In this paper Enhanced Wormhole Optimizer (EWO) algorithm is used to solve optimal reactive power problem. Proposed algorithm based on the Wormholes which exploits the exploration space. Between different universes objects are exchanged through white or black hole tunnels. Regardless of the inflation rate, through wormholes objects in all universes which possess high probability will shift to the most excellent universe. In the projected Enhanced Wormhole Optimizer (EWO) algorithm in order to avoid the solution to be get trapped into the local optimal solution Levy flight has been applied. Projected Enhanced Wormhole Optimizer (EWO) algorithm has been tested in standard IEEE 14, 30, 57,118,300 bus test systems and simulation results show that the EWO algorithm reduced the real power loss efficiently.
The modern power system around the world has grown in complexity of interconnection and
power demand. The focus has shifted towards enhanced performance, increased customer focus,
low cost, reliable and clean power. In this changed perspective, scarcity of energy resources,
increasing power generation cost, environmental concern necessitates optimal economic dispatch.
In reality power stations neither are at equal distances from load nor have similar fuel cost
functions. Hence for providing cheaper power, load has to be distributed among various power
stations in a way which results in lowest cost for generation. Practical economic dispatch (ED)
problems have highly non-linear objective function with rigid equality and inequality constraints.
Particle swarm optimization (PSO) is applied to allot the active power among the generating
stations satisfying the system constraints and minimizing the cost of power generated. The
viability of the method is analyzed for its accuracy and rate of convergence. The economic load
dispatch problem is solved for three and six unit system using PSO and conventional method for
both cases of neglecting and including transmission losses. The results of PSO method were
compared with conventional method and were found to be superior. The conventional
optimization methods are unable to solve such problems due to local optimum solution
convergence. Particle Swarm Optimization (PSO) since its initiation in the last 15 years has been
a potential solution to the practical constrained economic load dispatch (ELD) problem. The
optimization technique is constantly evolving to provide better and faster results.
While writing the report on our project seminar, we were wondering that Science and smart
technology are as ever expanding field and the engineers working hard day and night and make
the life a gift for us
In this paper Enhanced whale Optimization Algorithm (EWO) proposed to solve the optimal reactive power problem. Whale optimization algorithm is modeled by Bubble-net hunting tactic. In the projected optimization algorithm an inertia weight ω ∈ [1, 0] has been introduced to perk up the search ability. Whales are commonly moving 10-16 meters down then through the bubbles which are created artificially then they encircle the prey and move upward towards the surface of sea. Proposed Enhanced whale optimization algorithm (EWO) is tested in standard IEEE 57 bus systems and power loss reduced considerably.
This paper projects Gryllidae Optimization Algorithm (GOA) has been applied to solve optimal reactive power problem. Proposed GOA approach is based on the chirping characteristics of Gryllidae. In common, male Gryllidae chirp, on the other hand some female Gryllidae also do as well. Male Gryllidae draw the females by this sound which they produce. Moreover, they caution the other Gryllidae against dangers with this sound. The hearing organs of the Gryllidae are housed in an expansion of their forelegs. Through this, they bias to the produced fluttering sounds. Proposed Gryllidae Optimization Algorithm (GOA) has been tested in standard IEEE 14, 30 bus test systems and simulation results show that the projected algorithms reduced the real power loss considerably.
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
This paper studies an approximate dynamic programming (ADP) strategy of a group of nonlinear switched systems, where the external disturbances are considered. The neural network (NN) technique is regarded to estimate the unknown part of actor as well as critic to deal with the corresponding nominal system. The training technique is simul-taneously carried out based on the solution of minimizing the square error Hamilton function. The closed system’s tracking error is analyzed to converge to an attraction region of origin point with the uniformly ultimately bounded (UUB) description. The simulation results are implemented to determine the effectiveness of the ADP based controller.
