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.
Economic/Emission Load Dispatch Using Artificial Bee Colony AlgorithmIDES Editor
This paper presents an application of the
artificial bee colony (ABC) algorithm to multi-objective
optimization problems in power system. A new multiobjective
artificial bee colony (MOABC) algorithm to
solve the economic/ emission dispatch (EED) problem is
proposed in this paper. Non-dominated sorting is
employed to obtain a Pareto optimal set. Moreover, fuzzy
decision theory is employed to extract the best
compromise solution. A numerical result for IEEE 30-bus
test system is presented to demonstrate the capability of
the proposed approach to generate well-distributed
Pareto-optimal solutions of EED problem in one single
run. In addition, the EED problem is also solved using the
weighted sum method using ABC. Results obtained with
the proposed approach are compared with other
techniques available in the literature. Results obtained
show that the proposed MOABC has a great potential in
handling multi-objective optimization problem.
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.
Dynamic Economic Dispatch Assessment Using Particle Swarm Optimization TechniquejournalBEEI
This paper presents the application of particle swarm optimization (PSO) technique for solving the dynamic economic dispatch (DED) problem. The DED is one of the main functions in power system planning in order to obtain optimum power system operation and control. It determines the optimal operation of generating units at every predicted load demands over a certain period of time. The optimum operation of generating units is obtained by referring to the minimum total generation cost while the system is operating within its limits. The DED based PSO technique is tested on a 9-bus system containing of three generator bus, six load bus and twelve transmission lines.
Security constrained optimal load dispatch using hpso technique for thermal s...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Enriched Firefly Algorithm for Solving Reactive Power Problemijeei-iaes
In this paper, Enriched Firefly Algorithm (EFA) is planned to solve optimal reactive power dispatch problem. This algorithm is a kind of swarm intelligence algorithm based on the response of a firefly to the light of other fireflies. In this paper, we plan an augmentation on the original firefly algorithm. The proposed algorithm extends the single population FA to the interacting multi-swarms by cooperative Models. The proposed EFA has been tested on standard IEEE 30 bus test system and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.
Economic/Emission Load Dispatch Using Artificial Bee Colony AlgorithmIDES Editor
This paper presents an application of the
artificial bee colony (ABC) algorithm to multi-objective
optimization problems in power system. A new multiobjective
artificial bee colony (MOABC) algorithm to
solve the economic/ emission dispatch (EED) problem is
proposed in this paper. Non-dominated sorting is
employed to obtain a Pareto optimal set. Moreover, fuzzy
decision theory is employed to extract the best
compromise solution. A numerical result for IEEE 30-bus
test system is presented to demonstrate the capability of
the proposed approach to generate well-distributed
Pareto-optimal solutions of EED problem in one single
run. In addition, the EED problem is also solved using the
weighted sum method using ABC. Results obtained with
the proposed approach are compared with other
techniques available in the literature. Results obtained
show that the proposed MOABC has a great potential in
handling multi-objective optimization problem.
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.
Dynamic Economic Dispatch Assessment Using Particle Swarm Optimization TechniquejournalBEEI
This paper presents the application of particle swarm optimization (PSO) technique for solving the dynamic economic dispatch (DED) problem. The DED is one of the main functions in power system planning in order to obtain optimum power system operation and control. It determines the optimal operation of generating units at every predicted load demands over a certain period of time. The optimum operation of generating units is obtained by referring to the minimum total generation cost while the system is operating within its limits. The DED based PSO technique is tested on a 9-bus system containing of three generator bus, six load bus and twelve transmission lines.
Security constrained optimal load dispatch using hpso technique for thermal s...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Enriched Firefly Algorithm for Solving Reactive Power Problemijeei-iaes
In this paper, Enriched Firefly Algorithm (EFA) is planned to solve optimal reactive power dispatch problem. This algorithm is a kind of swarm intelligence algorithm based on the response of a firefly to the light of other fireflies. In this paper, we plan an augmentation on the original firefly algorithm. The proposed algorithm extends the single population FA to the interacting multi-swarms by cooperative Models. The proposed EFA has been tested on standard IEEE 30 bus test system and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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.
Comparisional Investigation of Load Dispatch Solutions with TLBO IJECEIAES
This paper discusses economic load dispatch Problem is modeled with nonconvex functions. These are problem are not solvable using a convex optimization techniques. So there is a need for using a heuristic method. Among such methods Teaching and Learning Based Optimization (TLBO) is a recently known algorithm and showed promising results. This paper utilized this algorithm to provide load dispatch solutions. Comparisons of this solution with other standard algorithms like Particle Swarm Optimization (PSO), Differential Evolution (DE) and Harmony Search Algorithm (HSA). This proposed algorithm is applied to solve the load dispatch problem for 6 unit and 10 unit test systems along with the other algorithms. This comparisional investigation explored various merits of TLBO with respect to PSO, DE, and HAS in the field economic load dispatch.
Reduction of Active Power Loss byUsing Adaptive Cat Swarm Optimizationijeei-iaes
This paper presents, an Adaptive Cat Swarm Optimization (ACSO) for solving reactive power dispatch problem. Cat Swarm Optimization (CSO) is one of the new-fangled swarm intelligence algorithms for finding the most excellent global solution. Because of complication, sometimes conventional CSO takes a lengthy time to converge and cannot attain the precise solution. For solving reactive power dispatch problem and to improve the convergence accuracy level, we propose a new adaptive CSO namely ‘Adaptive Cat Swarm Optimization’ (ACSO). First, we take account of a new-fangled adaptive inertia weight to velocity equation and then employ an adaptive acceleration coefficient. Second, by utilizing the information of two previous or next dimensions and applying a new-fangled factor, we attain to a new position update equation composing the average of position and velocity information. The projected ACSO has been tested on standard IEEE 57 bus test system and simulation results shows clearly about the high-quality performance of the planned algorithm in tumbling the real power loss.
Bi-objective Optimization Apply to Environment a land Economic Dispatch Probl...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Atmosphere Clouds Model Algorithm for Solving Optimal Reactive Power Dispatch...ijeei-iaes
In this paper, a new method, called Atmosphere Clouds Model (ACM) algorithm, used for solving optimal reactive power dispatch problem. ACM stochastic optimization algorithm stimulated from the behavior of cloud in the natural earth. ACM replicate the generation behavior, shift behavior and extend behavior of cloud. The projected (ACM) algorithm has been tested on standard IEEE 30 bus test system and simulation results shows clearly about the superior performance of the proposed algorithm in plummeting the real power loss.
Multi Area Economic Dispatch Using Secant Method and Tie Line MatrixIJAPEJOURNAL
In this paper, Secant method and tie line matrix are proposed to solve multi area economic dispatch (MAED) problem with tie line loss. Generator limits of all generators in each area are calculated at given area power demands plus export (or import) using secant method and the generator limits of all generators are modified as modified generator limits. Central economic dispatch (CED) problem is used to determine the output powers of all generators and finally power flows in all tie lines are determined from tie line matrix. Here, Secant method is applied to solve the CED problem. A modified tie line matrix is used to find power flow in each tie line and then tie line loss is calculated from the power flow in each tie line. The proposed approach has been tested on two-area (two generators in each area) system and four-area (four generators in each area) system. It is observed from various cases that the proposed approach provides optimally best solution in terms of cost with tie line loss with less computational burden.
A Minimum Spanning Tree Approach of Solving a Transportation Probleminventionjournals
: This work centered on the transportation problem in the shipment of cable troughs for an underground cable installation from three supply ends to four locations at a construction site where they are needed; in which case, we sought to minimize the cost of shipment. The problem was modeled into a bipartite network representation and solved using the Kruskal method of minimum spanning tree; after which the solution was confirmed with TORA Optimization software version 2.00. The result showed that the cost obtained in shipping the cable troughs under the application of the method, which was AED 2,022,000 (in the United Arab Emirate Dollar), was more effective than that obtained from mere heuristics when compared.
