling optimal economic load dispatch (OELD) problems. Electricity generation cost
minimization by controlling power output of all available generating units is a major
goal of the problem. ALO is a metaheuristic algorithm based on the hunting process
of Antlions. The effect of ALO is investigated by solving a 10-unit system. Each
studied case has different objective function and complex level of restraints. Three test
cases are employed and arranged according to the complex level in which the first one
only considers multi fuel sources while the second case is more complicated by taking
valve point loading effects into account. And, the third case is the highest challenge to
ALO since the valve effects together with ramp rate limits, prohibited operating zones
and spinning reserve constraints are taken into consideration. The comparisons of the
result obtained by ALO and other ones indicate the ALO algorithm is more potential
than most methods on the solution, the stabilization, and the convergence velocity.
Therefore, the ALO method is an effective and promising tool for systems with multi
fuel sources and considering complicated constraints.
Copyright c 2020 Insitute of Advanced Engineeering and Science.
All rights reserved.
Corresponding Author:
Thang Trung Nguyen,
Power System Optimization Research Group, Faculty of Electrical and Electronics Engineering,
Ton Duc Thang University,
19 Nguyen Huu Tho street, Tan Phong ward, District 7, Ho Chi Minh City, Viet Nam.
Email: nguyentrungthang@tdtu.edu.vn
1. INTRODUCTION
Minimizing electricity generation fuel cost in thermal power plants (TPPs) is extremely important
because it accounts for a high rate of total electricity generation cost. So, the OELD problem has been widely
applied for this purpose. So far solutions which have been just achieved by the OELD problem is to decide the
power output of each thermal generating unit (TGU) so that the electricity generation fuel cost can decrease as
much as possible. In addition, the OELD problem also takes many constraints into account. The constraints
are power balance, spinning reserve, power output limits of generators, prohibited operating zones, and ramp
rate limits. Furthermore, fuel consuming characteristics of TGU such as multi fuel sources and valve point
loading effects are also considered as main issues in the OELD problem. The OELD problem has attracted
many researchers because of its importance in using fuel for the TPPs reasonably. Two main method groups
have used by many authors including traditional numerical methods and modern methods based on artificial
intelligence.
Journal homepage: http://ijece.iaescore.com/index.php/IJECE
2. 1188 Ì ISSN: 2088-8708
Traditional numerical methods have been applied to handle the OELD problem such as Lagrangian
relaxation (LR) method [1], Linear programming techniques (LPT) [2], Fast Newton raphson (FNR) method
[3]. Among the methods, LR is one of the earliest methods which has been applied to systems with a quadratic
fuel cost function. The constraints of the problem were rather simple such as power balance considering trans-
mission losses, voltage limitations, and generation limits. This method was also tested on the 10-unit system
and achieved results also met the requirements of technology at that time. The LPT method was combination
of the Lagrangian method and linear program (LP) method in which duty of the LP method was to linearize
non-linear functions and the Lagrangian method was used as usual. Therefore, when there were many non-
linear constraints, this method would face errors due to linearization. In general, all of the traditional numerical
methods only have considered basic constraints of the OELD problem. Furthermore, these methods had to take
the partial derivative. Consequently, traditional numerical methods will have some restrictions if they handle
the OELD problem with complex constraints.