Importance sampling has been widely used to improve the efficiency of deterministic computer simulations where the simulation output is uniquely determined, given a fixed input. To represent complex system behavior more realistically, however, stochastic computer models are gaining popularity. Unlike deterministic computer simulations, stochastic simulations produce different outputs even at the same input. This extra degree of stochasticity presents a challenge for reliability assessment in engineering system designs. Our study tackles this challenge by providing a computationally efficient method to estimate a system's reliability. Specifically, we derive the optimal importance sampling density and allocation procedure that minimize the variance of a reliability estimator. The application of our method to a computationally intensive, aeroelastic wind turbine simulator demonstrates the benefits of the proposed approaches.
The modern power system around the world has grown in complexity of interconnection and
power demand. The focus has shifted towards enhanced performance, increased customer focus,
low cost, reliable and clean power. In this changed perspective, scarcity of energy resources,
increasing power generation cost, environmental concern necessitates optimal economic dispatch.
In reality power stations neither are at equal distances from load nor have similar fuel cost
functions. Hence for providing cheaper power, load has to be distributed among various power
stations in a way which results in lowest cost for generation. Practical economic dispatch (ED)
problems have highly non-linear objective function with rigid equality and inequality constraints.
Particle swarm optimization (PSO) is applied to allot the active power among the generating
stations satisfying the system constraints and minimizing the cost of power generated. The
viability of the method is analyzed for its accuracy and rate of convergence. The economic load
dispatch problem is solved for three and six unit system using PSO and conventional method for
both cases of neglecting and including transmission losses. The results of PSO method were
compared with conventional method and were found to be superior. The conventional
optimization methods are unable to solve such problems due to local optimum solution
convergence. Particle Swarm Optimization (PSO) since its initiation in the last 15 years has been
a potential solution to the practical constrained economic load dispatch (ELD) problem. The
optimization technique is constantly evolving to provide better and faster results.
While writing the report on our project seminar, we were wondering that Science and smart
technology are as ever expanding field and the engineers working hard day and night and make
the life a gift for us
In this paper Enhanced whale Optimization Algorithm (EWO) proposed to solve the optimal reactive power problem. Whale optimization algorithm is modeled by Bubble-net hunting tactic. In the projected optimization algorithm an inertia weight ω ∈ [1, 0] has been introduced to perk up the search ability. Whales are commonly moving 10-16 meters down then through the bubbles which are created artificially then they encircle the prey and move upward towards the surface of sea. Proposed Enhanced whale optimization algorithm (EWO) is tested in standard IEEE 57 bus systems and power loss reduced considerably.
This paper projects Gryllidae Optimization Algorithm (GOA) has been applied to solve optimal reactive power problem. Proposed GOA approach is based on the chirping characteristics of Gryllidae. In common, male Gryllidae chirp, on the other hand some female Gryllidae also do as well. Male Gryllidae draw the females by this sound which they produce. Moreover, they caution the other Gryllidae against dangers with this sound. The hearing organs of the Gryllidae are housed in an expansion of their forelegs. Through this, they bias to the produced fluttering sounds. Proposed Gryllidae Optimization Algorithm (GOA) has been tested in standard IEEE 14, 30 bus test systems and simulation results show that the projected algorithms reduced the real power loss considerably.
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
This paper studies an approximate dynamic programming (ADP) strategy of a group of nonlinear switched systems, where the external disturbances are considered. The neural network (NN) technique is regarded to estimate the unknown part of actor as well as critic to deal with the corresponding nominal system. The training technique is simul-taneously carried out based on the solution of minimizing the square error Hamilton function. The closed system’s tracking error is analyzed to converge to an attraction region of origin point with the uniformly ultimately bounded (UUB) description. The simulation results are implemented to determine the effectiveness of the ADP based controller.