5. 9375 11036-1-sm-1 20 apr 18 mar 16oct2017 ed iqbal qcIAESIJEECS
In this paper, Tailored Flower Pollination (TFP) algorithm is proposed to solve the optimal reactive power problem. Comprising of the elements of chaos theory, Shuffled frog leaping search and Levy Flight, the performance of the flower pollination algorithm has been improved. Proposed TFP algorithm has been tested in standard IEEE 118 & practical 191 bus test systems and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.
Non-convex constrained economic power dispatch with prohibited operating zone...IJECEIAES
This paper is focused on the solution of the non-convex economic power dispatch problem with piecewise quadratic cost functions and practical operation constraints of generation units. The constraints of the economic dispatch problem are power balance constraint, generation limits constraint, prohibited operating zones and transmission power losses. To solve this problem, a meta-heuristic optimization algorithm named crow search algorithm is proposed. A constraint handling technique is also implemented to satisfy the constraints effectively. For the verification of the effectiveness and the superiority of the proposed algorithm, it is tested on 6-unit, 10-unit and 15-unit test systems. The simulation results and statistical analysis show the efficiency of the proposed algorithm. Also, the results confirm the superiority and the high-quality solutions of the proposed algorithm when compared to the other reported algorithms.
Combination of Immune Genetic Particle Swarm Optimization algorithm with BP a...paperpublications3
Abstract:In this paper, merging Immune Genetic Particle Swarm Optimization algorithm (IGPSO) with BP algorithm to optimize BP Neural Network parameter i.e., BPIGPSO amalgamation to solve optimal reactive power dispatch algorithm. The basic perception is that first training BP neural network with IGPSO to find out a comparatively optimal solution, then take the network parameter at this time as the preliminary parameter of BP algorithm to carry out the training, finally searching the optimal solution. The proposed BPIGPSO has been tested on standard IEEE 57 bus test system and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.
Keywords:BP neural network, Immune Genetic Particle Swarm Optimization algorithm, Optimal Reactive Power, Transmission loss.
Economic Load Dispatch (ELD) is a process of scheduling the required load demand among available generation units such that the fuel cost of operation is minimized. The ELD problem is formulated as a non-linear constrained optimization problem with both equality and inequality constraints. In this paper, two test systems of the ELD problems are solved by adopting the Cuckoo Search (CS) Algorithm. A comparison of obtained simulation results by using the CS is carried out against six other swarm intelligence algorithms: Particle Swarm Optimization, Shuffled Frog Leaping Algorithm, Bacterial Foraging Optimization, Artificial Bee Colony, Harmony Search and Firefly Algorithm. The effectiveness of each swarm intelligence algorithm is demonstrated on a test system comprising three-generators and other containing six-generators. Results denote superiority of the Cuckoo Search Algorithm and confirm its potential to solve the ELD problem.
Multi Objective Directed Bee Colony Optimization for Economic Load Dispatch W...IJECEIAES
Earlier economic emission dispatch methods for optimizing emission level comprising carbon monoxide, nitrous oxide and sulpher dioxide in thermal generation, made use of soft computing techniques like fuzzy,neural network,evolutionary programming,differential evolution and particle swarm optimization etc..The above methods incurred comparatively more transmission loss.So looking into the nonlinear load behavior of unbalanced systems following differential load pattern prevalent in tropical countries like India,Pakistan and Bangladesh etc.,the erratic variation of enhanced power demand is of immense importance which is included in this paper vide multi objective directed bee colony optimization with enhanced power demand to optimize transmission losses to a desired level.In the current dissertation making use of multi objective directed bee colony optimization with enhanced power demand technique the emission level versus cost of generation has been displayed vide figure-3 & figure-4 and this result has been compared with other dispatch methods using valve point loading(VPL) and multi objective directed bee colony optimization with & without transmission loss.
A new design reuse approach for voip implementation into fpsocs and asicsijsc
The aim of this paper is to present a new design reuse approach for automatic generation of Voice over Internet protocol (VOIP) hardware description and implementation into FPSOCs and ASICs. Our motivation behind this work is justified by the following arguments: first, VOIP based System on chip (SOC) implementation is an emerging research and development area, where innovative applications can be implemented. Second, these systems are very complex and due to time to market pressure, there is a need to built platforms that help the designer to explore with different architectural possibilities and choose the circuit that best correspond to the specifications. Third, we aim to develop in hardware, design, methods and tools that are used in software like the MATLAB tool for VOIP implementation. To achieve our goal, the proposed design approach is based on a modular design of the VOIP architecture. The originality of our approach is the application of the design for reuse (DFR) and the design with reuse (DWR) concepts. To validate the approach, a case study of a SOC based on the OR1K processor is studied. We demonstrate that the proposed SoC architecture is reconfigurable, scalable and the final RTL code can be reused for any FPSOC or ASIC technology. As an example, Performances measures, in the VIRTEX-5 FPGA device family, and ASIC 65nm technology are shown through this paper.
METAHEURISTIC OPTIMIZATION ALGORITHM FOR THE SYNCHRONIZATION OF CHAOTIC MOBIL...ijsc
We provide a scheme for the synchronization of two chaotic mobile robots when a mismatch between the
parameter values of the systems to be synchronized is present. We have shown how meta-heuristic
optimization can be used to adapt the parameters in two coupled systems such that the two systems are
synchronized, although their behavior is chaotic and they have started with different initial conditions and
parameter settings. The controlled system synchronizes its dynamics with the control signal in the periodic
as well as chaotic regimes. The method can be seen also as another way of controlling the chaotic
behavior of a coupled system. In the case of coupled chaotic systems, under the interaction between them,
their chaotic dynamics can be cooperatively self-organized. A synergistic approach to meta-heuristic
optimization search algorithm is developed. To avoid being trapped into local optimum and to enrich the
searching behavior, chaotic dynamics is incorporated into the proposed search algorithm. A chaotic Levy
flight is firstly incorporated in the proposed search algorithm for efficiently generating new solutions. And
secondly, chaotic sequence and a psychology factor of emotion are introduced for move acceptance in the
search algorithm. We illustrate the application of the algorithm by estimating the complete parameter
vector of a chaotic mobile robot.
Integrating vague association mining with markov modelijsc
The increasing demand of World Wide Web raises the need of predicting the user’s web page request. The
most widely used approach to predict the web pages is the pattern discovery process of Web usage mining.
This process involves inevitability of many techniques like Markov model, association rules and clustering.
Fuzzy theory with different techniques has been introduced for the better results. Our focus is on Markov
models. This paper is introducing the vague Rules with Markov models for more accuracy using the vague
set theory.
EXPLORING SOUND SIGNATURE FOR VEHICLE DETECTION AND CLASSIFICATION USING ANNijsc
This paper attempts to explore the possibility of using sound signatures for vehicle detection and
classification purposes. Sound emitted by vehicles are captured for a two lane undivided road carrying
moderate traffic. Simultaneous arrival of different types vehicles, overtaking at the study location, sound of
horns, random but identifiable back ground noises, continuous high energy noises on the back ground are
the different challenges encountered in the data collection. Different features were explored out of which
smoothed log energy was found to be useful for automatic vehicle detection by locating peaks. Melfrequency
ceptral coefficients extracted from fixed regions around the detected peaks along with the
manual vehicle labels are utilised to train an Artificial Neural Network (ANN). The classifier for four
broad classes heavy, medium, light and horns was trained. The ANN classifier developed was able to
predict categories well.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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.