Unlike traditional numerical methods above, the modern methods have been proposed to handle
the OELD problem more successfully including ANN-based methods (ANN: artificial neural network) and
metaheuristic-based methods. ANN-based methods are comprised of Hopfield neural network (HNN) [4],
Adaptive Hopfield neural networks (AHNN) [5], Augmented Lagrange Hopfield network (ALHN) [6] and
Enhanced Augmented Lagrange Hopfield network (EALHN) [7]. Metaheuristic-based methods have been
widely and more successfully handling OELD problem. Differential evolution algorithm (DEA) [8],
Quantum Evolutionary Algorithm (QEA) [9], Hybrid integer coded differential evolution – dynamic
programming (HICDE-DP) [10], Improved differential evolution algorithm (IDEA) [11], Colonial competi-
tive differential evolution (CCDE) [12], Stud differential evolution (SDE) [13] and Hybrid differential evo-
lution and Lagrange theory (HDE-LH) [14]. Real-coded genetic algorithm (RCGA) and Improved RCGA
(IRCGA) [17]. Hybrid real coded genetic algorithm (HRCGA) [15], Particle swarm optimization (PSO) [16],
Modified particle swarm optimization (MPSO) [18], Quantum-inspired particle swarm optimization
(QIPSO) [19], Distributed sobol PSO and Tabu Search algorithm (TSA) (DSPSO-TSA) [20], Fuzzy and self
adaptive particle swarm optimization (FSAPSO) [21], θ-Particle swarm optimization (θ-PSO) [22],
Cuckoo search algorithm (CSA) [23], [24], Improved CSA (ICSA) [25], Modified CSA (MCSA) [26], Kill herd
algorithm (KHA) [27], Improved KHA (IKHA) [28], Artificial immune systems (AIS) [29], Biogeography-
based optimization (BBO) [30], Chaotic firefly algorithm (CFA) [31], Grey wolf optimizer (GWO) [32], [33],
Crisscross optimization (CO) [34], Exchange market algorithm (EMA) [35], ALO [36], [37], Improved fire-
fly algorithm (IFA) [38], Whale Optimization Algorithm (WOA) [39], Crow search algorithm (CrSA) [40].
Among the DEA method and its different versions, SDE [13] is the best method. This method was created
by using stud crossover operator with intent to restrict the search around low quality solutions. The most
complicated conditions that this method has solved include multi fuel sources and valve point loading effects.
The results show that the SDE method has provided the highest quality results compared to all other
methods including the GA and TSA methods in [20] and HDE-LH in [14]; however, more complicated
constraints like ramp rate, spinning reserve, and prohibited operating zones were not taken into account for
challenging the method. The traditional GA method was difficult to solve OELD problem, but its variants have
been applied to this problem usefully. IRCGA [17] was the strongest method in GA family. In this method, an
efficient real-coded genetic algorithm (RCGA) with arithmetic-average-bound crossover and wavelet mutation
was presented. The solved system by method consists of 10 TGUs with valve point loading effects, multi fuel
sources, ramp rate limits, prohibited operating zones, and spinning reserve. It has proven to be the most effec-
tive when comparing to other methods including PSO, new PSO, DEA, an improved genetic algorithm (IGA).
DSPSO–TSA [20] is better than several methods such as TSA, GA, PSO, and other PSO variants but it was
only considering multi fuel sources and valve point loading effects but power loss constraints, prohibited oper-
ating zones as well as other complicated constraints were not consist of. The IFA method in [38] seems to be
the best method since it was applied for solving systems with the most complicated constraints including both
constraints considered in mentioned work and all constraints in transmission power networks. All test cases
could demonstrate the outstanding performance of the method. As a new approach method, the ALO method
was introduced the first time by Mirjalili in 2015 [41]. Unlike the other algorithms, ALO has two population
sets are an Ant colony and an Antlion colony. According to paper [41], the ALO method has handled several
mathematical functions and some engineering problems such as three classical engineering problems including
three-bar truss design, cantilever beam design, and gear train design. The ALO method also has been proposed
for handling the OELD problem. For example, in [36] the OELD problem has handled with simple constraints
Int J Elec & Comp Eng, Vol. 10, No. 2, April 2020 : 1187 – 1199
3. Int J Elec & Comp Eng ISSN: 2088-8708 Ì 1189
such as power balance and generator limits constraint, or in [37] the method has tested on small-scale power
systems considering valve point effects. The ALO method has shown its potential search including several
systems of the OELD problem as in [36], [37]. However, the complex level of considered systems was not
large and complicated enough to decide its performance. So, in order to clarify further for the efficiency of
ALO need to be more research.
In this paper, the ALO method has been applied for handling the OELD problem with the most con-
straints and different fuel consuming characteristics. The set of constraints is power balance, spinning reserve,
power output limits of the TGU, prohibited operating zones and ramp rate limits. Fuel consuming characteris-
tics are directly related to objective functions such as piecewise quadratic functions and non-convex piecewise
function. The method has been tested on three study cases and obtained results have been compared to other
methods for investigation of the ALO method.
2. PROBLEM FORMULATION
2.1. Major objective of the problem
In operation process of TPPs using fossil fuels, electricity generation cost is required to be optimized.