Importance sampling has been widely used to improve the efficiency of deterministic computer simulations where the simulation output is uniquely determined, given a fixed input. To represent complex system behavior more realistically, however, stochastic computer models are gaining popularity. Unlike deterministic computer simulations, stochastic simulations produce different outputs even at the same input. This extra degree of stochasticity presents a challenge for reliability assessment in engineering system designs. Our study tackles this challenge by providing a computationally efficient method to estimate a system's reliability. Specifically, we derive the optimal importance sampling density and allocation procedure that minimize the variance of a reliability estimator. The application of our method to a computationally intensive, aeroelastic wind turbine simulator demonstrates the benefits of the proposed approaches.
PSOk-NN: A Particle Swarm Optimization Approach to Optimize k-Nearest Neighbo...Aboul Ella Hassanien
This talk presented at Bio-inspiring and evolutionary computation: Trends, applications and open issues workshop, 7 Nov. 2015 Faculty of Computers and Information, Cairo University
In this work Tundra wolf algorithm (TWA) is proposed to solve the optimal reactive power problem. In the projected Tundra wolf algorithm (TWA) in order to avoid the searching agents from trapping into the local optimal the converging towards global optimal is divided based on two different conditions. In the proposed Tundra wolf algorithm (TWA) omega tundra wolf has been taken as searching agent as an alternative of indebted to pursue the first three most excellent candidates. Escalating the searching agents’ numbers will perk up the exploration capability of the Tundra wolf wolves in an extensive range. Proposed Tundra wolf algorithm (TWA) has been tested in standard IEEE 14, 30 bus test systems and simulation results show the proposed algorithm reduced the real power loss effectively.
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.
An Improved Adaptive Multi-Objective Particle Swarm Optimization for Disassem...IJRESJOURNAL
With the development of productivity and the fast growth of the economy, environmental pollution, resource utilization and low product recovery rate have emerged subsequently, so more and more attention has been paid to the recycling and reuse of products. However, since the complexity of disassembly line balancing problem (DLBP) increases with the number of parts in the product, finding the optimal balance is computationally intensive. In order to improve the computational ability of particle swarm optimization (PSO) algorithm in solving DLBP, this paper proposed an improved adaptive multi-objective particle swarm optimization (IAMOPSO) algorithm. Firstly, the evolution factor parameter is introduced to judge the state of evolution using the idea of fuzzy classification and then the feedback information from evolutionary environment is served in adjusting inertia weight, acceleration coefficients dynamically. Finally, a dimensional learning strategy based on information entropy is used in which each learning object is uncertain. The results from testing in using series of instances with different size verify the effect of proposed algorithm.
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.
A COMPARISON OF PARTICLE SWARM OPTIMIZATION AND DIFFERENTIAL EVOLUTIONijsc
Two modern optimization methods including Particle Swarm Optimization and Differential Evolution are
compared on twelve constrained nonlinear test functions. Generally, the results show that Differential
Evolution is better than Particle Swarm Optimization in terms of high-quality solutions, running time and
robustness.
The myphotonics project deals with the construction of opto-mechanical components and optical experiment implementation using modular systems such as LEGO®.
The components are low cost and the instructions that originated them are free to use. OpenAdaptonik and myphotonics can work together sharing the same purpose.
DriP PSO- A fast and inexpensive PSO for drifting problem spacesZubin Bhuyan
Particle Swarm Optimization is a class of stochastic, population based optimization techniques which are mostly suitable for static problems. However, real world optimization problems are time variant, i.e., the problem space changes over time. Several researches have been done to address this dynamic optimization problem using Particle Swarms. In this paper we probe the issues of tracking and optimizing Particle Swarms in a dynamic system where the problem-space drifts in a particular direction. Our assumption is that the approximate amount of drift is known, but the direction of the drift is unknown. We propose a Drift Predictive PSO (DriP-PSO) model which does not incur high computation cost, and is very fast and accurate. The main idea behind this technique is to use a few stagnant particles to determine the approximate direction in which the problem-space is drifting so that the particle velocities may be adjusted accordingly in the subsequent iteration of the algorithm.
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.