Comparisional Investigation of Load Dispatch Solutions with TLBO IJECEIAES
This paper discusses economic load dispatch Problem is modeled with nonconvex functions. These are problem are not solvable using a convex optimization techniques. So there is a need for using a heuristic method. Among such methods Teaching and Learning Based Optimization (TLBO) is a recently known algorithm and showed promising results. This paper utilized this algorithm to provide load dispatch solutions. Comparisons of this solution with other standard algorithms like Particle Swarm Optimization (PSO), Differential Evolution (DE) and Harmony Search Algorithm (HSA). This proposed algorithm is applied to solve the load dispatch problem for 6 unit and 10 unit test systems along with the other algorithms. This comparisional investigation explored various merits of TLBO with respect to PSO, DE, and HAS in the field economic load dispatch.
Reduction of Active Power Loss byUsing Adaptive Cat Swarm Optimizationijeei-iaes
This paper presents, an Adaptive Cat Swarm Optimization (ACSO) for solving reactive power dispatch problem. Cat Swarm Optimization (CSO) is one of the new-fangled swarm intelligence algorithms for finding the most excellent global solution. Because of complication, sometimes conventional CSO takes a lengthy time to converge and cannot attain the precise solution. For solving reactive power dispatch problem and to improve the convergence accuracy level, we propose a new adaptive CSO namely ‘Adaptive Cat Swarm Optimization’ (ACSO). First, we take account of a new-fangled adaptive inertia weight to velocity equation and then employ an adaptive acceleration coefficient. Second, by utilizing the information of two previous or next dimensions and applying a new-fangled factor, we attain to a new position update equation composing the average of position and velocity information. The projected ACSO has been tested on standard IEEE 57 bus test system and simulation results shows clearly about the high-quality performance of the planned algorithm in tumbling the real power loss.
Bi-objective Optimization Apply to Environment a land Economic Dispatch Probl...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Atmosphere Clouds Model Algorithm for Solving Optimal Reactive Power Dispatch...ijeei-iaes
In this paper, a new method, called Atmosphere Clouds Model (ACM) algorithm, used for solving optimal reactive power dispatch problem. ACM stochastic optimization algorithm stimulated from the behavior of cloud in the natural earth. ACM replicate the generation behavior, shift behavior and extend behavior of cloud. The projected (ACM) algorithm has been tested on standard IEEE 30 bus test system and simulation results shows clearly about the superior performance of the proposed algorithm in plummeting the real power loss.
Multi Area Economic Dispatch Using Secant Method and Tie Line MatrixIJAPEJOURNAL
In this paper, Secant method and tie line matrix are proposed to solve multi area economic dispatch (MAED) problem with tie line loss. Generator limits of all generators in each area are calculated at given area power demands plus export (or import) using secant method and the generator limits of all generators are modified as modified generator limits. Central economic dispatch (CED) problem is used to determine the output powers of all generators and finally power flows in all tie lines are determined from tie line matrix. Here, Secant method is applied to solve the CED problem. A modified tie line matrix is used to find power flow in each tie line and then tie line loss is calculated from the power flow in each tie line. The proposed approach has been tested on two-area (two generators in each area) system and four-area (four generators in each area) system. It is observed from various cases that the proposed approach provides optimally best solution in terms of cost with tie line loss with less computational burden.
A Minimum Spanning Tree Approach of Solving a Transportation Probleminventionjournals
: This work centered on the transportation problem in the shipment of cable troughs for an underground cable installation from three supply ends to four locations at a construction site where they are needed; in which case, we sought to minimize the cost of shipment. The problem was modeled into a bipartite network representation and solved using the Kruskal method of minimum spanning tree; after which the solution was confirmed with TORA Optimization software version 2.00. The result showed that the cost obtained in shipping the cable troughs under the application of the method, which was AED 2,022,000 (in the United Arab Emirate Dollar), was more effective than that obtained from mere heuristics when compared.
5. 9375 11036-1-sm-1 20 apr 18 mar 16oct2017 ed iqbal qcIAESIJEECS
In this paper, Tailored Flower Pollination (TFP) algorithm is proposed to solve the optimal reactive power problem. Comprising of the elements of chaos theory, Shuffled frog leaping search and Levy Flight, the performance of the flower pollination algorithm has been improved. Proposed TFP algorithm has been tested in standard IEEE 118 & practical 191 bus test systems and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.
Non-convex constrained economic power dispatch with prohibited operating zone...IJECEIAES
This paper is focused on the solution of the non-convex economic power dispatch problem with piecewise quadratic cost functions and practical operation constraints of generation units. The constraints of the economic dispatch problem are power balance constraint, generation limits constraint, prohibited operating zones and transmission power losses. To solve this problem, a meta-heuristic optimization algorithm named crow search algorithm is proposed. A constraint handling technique is also implemented to satisfy the constraints effectively. For the verification of the effectiveness and the superiority of the proposed algorithm, it is tested on 6-unit, 10-unit and 15-unit test systems. The simulation results and statistical analysis show the efficiency of the proposed algorithm. Also, the results confirm the superiority and the high-quality solutions of the proposed algorithm when compared to the other reported algorithms.
Combination of Immune Genetic Particle Swarm Optimization algorithm with BP a...paperpublications3
Abstract:In this paper, merging Immune Genetic Particle Swarm Optimization algorithm (IGPSO) with BP algorithm to optimize BP Neural Network parameter i.e., BPIGPSO amalgamation to solve optimal reactive power dispatch algorithm. The basic perception is that first training BP neural network with IGPSO to find out a comparatively optimal solution, then take the network parameter at this time as the preliminary parameter of BP algorithm to carry out the training, finally searching the optimal solution. The proposed BPIGPSO has been tested on standard IEEE 57 bus test system and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.
Keywords:BP neural network, Immune Genetic Particle Swarm Optimization algorithm, Optimal Reactive Power, Transmission loss.
Economic Load Dispatch (ELD) is a process of scheduling the required load demand among available generation units such that the fuel cost of operation is minimized. The ELD problem is formulated as a non-linear constrained optimization problem with both equality and inequality constraints. In this paper, two test systems of the ELD problems are solved by adopting the Cuckoo Search (CS) Algorithm. A comparison of obtained simulation results by using the CS is carried out against six other swarm intelligence algorithms: Particle Swarm Optimization, Shuffled Frog Leaping Algorithm, Bacterial Foraging Optimization, Artificial Bee Colony, Harmony Search and Firefly Algorithm. The effectiveness of each swarm intelligence algorithm is demonstrated on a test system comprising three-generators and other containing six-generators. Results denote superiority of the Cuckoo Search Algorithm and confirm its potential to solve the ELD problem.
Multi Objective Directed Bee Colony Optimization for Economic Load Dispatch W...IJECEIAES
Earlier economic emission dispatch methods for optimizing emission level comprising carbon monoxide, nitrous oxide and sulpher dioxide in thermal generation, made use of soft computing techniques like fuzzy,neural network,evolutionary programming,differential evolution and particle swarm optimization etc..The above methods incurred comparatively more transmission loss.So looking into the nonlinear load behavior of unbalanced systems following differential load pattern prevalent in tropical countries like India,Pakistan and Bangladesh etc.,the erratic variation of enhanced power demand is of immense importance which is included in this paper vide multi objective directed bee colony optimization with enhanced power demand to optimize transmission losses to a desired level.In the current dissertation making use of multi objective directed bee colony optimization with enhanced power demand technique the emission level versus cost of generation has been displayed vide figure-3 & figure-4 and this result has been compared with other dispatch methods using valve point loading(VPL) and multi objective directed bee colony optimization with & without transmission loss.