It can be mathematically formulated by:
F =
n
s=1
Fs(Ps) (1)
where n is number of TGUs; Fs(Ps) is the fuel cost function of the sth
TGU.
When the system has one fuel type, the fuel cost function of the TGU can be presented in a single
quadratic form as follows:
Fs(Ps) = αsP2
s + βsPs + γs (2)
where αs, βs, γs are the cost coefficients of the sth
TGU; and Ps is power output of the sth
TGU.
In the case of the multi fuel sources, the fuel cost function of each generator should be represented by
a piecewise quadratic function as shown in (3). However, with the case of valve effects, the cost function is
more complicated as given in (4):
Fs(Ps) =
αs1P2
s + βs1Ps + γs1, fuel 1
αs2P2
s + βs2Ps + γs2, fuel 2
...
αsmP2
s + βsmPs + γsm, fuel m
(3)
Fs(Ps) =
αs1P2
s + βs1Ps + γs1 + |δs1 x sin( s1(Ps,min − Ps))|, fuel 1
αs2P2
s + βs2Ps + γs2 + |δs2 x sin( s2(Ps,min − Ps))|, fuel 2
...
αsmP2
s + βsmPs + γsm + |δsm x sin( sm(Ps,min − Ps))|, fuel m.
(4)
where αsm, βsm, γsm, δsm, sm are the cost coefficients of the sth
TGU; m is number of the fuel types; and
Ps,min is the minimum power output of the sth
TGU.
2.2. Constraints of power system and generator
2.2.1. Real power balance
The total power generation should meet the total load demand Pdemand plus transmission losses Ploss
as the following rule:
n
s=1
Ps = Pdemand + Ploss (5)
where Ploss is calculated by using Kron’s formula below:
Ploss =
n
s=1
n
h=1
PsBshPh +
n
s=1
B0sPs + B00 (6)
where Bsh, B0s, and B00 are loss coefficients.
Antlion optimization algorithm for optimal non-smooth... (Thanh Pham Van)
4. 1190 Ì ISSN: 2088-8708
2.2.2. Spinning reserve constraint
All TGUs are required that total active power reserve of them should be more than or equal to the
largest generating unit. The constraint requires total active power reserve of all units Prs must be at least equal
to the power system requirement Ppr.
n
s=1
Prs ≥ Ppr (7)
2.2.3. Generating capacity constraint
The power output of each generator must not exceed its operating limits described by
the following rule:
Ps,min ≤ Ps ≤ Ps,max, for s = 1, 2, ... n (8)
where Ps,max is the highest acceptable working power of the sth
TGU.
2.2.4. Ramp rate constraint
Because each TGU cannot change its power output with a high step compared to its previous genera-
tion. Thus, two major conditions are added as the following inequalities:
Ps − P0
s ≤ Pru
s for the case of increasing power (9)
P0
s − Ps ≤ Prd
s for the case of decreasing power (10)
where P0
s is initial power from the previous operating hour of generating unit; Pru
s and Prd
s are ramp up limit
and ramp down limit of the sth
TGU.
2.2.5. Prohibited operating zones
Due to engineering reason that generating units must avoid operating in several operating zones
as shown in the following model:
Pmin
s,h ≤ Ps ≤ Pmax
s,h (11)
where Pmin
s,h and Pmax
s,h are the minimum and maximum power output of the sth
TGU in the hth
prohibited
operating zone.
3. ANTLION OPTIMIZATION ALGORITHM
Initialization: In initialization of ALO algorithm, the population of Antlions is randomly produced
within the upper and lower limitations as the following model:
ALOd = CV min
+ rand(CV max
− CV min
) ; d = 1,..., Npop (12)
where Npop is the population size, and CV max
and CV min
are maximum and minimum limitations
of control variables.