PSOk-NN: A Particle Swarm Optimization Approach to Optimize k-Nearest Neighbo...Aboul Ella Hassanien
This talk presented at Bio-inspiring and evolutionary computation: Trends, applications and open issues workshop, 7 Nov. 2015 Faculty of Computers and Information, Cairo University
In this work Tundra wolf algorithm (TWA) is proposed to solve the optimal reactive power problem. In the projected Tundra wolf algorithm (TWA) in order to avoid the searching agents from trapping into the local optimal the converging towards global optimal is divided based on two different conditions. In the proposed Tundra wolf algorithm (TWA) omega tundra wolf has been taken as searching agent as an alternative of indebted to pursue the first three most excellent candidates. Escalating the searching agents’ numbers will perk up the exploration capability of the Tundra wolf wolves in an extensive range. Proposed Tundra wolf algorithm (TWA) has been tested in standard IEEE 14, 30 bus test systems and simulation results show the proposed algorithm reduced the real power loss effectively.
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.
An Improved Adaptive Multi-Objective Particle Swarm Optimization for Disassem...IJRESJOURNAL
With the development of productivity and the fast growth of the economy, environmental pollution, resource utilization and low product recovery rate have emerged subsequently, so more and more attention has been paid to the recycling and reuse of products. However, since the complexity of disassembly line balancing problem (DLBP) increases with the number of parts in the product, finding the optimal balance is computationally intensive. In order to improve the computational ability of particle swarm optimization (PSO) algorithm in solving DLBP, this paper proposed an improved adaptive multi-objective particle swarm optimization (IAMOPSO) algorithm. Firstly, the evolution factor parameter is introduced to judge the state of evolution using the idea of fuzzy classification and then the feedback information from evolutionary environment is served in adjusting inertia weight, acceleration coefficients dynamically. Finally, a dimensional learning strategy based on information entropy is used in which each learning object is uncertain. The results from testing in using series of instances with different size verify the effect of proposed algorithm.
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.
A COMPARISON OF PARTICLE SWARM OPTIMIZATION AND DIFFERENTIAL EVOLUTIONijsc
Two modern optimization methods including Particle Swarm Optimization and Differential Evolution are
compared on twelve constrained nonlinear test functions. Generally, the results show that Differential
Evolution is better than Particle Swarm Optimization in terms of high-quality solutions, running time and
robustness.
The myphotonics project deals with the construction of opto-mechanical components and optical experiment implementation using modular systems such as LEGO®.
The components are low cost and the instructions that originated them are free to use. OpenAdaptonik and myphotonics can work together sharing the same purpose.
DriP PSO- A fast and inexpensive PSO for drifting problem spacesZubin Bhuyan
Particle Swarm Optimization is a class of stochastic, population based optimization techniques which are mostly suitable for static problems. However, real world optimization problems are time variant, i.e., the problem space changes over time. Several researches have been done to address this dynamic optimization problem using Particle Swarms. In this paper we probe the issues of tracking and optimizing Particle Swarms in a dynamic system where the problem-space drifts in a particular direction. Our assumption is that the approximate amount of drift is known, but the direction of the drift is unknown. We propose a Drift Predictive PSO (DriP-PSO) model which does not incur high computation cost, and is very fast and accurate. The main idea behind this technique is to use a few stagnant particles to determine the approximate direction in which the problem-space is drifting so that the particle velocities may be adjusted accordingly in the subsequent iteration of the algorithm.
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.
Locational marginal pricing framework in secured dispatch scheduling under co...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.
On the dynamic behavior of the current in the condenser of a boost converter ...TELKOMNIKA JOURNAL
In this paper, an analytical and numerical study is conducted on the dynamics of the current in the condenser of a boost converter controlled with ZAD, using a pulse PWM to the symmetric center. A stability analysis of periodic 1T-orbits was made by the analytical calculation of the eigenvalues of the Jacobian matrix of the dynamic system, where the presence of flip and Neimar–Sacker-type bifurcations was determined. The presence of chaos, which is controlled by ZAD and FPIC techniques, is shown from the analysis of Lyapunov exponents.