A new design reuse approach for voip implementation into fpsocs and asicsijsc
The aim of this paper is to present a new design reuse approach for automatic generation of Voice over Internet protocol (VOIP) hardware description and implementation into FPSOCs and ASICs. Our motivation behind this work is justified by the following arguments: first, VOIP based System on chip (SOC) implementation is an emerging research and development area, where innovative applications can be implemented. Second, these systems are very complex and due to time to market pressure, there is a need to built platforms that help the designer to explore with different architectural possibilities and choose the circuit that best correspond to the specifications. Third, we aim to develop in hardware, design, methods and tools that are used in software like the MATLAB tool for VOIP implementation. To achieve our goal, the proposed design approach is based on a modular design of the VOIP architecture. The originality of our approach is the application of the design for reuse (DFR) and the design with reuse (DWR) concepts. To validate the approach, a case study of a SOC based on the OR1K processor is studied. We demonstrate that the proposed SoC architecture is reconfigurable, scalable and the final RTL code can be reused for any FPSOC or ASIC technology. As an example, Performances measures, in the VIRTEX-5 FPGA device family, and ASIC 65nm technology are shown through this paper.
METAHEURISTIC OPTIMIZATION ALGORITHM FOR THE SYNCHRONIZATION OF CHAOTIC MOBIL...ijsc
We provide a scheme for the synchronization of two chaotic mobile robots when a mismatch between the
parameter values of the systems to be synchronized is present. We have shown how meta-heuristic
optimization can be used to adapt the parameters in two coupled systems such that the two systems are
synchronized, although their behavior is chaotic and they have started with different initial conditions and
parameter settings. The controlled system synchronizes its dynamics with the control signal in the periodic
as well as chaotic regimes. The method can be seen also as another way of controlling the chaotic
behavior of a coupled system. In the case of coupled chaotic systems, under the interaction between them,
their chaotic dynamics can be cooperatively self-organized. A synergistic approach to meta-heuristic
optimization search algorithm is developed. To avoid being trapped into local optimum and to enrich the
searching behavior, chaotic dynamics is incorporated into the proposed search algorithm. A chaotic Levy
flight is firstly incorporated in the proposed search algorithm for efficiently generating new solutions. And
secondly, chaotic sequence and a psychology factor of emotion are introduced for move acceptance in the
search algorithm. We illustrate the application of the algorithm by estimating the complete parameter
vector of a chaotic mobile robot.
Integrating vague association mining with markov modelijsc
The increasing demand of World Wide Web raises the need of predicting the user’s web page request. The
most widely used approach to predict the web pages is the pattern discovery process of Web usage mining.
This process involves inevitability of many techniques like Markov model, association rules and clustering.
Fuzzy theory with different techniques has been introduced for the better results. Our focus is on Markov
models. This paper is introducing the vague Rules with Markov models for more accuracy using the vague
set theory.
EXPLORING SOUND SIGNATURE FOR VEHICLE DETECTION AND CLASSIFICATION USING ANNijsc
This paper attempts to explore the possibility of using sound signatures for vehicle detection and
classification purposes. Sound emitted by vehicles are captured for a two lane undivided road carrying
moderate traffic. Simultaneous arrival of different types vehicles, overtaking at the study location, sound of
horns, random but identifiable back ground noises, continuous high energy noises on the back ground are
the different challenges encountered in the data collection. Different features were explored out of which
smoothed log energy was found to be useful for automatic vehicle detection by locating peaks. Melfrequency
ceptral coefficients extracted from fixed regions around the detected peaks along with the
manual vehicle labels are utilised to train an Artificial Neural Network (ANN). The classifier for four
broad classes heavy, medium, light and horns was trained. The ANN classifier developed was able to
predict categories well.
In order to cope with real-world problems more effectively, we tend to design a decision support system for tuberculosis bacterium class identification. In this paper, we are concerned to propose a fuzzy diagnosability approach, which takes value between {0, 1} and based on observability of events, we
formalized the construction of diagnoses that are used to perform diagnosis. In particular, we present a
framework of the fuzzy expert system; discuss the suitability of artificial intelligence as a novel soft paradigm and reviews work from the literature for the development of a medical diagnostic system. The newly proposed approach allows us to deal with problems of diagnosability for both crisp and fuzzy value of input data. Accuracy analysis of designed decision support system based on demographic data was done
by comparing expert knowledge and system generated response. This basic emblematic approach using
fuzzy inference system is presented that describes a technique to forecast the existence of bacterium and
provides support platform to pulmonary researchers in identifying the ailment effectively.
In this study the controller for three tank multi loop system is designed using coefficient Diagram
method. Coefficient Diagram Method is one of the polynomial methods in control design. The
controller designed by using CDM technique is based on the coefficients of the characteristics
polynomial of the closed loop system according to the convenient performance such as equivalent
time constant, stability indices and stability limit. Controller is designed for the three tank process
by using CDM Techniques; the simulation results show that the proposed control strategies have
good set point tracking and better response capability.
Soft computing is likely to play aprogressively important role in many applications including image enhancement. The paradigm for soft computing is the human mind. The soft computing critique has been particularly strong with fuzzy logic. The fuzzy logic is facts representationas a
rule for management of uncertainty. Inthis paperthe Multi-Dimensional optimized problem is addressed by discussing the optimal thresholding usingfuzzyentropyfor Image enhancement. This technique is compared with bi-level and multi-level thresholding and obtained optimal
thresholding values for different levels of speckle noisy and low contrasted images. The fuzzy entropy method has produced better results compared to bi-level and multi-level thresholding techniques.
PRIVACY ENHANCEMENT OF NODE IN OPPORTUNISTIC NETWORK BY USING VIRTUAL-IDijsc
An entrepreneurial system is one of the sort of remote system. Delay resistance system is correspondence
organizing proposition which empowers the correspondence in such a situation where end to end way
might never be exist. Message is forward on the premise of chance. Time interim to convey a message is
long we can't evaluate or anticipate the time until we get the message. There is a security issue in these
sorts of system. In this paper we will proposed another procedure which will expand the protection of the
system and build execution of the system.
A reliable gait features are required to extract the gait sequences from an images. In this paper suggested a
simple method for gait identification which is based on moments. Moment values are extracted on different
number of frames of Gray Scale and Silhouette images of CASIA database. These moment values are
considered as feature values. Fuzzy logic and Nearest Neighbor Classifier are used for classification. Both
achieved higher recognition.
An Artificial Neural Network Model for Classification of Epileptic Seizures U...ijsc
Epilepsy is one of the most common neurological disorders characterized by transient and unexpected
electrical disturbance in the brain. In This paper the EEG signals are decomposed into a finite set of band
limited signals termed as Intrinsic mode functions. The Hilbert transom is applied on these IMF’s to
calculate instantaneous frequencies. The 2nd,3rd and 4th IMF's are used to extract features of epileptic
signal. A neural network using back propagation algorithm is implemented for classification of epilepsy.
An overall accuracy of 99.8% is achieved in classification..
An artificial neural network model for classification of epileptic seizures u...ijsc
Epilepsy is one of the most common neurological disorders characterized by transient and unexpected
electrical disturbance in the brain. In This paper
the EEG signals are decomposed into a finite set of
bandlimited signals termed as Intrinsic mode functions.
The Hilbert transom is applied on these IMF’s to
calculate instantaneous frequencies. The 2nd,3rd an
d 4th IMF's are used to extract features of epilepticsignal. A neural network using back propagation alg
orithm is implemented for classification of epilepsy.An overall accuracy of 99.8% is achieved in classification..