Random walk of Ant: The movement direction of Ants does not follow any rules and it is also
a random walk as described in the following model:
RWCI = [ 0,
1
CI=1
(2 ∗ αCI − 1),
2
CI=1
(2 ∗ αCI − 1),
3
CI=1
(2 ∗ αCI − 1), . . . ,
Gmax
CI=1
(2 ∗ αCI − 1) ] (13)
where CI is an ordinal number of the current iteration; Gmax is the maximum number of iterations; αCI
is considered as a moving factor; and calculated by:
αCI =
1 if α ≥ 0.5
0 otherwise
(14)
Int J Elec & Comp Eng, Vol. 10, No. 2, April 2020 : 1187 – 1199
5. Int J Elec & Comp Eng ISSN: 2088-8708 Ì 1191
The restricted space of Ant: The active radius of the jth
Ant would be more and more decreased
adaptively when the current number of iterations is increased. For mathematically modeling this behavior,
the following equations are used:
cj,CI =
CV min
j
λ
and dj,CI =
CV max
j
λ
(15)
where cj,CI and dj,CI are down and up limitations in the active range of the jth
Ant at iteration CI; and is
a factor λ obtained by:
λ = 10β CI
Gmax
(16)
where β is a constant defined based on the current iteration CI (β = 2 when CI > 0.1*Gmax, β = 3 when CI >
0.5*Gmax, β = 4 when CI > 0.75*Gmax, β = 5 when CI > 0.9*Gmax, β = 6 when CI > 0.95*Gmax)
Sliding Ant toward Antlion: The range of activity of Ant is affected by behavior of shooting sands
of Antlion. This made Ant sliding to the bottom of the trap where the massive jaw was waiting to catch prey.
To describe the assumption, the two following equations are necessary:
Xmin
j,CI = ALOj,CI + cj,CI and Xmax
j,CI = ALOj,CI + dj,CI (17)
where ALOj,CI is the position of the jth
Antlion at the CIth
iteration; Xmax
j,CI and Xmin
j,CI are corresponding to
newly updated upper and lower limits of control variables included in the position of Antlions.
The movement every Ant: The movement of ants is corresponding to the determination of search
zones since random walk in (13) only produces an updated step size that is not related to new solutions.
The random walk position of ant can be updated by the following model:
Xj,CI =
(RWCI − RWmin
) x (Xmax
j,CI − Xmin
j,CI)
RWmax − RWmin
+ Xmin
j,CI (18)
where RWmax
and RWmin
are the minimum and maximum values of RWCI respectively.
As a result, the real position of Ant is updated by using two random walks around Xj,CI and the
current best solution. The purpose is to use information exchange between two other positions. The real
position is obtained by:
Antj,CI =
XRW
j,CI + GbestRW
CI
2
(19)
where XRW
j,CI is a new solution around Xj,CI by using random walk; GbestRW
CI is a new solution nearby
the best current solution GbestCI.
The phase of catching prey: In the process, the assumption is that the action of catching prey happens
when Ants goes inside sand. The following equation is proposed
in this regard:
Antlionj,CI = Antj,CI if FF(Antj,CI) < FF(Antlionj,CI) (20)
where FF(Antj,CI) and FF(Antlionj,CI) are the fitness function of Antj,CI and Antlionj,CI respectively.
All steps ALO method has been summarized as Figure 1.
4. IMPLEMENTATION OF THE ALO ALGORITHM FOR OELD PROBLEMS
4.1. Variables of each individual of the algorithm
The position of each Antlion includes all control variables and is initialized within limits as the fol-
lowing model:
Xd = CV min
+ rand(CV max
− CV min
) ; d = 1, ..., Npop (21)
where CV min
= {P1,min, ..., Pn−1,min} and CV max
= {P1,max, ..., Pn−1,max} (22)
As a result, the power output Pn,d is obtained by equation (23) following:
Pn,d = Pdemand + Ploss −
n−1
i=1
Pi,d (23)
Antlion optimization algorithm for optimal non-smooth... (Thanh Pham Van)
6. 1192 Ì ISSN: 2088-8708
Start
Initializing the population of Antlions by using (12)
Calculating fitness function for all Antlions
Determine the best Antlions and set CI = 1
Determine RWCI using (13)
Calculate restrict space for Ants using (15)
Determine lower and uper limits for position of Ants using (17)
Find new position of Ants using (18) and (19)
Evaluate such new position by calculate fitness function
Compare Antlion and new Ant to update new Antlion by using (20)
Determine the best Antlion
CI = Gmax CI = CI + 1
no
Stop
yes
Figure 1. The flowchart of ALO algorithm
4.2. Punishment of dependent variable violations
In process of optimization, as Pn,d is outside upper and lower bounds, it is penalized and
determined by:
CostP un =
Pn,d − Pn,max if Pn,d > Pn,max
Pn,min − Pn,d if Pn,d < Pn,min
0 if Pn,min ≤ Pn,d ≤ Pn,max
(24)
4.3. Compatible function
The compatible function is added to the product of the square of punishment value and a punishment
factor (Fa), as following equation:
CFd =
n
i=1
Fi(Pi) + Fa(CostP un)2
(25)
5. NUMERICAL RESULTS
The efficiency proposed method is judged in this section. The ALO algorithm has been tested on
the 10-unit system with constraints of the power network and the generators, and different fuel consuming
characteristics of thermal units. The detail is as follows:
(a) Case 1: 10-unit power system using multi fuel sources and without valve point loading effects.