Simulation, bifurcation, and stability analysis of a SEPIC converter control...IJECEIAES
This article presents some results of SEPIC converter dynamics when controlled by a center pulse width modulator controller (CPWM). The duty cycle is calculated using the ZAD (Zero Average Dynamics) technique. Results obtained using this technique show a great variety of non-linear phenomena such as bifurcations and chaos, as parameters associated with the switching surface. These phenomena have been studied in the present paper in numerical form. Simulations were done in MATLAB.
Intelligent fault diagnosis for power distribution systemcomparative studiesnooriasukmaningtyas
Short circuit is one of the most popular types of permanent fault in power distribution system. Thus, fast and accuracy diagnosis of short circuit failure is very important so that the power system works more effectively. In this paper, a newly enhanced support vector machine (SVM) classifier has been investigated to identify ten short-circuit fault types, including single line-toground faults (XG, YG, ZG), line-to-line faults (XY, XZ, YZ), double lineto-ground faults (XYG, XZG, YZG) and three-line faults (XYZ). The performance of this enhanced SVM model has been improved by using three different versions of particle swarm optimization (PSO), namely: classical PSO (C-PSO), time varying acceleration coefficients PSO (T-PSO) and constriction factor PSO (K-PSO). Further, utilizing pseudo-random binary sequence (PRBS)-based time domain reflectometry (TDR) method allows to obtain a reliable dataset for SVM classifier. The experimental results performed on a two-branch distribution line show the most optimal variant of PSO for short fault diagnosis.
Improving initial generations in pso algorithm for transportation network des...ijcsit
Transportation Network Design Problem (TNDP) aims to select the best project sets among a number of new projects. Recently, metaheuristic methods are applied to solve TNDP in the sense of finding better solutions sooner. PSO as a metaheuristic method is based on stochastic optimization and is a parallel revolutionary computation technique. The PSO system initializes with a number of random solutions and seeks for optimal solution by improving generations. This paper studies the behavior of PSO on account of improving initial generation and fitness value domain to find better solutions in comparison with previous attempts.
To mine out relevant facts at the time of need from web has been a tenuous task. Research on diverse fields are fine tuning methodologies toward these goals that extracts the best of information relevant to the users search query. In the proposed methodology discussed in this paper find ways to ease the search complexity tackling the severe issues hindering the performance of traditional approaches in use. The proposed methodology find effective means to find all possible semantic relatable frequent sets with FP Growth algorithm. The outcome of which is the further source of fuel for Bio inspired Fuzzy PSO to find the optimal attractive points for the web documents to get clustered meeting the requirement of the search query without losing the relevance. On the whole the proposed system optimizes the objective function of minimizing the intra cluster differences and maximizes the inter cluster distances along with retention of all possible relationships with the search context intact. The major contribution being the system finds all possible combinations matching the user search transaction and thereby making the system more meaningful. These relatable sets form the set of particles for Fuzzy Clustering as well as PSO and thus being unbiased and maintains a innate behaviour for any number of new additions to follow the herd behaviour’s evaluations reveals the proposed methodology fares well as an optimized and effective enhancements over the conventional approaches
Embellished Particle Swarm Optimization Algorithm for Solving Reactive Power ...ijeei-iaes
This paper proposes Embellished Particle Swarm Optimization (EPSO) algorithm for solving reactive power problem .The main concept of Embellished Particle Swarm Optimization is to extend the single population PSO to the interacting multi-swarm model. Through this multi-swarm cooperative approach, diversity in the whole swarm community can be upheld. Concurrently, the swarm-to-swarm mechanism drastically speeds up the swarm community to converge to the global near optimum. In order to evaluate the performance of the proposed algorithm, it has been tested in standard IEEE 57,118 bus systems and results show that Embellished Particle Swarm Optimization (EPSO) is more efficient in reducing the Real power losses when compared to other standard reported algorithms.