Recently, many studies are carried out with inspirations from ecological phenomena for developing
optimization techniques. The new algorithm that is motivated by a common phenomenon in agriculture is
colonization of invasive weeds. In this paper, a modified invasive weed optimization (IWO) algorithm is
presented for optimization of multiobjective flexible job shop scheduling problems (FJSSPs) with the
criteria to minimize the maximum completion time (makespan), the total workload of machines and the
workload of the critical machine. IWO is a bio-inspired metaheuristic that mimics the ecological behaviour
of weeds in colonizing and finding suitable place for growth and reproduction. IWO is developed to solve
continuous optimization problems that’s why the heuristic rule the Smallest Position Value (SPV) is used to
convert the continuous position values to the discrete job sequences. The computational experiments show
that the proposed algorithm is highly competitive to the state-of-the-art methods in the literature since it is
able to find the optimal and best-known solutions on the instances studied.
Broad phoneme classification using signal based featuresijsc
Speech is the most efficient and popular means of human communication Speech is produced as a sequence
of phonemes. Phoneme recognition is the first step performed by automatic speech recognition system. The
state-of-the-art recognizers use mel-frequency cepstral coefficients (MFCC) features derived through short
time analysis, for which the recognition accuracy is limited. Instead of this, here broad phoneme
classification is achieved using features derived directly from the speech at the signal level itself. Broad
phoneme classes include vowels, nasals, fricatives, stops, approximants and silence. The features identified
useful for broad phoneme classification are voiced/unvoiced decision, zero crossing rate (ZCR), short time
energy, most dominant frequency, energy in most dominant frequency, spectral flatness measure and first
three formants. Features derived from short time frames of training speech are used to train a multilayer
feedforward neural network based classifier with manually marked class label as output and classification
accuracy is then tested. Later this broad phoneme classifier is used for broad syllable structure prediction
which is useful for applications such as automatic speech recognition and automatic language
identification.
PERFUSION SYSTEM CONTROLLER STRATEGIES DURING AN ECMO SUPPORTijsc
In this work modelling and control of Perfusion system is presented. The Perfusion system simultaneously
controls the partial pressures during Extra Corporeal Membrane Oxygenation (ECMO) support. The
main Problem in ECMO system is exchange of Blood Gases in the Artificial Lung (Oxygenator).It is a
highly Nonlinear Process comprising time-varying parameters, and varying time delays, it is currently
being controlled manually by trained Perfusionist. The new control strategy implemented here has a
feedback linearization routine with time-delay compensation for the Partial pressuresof Oxygen and
Carbon dioxide. The controllers were tuned robustly and tested in simulations with a detailed artificial
Lung (Oxygenator) model in Cardiopulmonary bypass conditions. This Automatic control strategy is
proposed to improve the patient’s safety by fast control reference tracking and good disturbance rejection
under varying conditions.
Methodological study of opinion mining and sentiment analysis techniquesijsc
Decision making both on individual and organizational level is always accompanied by the search of
other’s opinion on the same. With tremendous establishment of opinion rich resources like, reviews, forum
discussions, blogs, micro-blogs, Twitter etc provide a rich anthology of sentiments. This user generated
content can serve as a benefaction to market if the semantic orientations are deliberated. Opinion mining
and sentiment analysis are the formalization for studying and construing opinions and sentiments. The
digital ecosystem has itself paved way for use of huge volume of opinionated data recorded. This paper is
an attempt to review and evaluate the various techniques used for opinion and sentiment analysis.
Solving the associated weakness of biogeography based optimization algorithmijsc
Biogeography-based optimization (BBO) is a new population-based evolutionary algorithm and is based on an old theory of island biogeography that explains the geographical distribution of biological organisms. BBO was introduced in 2008 and then a lot of modifications and hybridizations were employed to enhance its performance. The researchers found that the original version of BBO has some weakness on its exploration. This paper tries to solve the root problems itself instead of solving its effect by using different techniques. It proposes two modifications; firstly, modifying the probabilistic selection process of the migration and mutation stages to give a fairly randomized selection for all the features of the islands. Secondly, the clear duplication process, which is located after the mutation stage, is sized to avoid any corruption on the suitability index variables of the non-mutated islands. The proposed modifications are extensively tested on 120 test functions with different dimensions and complexities. The results proved that the BBO performance can be enhanced effectively without embedding any additional sub-algorithm, and without using any complicated form of the immigration and emigration rates. In addition, the new BBO algorithm requires less CPU time and becomes even faster than the original simplified partial migration-based BBO. These essential modifications have to be considered as an initial step for any other modifications.
The increasing popularity of animes makes it vulnerable to unwanted usages like copyright violations and
pornography. That’s why, we need to develop a method to detect and recognize animation characters. Skin
detection is one of the most important steps in this way. Though there are some methods to detect human
skin color, but those methods do not work properly for anime characters. Anime skin varies greatly from
human skin in color, texture, tone and in different kinds of lighting. They also vary greatly among
themselves. Moreover, many other things (for example leather, shirt, hair etc.), which are not skin, can
have color similar to skin. In this paper, we have proposed three methods that can identify an anime
character’s skin more successfully as compared with Kovac, Swift, Saleh and Osman methods, which are
primarily designed for human skin detection. Our methods are based on RGB values and their comparative
relations.
Classical methods for classification of pixels in multispectral images include supervised classifiers such as
the maximum-likelihood classifier, neural network classifiers, fuzzy neural networks, support vector
machines, and decision trees. Recently, there has been an increase of interest in ensemble learning – a
method that generates many classifiers and aggregates their results. Breiman proposed Random Forestin
2001 for classification and clustering. Random Forest grows many decision trees for classification. To
classify a new object, the input vector is run through each decision tree in the forest. Each tree gives a
classification. The forest chooses the classification having the most votes. Random Forest provides a robust
algorithm for classifying large datasets. The potential of Random Forest is not been explored in analyzing
multispectral satellite images. To evaluate the performance of Random Forest, we classified multispectral
images using various classifiers such as the maximum likelihood classifier, neural network, support vector
machine (SVM), and Random Forest and compare their results.
Fault diagnosis of a high voltage transmission line using waveform matching a...ijsc
This paper is based on the problem of accurate fault diagnosis by incorporating a waveform matching technique. Fault isolation and detection of a double circuit high voltage power transmission line is of immense importance from point of view of Energy Management services. Power System Fault types namely single line to ground faults, line to line faults, double line to ground faults etc. are responsible for transients in current and voltage waveforms in Power Systems. Waveform matching deals with the approximate superimposition of such waveforms in discretized versions obtained from recording devices and Software respectively. The analogy derived from these waveforms is obtained as an error function of voltage and current, from the considered metering devices. This assists in modelling the fault identification as an optimization problem of minimizing the error between these sets of waveforms. In other words, it utilizes the benefit of software discrepancies between these two waveforms. Analysis has been done using the Bare Bones Particle Swarm Optimizer on an IEEE 2 bus, 6 bus and 14 bus system. The performance of the algorithm has been compared with an analogous meta-heuristic algorithm called BAT optimization on a 2 bus level. The primary focus of this paper is to demonstrate the efficiency of such methods and state the common peculiarities in measurements, and the possible remedies for such distortions.
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.
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.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Hybrid method for solving the non smooth cost function economic dispatch prob...IJECEIAES
This article is focused on hybrid method for solving the non-smooth cost function economic dispatch problem. The techniques were divided into two parts according to: the incremental cost rates are used to find the initial solution and bee colony optimization is used to find the optimal solution. The constraints of economic dispatch are power losses, load demand and practical operation constraints of generators. To verify the performance of the proposed algorithm, it is operated by the simulation on the MATLAB program and tests three case studies; three, six and thirteen generator units which compared to particle swarm optimization, cuckoo search algorithm, bat algorithm, firefly algorithm and bee colony optimization. The results show that the proposed algorithm is able to obtain higher quality solution efficiently than the others methods.
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.