(b) Case 2: 10-unit power system using multi fuel sources and valve point loading effects.
Int J Elec & Comp Eng, Vol. 10, No. 2, April 2020 : 1187 – 1199
7. Int J Elec & Comp Eng ISSN: 2088-8708 Ì 1193
(c) Case 3: 10-unit power system using multi fuel sources, valve point loading effects and complicated
constraints such as spinning reserve, prohibited operating zones and ramp rate limits.
Data which are used for the three cases is taken from [42]. In addition, The ALO algorithm has been
coded in Matlab platform and personal computer with processor 2.0 GHz and Ram of 2.0 Gb.
5.1. Investigating impact of control parameters on obtained results
Three cases above of the OELD problem have been executed by the ALO algorithm to investigate
the impact of different values of control parameters in the effectiveness, robustness, and stability of the search
process of ALO. Parameters have been used in the investigation are population size (Npop) and the number of
iterations (Gmax).
5.1.1. Case 1: 10-unit power system using multi fuel sources and without valve point loading effects
There are four studied subcases with four load cases from 2,400 to 2,700 MW with a change of 100
MW. The obtained results with respect to the minimum fuel cost for 100 trial runs with different cases of Npop
and Gmax have been reported in Table 1. Experimentation has been divided into two parts. In the first part,
the population size has been kept at Npop = 10 and the number of iterations has been changed as the right first
column of the Table 1. While the population size has been kept at Npop = 20 and the number of iterations as the
Table 1 in the second part. Observing the table can see that the same value of Npop, when Gmax rises, there is
a corresponding decline in the minimum fuel cost. When the minimum fuel cost equals 481.7226 $/h in both
of two parts then it is impossible to decrease although Gmax still increases. In the first part at the first six rows
of Table 1, the best costs of subcase 1.1, subcase 1.3 and subcase 1.4 are respectively 481.7426 $/h, 574.3808
$/h, 623.8092 $/h corresponding with Npop = 10, Gmax = 250. Particularly, subcase 1.2 get the best cost at
Npop = 10 and Gmax = 200 with value of 526.2388 $/h. In the second part at the last four rows of Table 1 all
four subcases reach the best cost at Gmax = 150. This point out that when the population size is set to higher
value, the number of iterations can be set to lower value but the best optimal solution can be found.
Table 1. The lowest cost ($/h) obtained from 100 runs for different values of Npop and Gmax
Load of Load of Load of Load of Npop Gmax
2,400 MW 2,500 MW 2,600 MW 2,700 MW
491.2418 533.6331 579.9704 632.5862 10 50
483.0403 526.5162 574.5738 624.9104 10 100
481.7637 526.2820 574.3815 623.8695 10 150
481.7424 526.2388 574.3852 623.8103 10 200
481.7226 526.2388 574.3808 623.8093 10 250
481.7226 526.2388 574.3808 623.8092 10 300
483.5568 526.8289 576.4240 629.4534 20 50
481.7424 526.2494 574.5813 623.8402 20 100
481.7226 526.2388 574.3808 623.8092 20 150
481.7226 526.2388 574.3808 623.8092 20 200
The results of the distribution of the fuel costs for subcase 1.4 over 100 trials are shown in
Figure 2. The results of the tests show that ALO can find the best optimal solution for different setting
of control parameters and the deviation among obtained minimums is very small. Thus, ALO is stable and
effective for the first case with four different loads.
5.1.2. Case 2: 10-unit power system using multi fuel sources and valve effects
The second study case only considers the load demand of 2,700 MW. Meantime, Npop has been kept
at the value of 20 but Gmax has been adjusted within 8 values from 50 to 400 with a small change of 50.
Numbers yielded from the test including minimum cost, average cost, maximum cost are presented in Table 2.
As shown in Table 2, once Gmax decrease, the fuel cost function will decrease in the first five rows. However,
row 7 indicates the fuel cost function increases unintentionally although Gmax increases equaling 300. This is
also repeated one more time at the last row. The overview on Table 2 point out that the best cost of 623.8709
$/h is obtained at Gmax = 350. Meanwhile, the minimum cost at Gmax = 400 is higher than 623.8709 $/h.
Clearly, this phenomenon has been caused by the impact of valve point loading effects on the stability of the
ALO search process. The most of the fuel costs for case 2 over 100 trials have distributed nearby the minimum
Antlion optimization algorithm for optimal non-smooth... (Thanh Pham Van)
8. 1194 Ì ISSN: 2088-8708
value as shown in Figure 2. This shows that ALO has good stability for solving the OELD problems on the
10-unit system with multi fuel sources and valve point loading effects.
Table 2. Result obtained by ALO for case 2 with different values of control variables
Lowest cost ($/h) Average cost ($/h) Highest cost ($/h) Npop Gmax
629.9271 653.2830 721.8110 20 50
624.1303 638.9358 690.4222 20 100
624.0333 631.6475 653.1102 20 150
623.9216 628.4258 643.9007 20 200
623.8958 625.8331 643.8601 20 250
623.9309 626.1910 644.3901 20 300
623.8709 625.6935 636.3510 20 350
623.8878 625.2053 635.7175 20 400
5.1.3. Case 3: 10-unit power system using multi fuel sources, valve effects and complicated constraints
In the third studied case, input parameters and obtained results are presented in Table 3. As observed
from Table 3, the minimum fuel costs obtained by ALO can drop significantly from Gmax = 50 to Gmax = 400
and it reaches the best minimum at Gmax = 400 with the cost of 624.3894 $/h. However, the minimum cannot
be reduced since setting Gmax to 450 and 500, corresponding to the cost of 624.3976 $/h and 624.4035 $/h.
Clearly, the phenomenon is similar to that in case 2 but totally different from 4 subcases in case 1. Obviously,
valve point loading effects and complicated constraints have a highly significant impact on the stability of ALO.
Figure 2 is shown the fuel costs after 100 trials. They are wavered between two numbers 625 $/h and 630 $/h.
Table 3. Result obtained by ALO for case 3 with different values of control variables
Lowest cost ($/h) Average cost ($/h) Highest cost ($/h) Npop Gmax
625.0314 634.9521 658.0532 30 50
624.6915 628.9171 639.8262 30 100
624.5606 627.2422 637.3534 30 150
624.4409 627.0395 634.9398 30 200
624.4920 626.2745 632.8379 30 250
624.4626 626.3989 630.5901 30 300
624.4287 626.2675 630.5357 30 350
624.3894 625.9337 630.5276 30 400
624.3976 625.7483 630.2841 30 450
624.4035 625.6773 629.0156 30 500
Figure 2. The best cost of 100 trials from sub-case 1.4, case 2 and case 3
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9. Int J Elec & Comp Eng ISSN: 2088-8708 Ì 1195
5.2. Result comparison obtained by ALO and other methods
In this section comparison of results obtained by the ALO method and other onces has been performed.
In order to investigate the real performance of ALO method, an important factor must be concerned to be a
number of fitness function evaluations Nfe, which is calculated by:
Nfe = ω ∗ Npop ∗ Gmax (26)
The ALO method has only one new solution generation for each iteration, so ω is 1 for ALO. For
other methods such as FA and IFA in [38], Nfe has another model, that is:
Nfe =
Npop ∗ (Npop + 1) ∗ Gmax
2
(27)
5.2.1. Comparisons of Test Case 1
This section compares fuel cost obtained by ALO and different methods from four subcases of case
1. The best cost together with Nfe are reported in Table 4. Numbers from Table 4 point out that the proposed
method can harvest optimal solutions as good as others. While ALHN [6] and EALHN [7] are not metaheuristic
methods. These methods will face to the difficulties of applying for OELD problem with non-convex fuel cost
function and complex constraints. ALO has an approximate minimum to most methods and has better cost than
FA [38] for all subcases and CFA [31] for subcase 1.4. However, as comparing Nfe values, ALO has taken
Nfe = 2,500 for seeking and reaching the best optimal solutions while other methods have employed high value
of Nfe, from 5,500 to 156,000. Namely, Nfe was respectively set to 12,000, 30,000 and 156,000 for DEA,
HRCGA, and CFA. In summary, ALO has found approximate or better results than compared methods but it
has owned very fast convergence speed compared to other ones. In summary, the applied ALO method is really
effective for case 1 with discrete objective function.