Evolutionary algorithm solution for economic dispatch problemsIJECEIAES
A modified firefly algorithm (FA) was presented in this paper for finding a solution to the economic dispatch (ED) problem. ED is considered a difficult topic in the field of power systems due to the complexity of calculating the optimal generation schedule that will satisfy the demand for electric power at the lowest fuel costs while satisfying all the other constraints. Furthermore, the ED problems are associated with objective functions that have both quality and inequality constraints, these include the practical operation constraints of the generators (such as the forbidden working areas, nonlinear limits, and generation limits) that makes the calculation of the global optimal solutions of ED a difficult task. The proposed approach in this study was evaluated in the IEEE 30-Bus test-bed, the evaluation showed that the proposed FA-based approach performed optimally in comparison with the performance of the other existing optimizers, such as the traditional FA and particle swarm optimization. The results show the high performance of the modified firefly algorithm compared to the other methods.
Operation cost reduction in unit commitment problem using improved quantum bi...IJECEIAES
Unit Commitment (UC) is a nonlinear mixed integer-programming problem. UC used to minimize the operational cost of the generation units in a power system by scheduling some of generators in ON state and the other generators in OFF state according to the total power outputs of generation units, load demand and the constraints of power system. This paper proposes an Improved Quantum Binary Particle Swarm Optimization (IQBPSO) algorithm. The tests have been made on a 10-units simulation system and the results show the improvement in an operation cost reduction after using the proposed algorithm compared with the ordinary Quantum Binary Particle Swarm Optimization (QBPSO) algorithm
The problem of power system optimization has become a deciding factor in electrical power system engineering practice with emphasis on cost and emission reduction. The economic emission dispatch (EED) problem has been addressed in this paper using a Biogeography-based optimization (BBO). The BBO is inspired by geographical distribution of species within islands. This optimization algorithm works on the basis of two concepts-migration and mutation. In this paper a non-uniform mutation operator has been employed. The proposed technique shows better diversified search process and hence finds solutions more accurately with high convergence rate. The BBO with new mutation operator is tested on ten unit system. The comparison which is based on efficiency, reliability and accuracy shows that proposed mutation operator is competitive to the present one.
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.
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.
PuShort Term Hydrothermal Scheduling using Evolutionary Programmingblished pa...Satyendra Singh
In this paper, Evolutionary Programming method
is used for short term hydrothermal scheduling which minimize
the total fuel cost while satisfying the constraints. This paper
developed and studies the performance of evolutionary programs
in solving hydrothermal scheduling problem. The effectiveness of
the developed program is tested for the system having one hydro
and one thermal unit for 24 hour load demand. Numerical results
show that highly near-optimal solutions can be obtained by
Evolutionary Programming.
Security constrained optimal load dispatch using hpso technique for thermal s...eSAT Journals
Abstract This paper presents Hybrid Particle Swarm Optimization (HPSO) technique to solve the Optimal Load Dispatch (OLD) problems with line flow constrain, bus voltage limits and generator operating constraints. In the proposed HPSO method both features of EP and PSO are incorporated, so the combined HPSO algorithm may become more effective to find the optimal solutions. In this paper, the proposed Hybrid PSO, PSO and EP techniques have been tested on IEEE14, 30 bus systems. Numerical simulation results show that the Hybrid PSO algorithm outperformed standard PSO algorithm and Evolution Programming method on the same problem and can save considerable cost of Optimal Load Dispatch.
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.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Connector Corner: Automate dynamic content and events by pushing a button
HYBRID PARTICLE SWARM OPTIMIZATION FOR SOLVING MULTI-AREA ECONOMIC DISPATCH PROBLEM
1. International Journal on Soft Computing (IJSC) Vol.4, No.2, May 2013
DOI: 10.5121/ijsc.2013.4202 17
HYBRID PARTICLE SWARM OPTIMIZATION FOR
SOLVING MULTI-AREA ECONOMIC DISPATCH
PROBLEM
Huynh Thi Thanh Binh
School of Information and Communication Technology,
HaNoi University of Science and Technology, Ha Noi, Viet Nam
binhht@soict.hut.edu.vn
ABSTRACT
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.
KEYWORDS
Multi-Area Economic Dispatch, Particle Swarm Optimization.
1. INTRODUCTION
In recent years, energy deregulation of the electricity industry has been deployed in many
countries to improve the economic efficiency of power system operation. It has been used widely
in a more competitive market of power industry. However, even in a competitive environment,
we have to guarantee the adequate level of reliability to supply for customers. Therefore,
economic dispatch (ED) is one of the main problems in many energy systems. In this paper, we
focus on solving ED problem in multi-area environment, so we call this problem MAEDP (Multi-
Area Economic Dispatch problem).
Our MAEDP system is comprised of three components. The generators are the electricity
suppliers. The areas are where to receive and consume electricity from generators in our system.
The tie-lines are the ways to transport electricity between two areas. The objective of MAEDP is
to determine the generation levels and the power interchange between two areas which would
minimize total fuel costs in all areas while satisfying power balance, generating limit and
transmission capacity constraints. If an area with excess power is not adjacent to a power
deficient area, or the tie-line between the two areas is at the transmission limit, it is necessary to
find an alternative path between these two areas in order to transmit additional power.
The most simple cost function of each generator can be represented as a quadratic function as
follows.
2. International Journal on Soft Computing (IJSC) Vol.4, No.2, May 2013
18
2
( )u u u u u u uF P a P b P c= + + (1)
Where , ,u u ua b c are fuel-cost coefficients of the generatoru and uP is the generated power of the
generator .u
In fact, the cost function of the MAED problem has non-differential points depending on the
valve-point effects or the change of fuels; therefore, the cost function is usually a non-smooth
function. This paper will consider two cases of non-smooth cost functions. The first case
considers the valve-point effect where the objective function is normally expressed as the
superposition of a sinusoidal function and a quadratic function. Another addresses multiple fuels
where the objective function is described as a set of piecewise quadratic functions.
1.1. ED Problem Considering Valve-Point Effects
The generator with multi-valve steam turbines has input-output curve which is very different from
the smooth cost function. To calculate the accurate cost curve of each generator, the valve-point
effects must be included in the cost model. Therefore, the sinusoidal function is incorporated into
the quadratic function. The fuel cost of the generator u can be formulated as follows:
2 min
( ) | sin( ( )) |u u u u u u u u u u uF P a P b P c e f P P= + + + × × − (2)
Where ue and uf are the coefficients of the generator u representing valve-point effects.
1.2. ED Problem Considering Multiple Fuels
When the generators are supplied by multiple fuel sources, the cost of each generator is
represented by several piecewise quadratic functions reflecting the effects of fuel changes. The
fuel cost function for such a case should be practically expressed as:
1 2 1 1 min 1
2 2 2 2 1 2
2 ( 1) max
( ) ....
....
l l l l
u u u u u u u u
l l l l l
u u u u u u u u
u u
lk lk lk l k
u u u u u u u u
a P b P c if P P P
a P b P c if P P P
F P
a P b P c if P P P−
+ + ≤ ≤
+ + ≤ ≤
=
+ + ≤ ≤
(3)
Where ,lk lk
u ua b and lk
uc are the fuel-cost coefficients of the generator u and k = 1, 2,. . ., k: the
number of available fuels.
( 1)
, :l k lk
u uP P−
capacity consumption
min max
, :u uP P minimum and maximum capacity of generator u
The objective of MAEDP is to determine the generated powers uP of generators so that the total
fuel cost for the N number of generators is minimal. Therefore, the objective function of MAEDP
is as follows.
3. International Journal on Soft Computing (IJSC) Vol.4, No.2, May 2013
19
1
( )
N
u u
u
Minimize F P
=
∑ (4)
Where ( )u uF P is the fuel cost function of generator u.