Table 4. The lowest cost ($/h) of case 1 and compared methods
Load
(MW)
ALHN
[6]
EALHN
[7]
DEA
[8]
HICDE-
DP
[10]
HRCGA
[15]
CSA
[23]
AIS
[29]
CFA
[31]
FA
[38]
IFA
[38]
ALO
2,400 481.723 481.723 481.723 481.7226 481.7226 - 481.723 - 505.2337 481.7226 481.7226
2,500 526.239 526.239 526.239 526.2388 526.2388 - 526.24 - 580.4417 526.2388 526.2388
2,600 574.381 574.381 574.381 574.3808 574.3808 - 574.381 - 639.287 574.3808 574.3808
2,700 623.809 623.809 623.809 623.809 623.8092 623.8092 623.8092 623.8339 679.9525 623.809 623.8093
Nfe - - 12,000 8,000 30,000 6,000 4,000 156,000 11,000 5,500 2,500
5.2.2. Comparisons of Test Case 2
The section is carrying our comparison of results from the applied ALO and other methods for case 2.
According to reported data in Table 5, KHA [27] is the best one; however, checking optimal solution reported
in [27] sees that incorrect type of fuel was reported and fuel cost is much higher than reported values. Thus,
KHA [27] is not a competitor of the applied ALO method. When compared to accepted methods like GA,
TSA, PSO [20], FA and IFA [38], the proposed method is better with respect to the best cost. On the contrary,
the ALO method is less effective than remaining methods like in DSPSO-TSA [20], CSA [23], ICSA [25].
However, it should be noted that CSA and ICSA have used Nfe equaling 10,000 and 12,000 while that of the
applied ALO method was 7,000. Compared to GA, TSA, and PSO in [20], ALO has better cost but higher Nfe
since those from ALO are 623.8708 and 7,000 while those from these methods are higher than 624.3045 and
3,000. However, results of ALO reported in Table 2 sees that ALO found the best cost of 624.1303 at Npop =
20 and Gmax = 100, corresponding to Nfe = 2,000. Thus, ALO could find better optimal solutions with faster
convergence than GA, TSA, and PSO in [20]. In summary, ALO has yielded better results but its search speed
has been faster than these methods while other ones with better results than ALO were slower for converging
to the best optimal solution. In other words, ALO is a promising method for case 2 considering with 10 units
taking multi fuel sources and valve point loading effects into account.
Antlion optimization algorithm for optimal non-smooth... (Thanh Pham Van)
10. 1196 Ì ISSN: 2088-8708
Table 5. The comparison of results for case 2
Cost
($/h)
CCDE
[12]
GA
[20]
TSA
[20]
PSO
[20]
DSPSO-
TSA [20]
CSA
[23]
ICSA
[25]
KHA
[27]
FA
[38]
IFA
[38]
ALO
Lowest 623.8288 624.505 624.3078 624.3045 623.8375 623.8684 623.8684 605.7582 664.5306 623.8768 623.8708
Average 623.8574 624.7419 635.0623 624.5054 623.8625 623.9495 623.9495 605.8043 675.5344 625.2704 625.6935
Highest 623.8904 624.8169 624.8285 625.9252 623.9001 626.3666 626.3666 605.9426 679.426 629.2765 636.3510
Nfe 7,000 3,000 3,000 3,000 3,000 10,000 12,000 10,000 11,000 5,500 7,000
5.2.3. Comparisons of Test Case 3
The section presents the comparison of fuel cost and Nfe from the applied ALO and other methods for
case 3. The results obtained for case 3 are given in Table 6. According to the results, the ALO method is only
less effective than IRCGA [17] while it is better than all other methods; however, the applied ALO method has
the most effective convergence speed since it has used Nfe = 12,000 but that from other was much higher. For
instance, the value is 33,000 for IFA [38], 66,000 for FA [38] and 90,000 for both RCGA and IRCGA. Clearly,
ALO can be faster than these methods approximately from 3 times to 8 times. In summary, ALO has found a
better optimal solution than three methods but less effective optimal solution than one method. However ALO
is faster than all compared methods from 3 times to 8 times. Thus, ALO is a very effective method for the case,
which has multi fuel sources, valve point loading effects and many complex constraints.