Subject to:
i. Area Power Balance Constraints
| | |l l
u al l
u u a l T o a l From a
P P PD P
∀ ∈ ∀ = ∀ =
+ = +∑ ∑ ∑ (5)
ii. Generating Limit Constraints
| | |l l
u al l
u u a l T o a l From a
P P PD P
∀ ∈ ∀ = ∀ =
+ = +∑ ∑ ∑ (6)
iii. Tie-line Limit Constraints
max max
l l lP P P− ≤ ≤ (7)
max
:lP maximum transmission capacity of line l;
:lP transmission capacity of line l.
:aPD addition charge demand of generator a
In this paper, we propose new algorithm to solve the MAEDP, called Hybrid Particle Swarm
Optimization (HGAPSO), which is the combination of Particle Swarm Optimization (PSO)
algorithm and Genetic Algorithm (GA) model. This combination not only able to change search
area by considering the value of ,best iP and ,bestG but it also improve the running time.
The rest of this paper is organized as follows. Section 2 describes the related works. Our new
proposed algorithm is showed in section 3. Section 4 gives our experiments and computational
and comparative results. The paper concludes with discussions and future works in section 5.
2. RELATED WORKS
Economic dispatch is a complex problem with many different models and has been set up for a
long time. Through twenty years, along with the development of competitive electricity markets
and smart grid technology, many new models of the problem have been introduced. At the same
time, the heuristic algorithms also provide a new approach to solve the ED problem.
In 1994, Bakirtzis et al. proposed a Binary-Coded Genetic Algorithm for traditional ED problem
with the generating limit constraints and power balance constraints [3, 4]. In 2005, Hamid
Bouzeboudja et al. solve that problem by Real-Coded Genetic Algorithm [5] and Jong-Bae Park
et al. proposed PSO algorithm [1, 2] for the ED problem which has the non-smooth cost function
[6]. With smooth cost functions, they have provided the global solution satisfying the constraints.
And their global solution has a high probability for 3-generator system and it is better than other
heuristic approaches for 40-generator system. However, they have just solved ED problem with
one or two area. The next year, GA and PSO approaches are modified to apply to the more
complex model of ED problem such as: generators with valve point effect and multiple fuels,
ramp rate limit, thermal generator forbidden zones…
MAEDP problem with transmission capacity limit constraints were also interested in solving in
recent years. Zarei et al. introduced Direct Search Method (DSM) for the two-area problem in
2007 [7]. In 2008, Nasr Azadani et al. solve the two-area problem with spinning reserve
constraints by PSO [8]. In 2009, Prasanna et al. proposed algorithms which are based on the
combined application of Fuzzy logic strategy incorporated in both Evolutionary Programming
4. International Journal on Soft Computing (IJSC) Vol.4, No.2, May 2013
20
and Tabu-Search algorithms for the n-area problem. In 2010 and 2011, Manjaree Pandit et al.
introduced and compare different versions of PSO for the MAED problem with n-area [8, 9]. The
results show that the PSO algorithm achieves better solutions and faster computation time than
previous methods.
In [14], Binh et al. proposed genetic algorithm, called RCGA, for solving MAEDP. The main idea
of this algorithm is to make a modified GAs employing real valued vector for representation of
the chromosomes. The use of real number representation in the GAs has a number of advantages
in numerical function optimization compared to the binary representation as: no need to convert
chromosomes to binary code; requires less memory capacity; no loss of accuracy incurred by
discretion of the binary value and easy implementation of different genetic operators. This
algorithm gives good results, but the running time is so great.
In next section a new approach using PSO will be introduced. The new algorithm can achieve
better result than previous methods in some known benchmark and the running time are
improved.
3. HYBRID OF GENETIC ALGORITHM AND PARTICLE SWARM
OPTIMIZATION FOR SOLVING MAEDP
3.1. Individual Representation
We propose to apply real-coded to encode solution. An individual is represented by a
chromosome whose length is equal to U + L (U: the number of generators; L: the number of tie-
lines in the system).
Figure 1. Individual representation
Figure 1 shows that ( 1 )iP i U= → represents the generated power of the generator i, and
( 1 )jT j L= → represents the transport power in tie-line j.
Each individual has two components which are position and velocity vector. With MAEDP, the
position and velocity vector are represented bellow:
( ) ( )1 2 1 2, ,..., , , ,...,i iU iLi i i iX P P P T T T
= (8)
( ) ( )1 2 1 2, ,..., , , ,...,i i i iU i i iLV VP VP VP VT VT VT = (9)
In the first population, we initialize randomly the velocity value as satisfying:
( ) ( )min 0 0 max 0
iu iu iu iu iuP P VP P Pε ε− − ≤ ≤ + − (10)
( ) ( )min 0 0 max 0
il il il il ilor T T VT T Tε ε− − ≤ ≤ + − (11)
Where
5. International Journal on Soft Computing (IJSC) Vol.4, No.2, May 2013
21
ε : the very small positive real number,
0
iuP : initial power of generator u on individual i,
0
ilT : initial power of tie-line l on individual i.
0 0
, :iu ilVP VT initial velocity vector of power of generator u or power of tie-line l on
individual i.
3.2. Fitness function
We use fitness function to estimate the optimality of solution. Fitness value of an individual is
calculated by the total of the fuel cost of all generators and penal cost when the system does not
guarantee the energy balance constraint. Therefore, this function is as follows:
1 1
( )
U A
u u a
u a
Fitness F P PenCoef AreaV
= =
= + ∗∑ ∑ (12)
( ) ( )a a a a aAreaV PG Pin PD Pout= + − + (13)
Where uP is the generated power of generator u.
PenCoef : the penal coefficient.
U, A: the number of generators and the number of areas respectively.
aAreaV is penal cost when the system does not guarantee the energy balance constraint and
calculated as above.
3.3. Individuals updated
Each iteration, position and velocity are updated by
( ) ( )1
1 1 2 2
k k k k
i i i ibesti bestV V w C R P X C R G X+
= ∗ + ∗ ∗ − + ∗ ∗ − (14)
1 1k k k
i i iX X V+ +
= + (15)
• k
iV : velocity of individual i at iteration k;
• k
iX : position of individual i at iteration k;
• ,best iP : the best position of individual i up to the iteration k;
• bestG : the best position of population up to the iteration k;
• w: inertia weight
• 1C : cognitive acceleration coefficient;
• 2C : social acceleration coefficient;
• 1 2,R R : randomize number between 0 and 1.
6. International Journal on Soft Computing (IJSC) Vol.4, No.2, May 2013
22
Figure 2. Individuals updated
Inertia weight: High inertia weight is good for global search while small one is good for local
search [2]. So, it it better if we use high inertia weight in the first step and descending to the final
step. We propose time-varying inertia weight as bellow.
( ) max
maxmin min
max
iter iter
w w w w
iter
−
= + − × (16)
In which
• wmin, wmax: range on inertia weight;
• iter : the number of iteration;
• maxiter : maximum iteration.
Acceleration factors
( )1 1 1 1
max
i f i
iter
C C C C
iter
= + − × (17)
( )2 2 2 2
max
i f i
iter
C C C C
iter
= + − × (18)
1 1 2 2, , ,i f i fC C C C : initial and final value of cognitive acceleration and social acceleration.
3.4. Crossover Operator
In population i, we choose two randomly individuals called parent1 and parent2 and recombine
them to create two childs. The position and velocity vectors of offspring are formulated
respectively as followings:
1 1 2( ) ( ) (1.0 ) ( )i i i i iChild x p parent x p parent x= ∗ + − ∗ (19)
2 2 1( ) ( ) (1.0 ) ( )i i i i iChild x p parent x p parent x= ∗ + − ∗ (20)
The velocity of the child is calculated as bellow:
7. International Journal on Soft Computing (IJSC) Vol.4, No.2, May 2013
23
( )1 2
1 1
1 2
( ) ( )
( )
( ) ( )
parent v parent v
Child v parent v
parent v parent v
+
=
+
r r
r r
r r (21)
( )1 2
2 2
1 2
( ) ( )
( )
( ) ( )
parent v parent v
Child v parent v
parent v parent v
+
=
+
r r
r r
r r (22)
ip : randomized number between 0 and 1.