The optimal solution obtained by the ALO algorithm for all cases have been presented in Appendix.
Table 6. The comparison of results for case 3 (2,700 MW)
Cost
($/h)
RCGA
[17]
IRCGA
[17]
FA
[38]
IFA
[38]
ALO
Lowest 624.6605 624.355 673.5544 624.4950 624.3894
Average 625.9201 624.5792 685.2872 625.2647 625.6773
Highest 628.9253 624.7541 699.2855 629.3951 629.0156
Nfe 90,000 90,000 66,000 33,000 12,000
In this section, it is explained the results of research and at the same time is given the comprehensive
discussion. Results can be presented in figures, graphs, tables and others that make the reader understand
easily [2, 5]. The discussion can be made in several sub-chapters.
6. CONCLUSION
In this article, the proposed ALO method is effectually implemented to handle the OELD problem.
The studied system has 10 TGUs with different types of fuel consuming characteristic and almost complex
operation constraints of the power grid practiced in three tested cases. The method has been proved to be stable,
effective and robust. The obtained results have been compared with many other methods. The comparison can
imply that the ALO method is better than most other methods in term of lower fuel cost and smaller number of
fitness evaluations. However, ALO has not found all better results than all methods for study cases, especially
in comparison with improved versions of original meta-heuristic algorithms. Thus, it can conclude that ALO
method can be selected as an optimization tool for dealing with OELD problem but it needs more improvements
for enhancing optimal solution quality and converge speed.
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Appendix
Table 7. Optimal solutions found by ALO study cases
Power output Case 1 Case 2 Case 3
MW 2,400 MW 2,500 MW 2,600 MW 2,700 MW 2,700 MW 2,700 MW
P1 189.7408 206.5197 216.5486 218.2607 219.1333 218.9026
P2 202.3362 206.4566 210.9095 211.7224 212.4026 213.4446
P3 253.8923 265.7344 278.5384 280.7647 279.6938 284.6929
P4 233.0488 235.9536 239.0974 239.6537 239.9551 240.1769
P5 241.8215 258.0099 275.4831 278.3093 279.1825 259.9421
P6 233.0420 235.9511 239.1002 239.6635 238.9891 241.1175
P7 253.2723 268.8587 285.7104 288.7401 292.4503 294.7045
P8 233.0428 235.9521 239.1087 239.5935 239.0145 240.8488
P9 320.3769 331.4902 343.5002 428.3186 426.7170 436.3226
P10 239.4264 255.0738 272.0035 274.9736 272.4617 269.8475
Cost ($/h) 481.7226 526.2388 574.3808 623.8093 623.8709 624.3894
BIOGRAPHIES OF AUTHORS
Thanh Pham received his B.Eng degrees in Electrical & Electronics Engineering and M.Eng degrees
in Automatic Control Engineering from Ho Chi Minh City University of Technology, National Ho
Chi Minh University, Viet Nam in 1997 and 2004 respectively. Now he is a lecturer at faculty of
electrical engineering, Cao Thang Technical College, VN. He is also a doctoral student at Technical
University of Ostrava, Czech Republic. His research interests include optimization algorithms, power
system operation, and Renewable Energy.
Vaclav Snasel studied numerical mathematics at Palacky University in Olomouc, and Ph.D. degree
obtained at Masaryk University in Brno. Currently, he is a rector of VSB-Technical University of
Ostrava. His research interests include neural network, nature and biologically inspired computing,
data mining, and applied to various real-world problems.
Thang Nguyen finished his PhD degree in 2018 at Ho Chi Minh City University of Technology
and Education, Viet Nam. Currently, he is working at Power system optimization research group of
faculty of electrical and electronics engineering. His research is about applications of metaheuristics
for optimization problems in electrical engineering.
Antlion optimization algorithm for optimal non-smooth... (Thanh Pham Van)