3.5. Proposed algorithm structure
1. Procedure GeneticAlgorithm
2. Begin
3. Initialize population
4. Update GBest
5. While !(Maximum_Number Interations)
6. Individual Updated
7. For i = 1 to number_crossover do
Begin
8. Random parent1, parent2
9. Random pi
10. Create Child1, Child2
11. Calculate Child1( v
r
),Child2( v
r
)
12. Endfor
13. Update Pbest,i,GBest
14. Endwhile
15. Return GBest
4. EXPERIMENTAL RESULTS
4.1. Problem Instances
In our experiments, we used five test systems that have different sizes and nonlinearities. They
are: test systems I taken from [10] (one area, 3 generators, quadratic cost function), test system II
taken from [11] (2 areas, 4 generators, quadratic cost function), test system III taken from [12] (3
areas, 10 generators, with three fuel options), test system IV taken from [13] (2 areas, 40
generators, with valve-point effects), and test system V taken from [13] (2 areas, 120 generators,
quadratic cost function). With each test system, we create four different values of total power
demand (PD). Therefore, we have 20 test cases.
4.2. Experiment Setup
We experiment our proposed algorithm independently and compare its performance with PSO-
TVAC [9, 10] and RCGA [14].
4.3. System Setting
The parameters are used in our algorithm:
Population size: 400
Max iteration: 100
Minimum inertia weight: 0.4
Maximum inertia weight: 0.9
8. International Journal on Soft Computing (IJSC) Vol.4, No.2, May 2013
24
Initial value of cognitive acceleration factors: 1.8
Final value of cognitive acceleration factors: 0.2
Initial value of social acceleration factors: 0.2
Final value of social acceleration factors: 1.9
Crossover probability: 0.7
Our system is run 20 times for each test set. All the programs are run on a machine with Intel
Core 2 Duo P7450 2.13Ghz, RAM 3GB, Windows 7 Professional, and are installed in C#
language.
4.4. Computational Results
Table 1. Comparison of the best results found by HGAPSO, PSO-TVAC and RCGA
No
Test
system
PD
Min
PSO-TVAC RCGA HGAPSO
1
I
1 100,00% 100,00% 100,00%
2 2 100,00% 100,00% 100,00%
3 3 100,00% 100,02% 100,00%
4 4 100,00% 100,01% 100,00%
5
II
1 100,00% 100,52% 100,02%
6 2 100,00% 100,41% 99,98%
7 3 100,00% 100,24% 100,07%
8 4 100,00% 100,06% 100,00%
9
III
1 100,00% 97,17% 98,02%
10 2 100,00% 100,16% 99,89%
11 3 100,00% 105,23% 100,01%
12 4 100,00% 105,96% 100,00%
13
IV
1 100,00% 99,09% 100,62%
14 2 100,00% 100,35% 101,77%
15 3 100,00% 101,44% 104,19%
16 4 100,00% 99,30% 100,13%
17
V
1 100,00% 104,25% 101,12%
18 2 100,00% 100,55% 99,19%
19 3 100,00% 103,32% 103,00%
20 4 100,00% 102,59% 103,01%
9. International Journal on Soft Computing (IJSC) Vol.4, No.2, May 2013
25
Table 2. Comparison of the average results found HGAPSO, PSO-TVAC and RCGA
No
Test
system
PD
Average
PSO-TVAC RCGA HGAPSO
1
I
1 100,00% 99,91% 100,10%
2 2 100,00% 99,94% 100,04%
3 3 100,00% 100,03% 100,03%
4 4 100,00% 100,02% 100,00%
5
II
1 100,00% 99,27% 99,25%
6 2 100,00% 99,87% 99,75%
7 3 100,00% 100,03% 99,96%
8 4 100,00% 116,41% 99,96%
9
III
1 100,00% 93,57% 95,93%
10 2 100,00% 101,40% 99,84%
11 3 100,00% 123,64% 100,00%
12 4 100,00% 111,87% 99,99%
13
IV
1 100,00% 95,81% 99,51%
14 2 100,00% 98,12% 102,86%
15 3 100,00% 98,02% 103,39%
16 4 100,00% 96,79% 101,91%
17
V
1 100,00% 101,54% 101,79%
18 2 100,00% 100,16% 101,76%
19 3 100,00% 100,09% 100,97%
20 4 100,00% 99,29% 103,62%
10. International Journal on Soft Computing (IJSC) Vol.4, No.2, May 2013
26
Table 3. Comparison of the running time found by HGAPSO, PSO-TVAC and RCGA
No
Test
system
PD
Time
PSO-TVAC RCGA HGAPSO
1
I
1 100,00% 241,65% 89,49%
2 2 100,00% 255,36% 91,39%
3 3 100,00% 254,70% 91,72%
4 4 100,00% 254,47% 94,88%
5
II
1 100,00% 280,11% 86,04%
6 2 100,00% 278,21% 87,92%
7 3 100,00% 286,46% 89,19%
8 4 100,00% 281,13% 88,65%
9
III
1 100,00% 450,02% 81,35%
10 2 100,00% 455,81% 82,32%
11 3 100,00% 457,01% 82,75%
12 4 100,00% 462,80% 83,10%
13
IV
1 100,00% 614,82% 79,29%
14 2 100,00% 611,01% 79,49%
15 3 100,00% 622,53% 79,58%
16 4 100,00% 617,50% 78,76%
17
V
1 100,00% 975,79% 87,59%
18 2 100,00% 1198,74% 89,69%
19 3 100,00% 1257,07% 90,97%
20 4 100,00% 1258,36% 91,32%
• Table 1 shows that the best results found by HGAPSO are better than or equal to PSO-
TVAC and RCGA in 9/20 test cases.
• Table 2 shows that the average results found by HGAPSO are better than or equal to
PSO-TVAC and RCGA in 8/20 test cases.
• Table 3 shows that the running time of HGAPSO is better than PSO-TVAC and RCGA in
all of the test cases.
• The experiment results show that the combination between PSO and GA can find better
results in the fastest running time.
5. CONCLUSION
In this paper, we proposed new hybrid particle swarm optimization algorithm for solving
MAEDP. We experimented on five test systems. With each test system, we create 4 test cases
which are different from the value of total power demand. The results show that our proposed
approaches are stable and quite effective with MAEDP. The running time of HGAPSO is fastest
compare to PSO-TVAC and RCGA.
In the future work, we are planning to improve the algorithm for solving MAEDP with more
constraints. Moreover, we hope that we can find the other approach with better results for
MAEDP.
11. International Journal on Soft Computing (IJSC) Vol.4, No.2, May 2013
27
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Author
Huynh Thi Thanh Binh has received Master and PhD degree in Computer Science field at
Ha Noi University of Science and Technology on 1999 and 2011. Her research is
computational Intelligence: genetic algorithms, heuristic algorithms.Dr. Huynh Thi Thanh
Binh is now lecturer at Ha Noi University of Science and Technology, School of Information
and Communication Technology.Dr. Binh is member of IEEE (from 2006 to now). Now, she
is treasure of IEEE Viet Nam Section. Dr. Binh also is member of ACM. Dr. Binh has served
as the organizing chair of the SoICT2011, SoICT2012, a program committee of the international
conference SoCPaR 2013, KSE 2013, ACIIDS 2013, SEAL 2012, ICCASA 2012, ICCCI 2012, KSE 2012,
reviewer of International Journal Intelligent Information and Database Systems, INFORMS Journal on
Computing, Journal of Theoretical and Applied Computer Science.