Using Machine Learning to Measure the Cross Section of Top Quark Pairs in the...m.a.kirn
Malina Kirn's 2011-09-06 University of Maryland Scientific Computation dissertation defense. Using neural networks and grid computing to measure top quark pair production cross section at the Compact Muon Solenoid detector at the Large Hadron Collider.
APPLICATION OF PARTICLE SWARM OPTIMIZATION TO MICROWAVE TAPERED MICROSTRIP LINEScseij
Application of metaheuristic algorithms has been of continued interest in the field of electrical engineering because of their powerful features. In this work special design is done for a tapered transmission line used for matching an arbitrary real load to a 50Ω line. The problem at hand is to match this arbitray load to 50 Ω line using three section tapered transmission line with impedances in decreasing order from the load. So the problem becomes optimizing an equation with three unknowns with various conditions. The optimized values are obtained using Particle Swarm Optimization. It can easily be shown that PSO is very strong in solving this kind of multiobjective optimization problems.
In our homes or offices, security has been a vital issue. Control of home security system remotely always offers huge advantages like the arming or disarming of the alarms, video monitoring, and energy management control apart from safeguarding the home free up intruders. Considering the oldest simple methods of security that is the mechanical lock system that has a key as the authentication element, then an upgrade to a universal type, and now unique codes for the lock. The recent advancement in the communication system has brought the tremendous application of communication gadgets into our various areas of life. This work is a real-time smart doorbell notification system for home Security as opposes of the traditional security methods, it is composed of the doorbell interfaced with GSM Module, a GSM module would be triggered to send an SMS to the house owner by pressing the doorbell, the owner will respond to the guest by pressing a button to open the door, otherwise, a message would be displayed to the guest for appropriate action. Then, the keypad is provided for an authorized person for the provision of password for door unlocking, if multiple wrong password attempts were made to unlock, a message of burglary attempt would be sent to the house owner for prompt action. The main benefit of this system is the uniqueness of the incorporation of the password and messaging systems which denies access to any unauthorized personality and owner's awareness method.
Augmented reality, the new age technology, has widespread applications in every field imaginable. This technology has proven to be an inflection point in numerous verticals, improving lives and improving performance. In this paper, we explore the various possible applications of Augmented Reality (AR) in the field of Medicine. The objective of using AR in medicine or generally in any field is the fact that, AR helps in motivating the user, making sessions interactive and assist in faster learning. In this paper, we discuss about the applicability of AR in the field of medical diagnosis. Augmented reality technology reinforces remote collaboration, allowing doctors to diagnose patients from a different locality. Additionally, we believe that a much more pronounced effect can be achieved by bringing together the cutting edge technology of AR and the lifesaving field of Medical sciences. AR is a mechanism that could be applied in the learning process too. Similarly, virtual reality could be used in the field where more of practical experience is needed such as driving, sports, neonatal care training.
Image fusion is a sub field of image processing in which more than one images are fused to create an image where all the objects are in focus. The process of image fusion is performed for multi-sensor and multi-focus images of the same scene. Multi-sensor images of the same scene are captured by different sensors whereas multi-focus images are captured by the same sensor. In multi-focus images, the objects in the scene which are closer to the camera are in focus and the farther objects get blurred. Contrary to it, when the farther objects are focused then closer objects get blurred in the image. To achieve an image where all the objects are in focus, the process of images fusion is performed either in spatial domain or in transformed domain. In recent times, the applications of image processing have grown immensely. Usually due to limited depth of field of optical lenses especially with greater focal length, it becomes impossible to obtain an image where all the objects are in focus. Thus, it plays an important role to perform other tasks of image processing such as image segmentation, edge detection, stereo matching and image enhancement. Hence, a novel feature-level multi-focus image fusion technique has been proposed which fuses multi-focus images. Thus, the results of extensive experimentation performed to highlight the efficiency and utility of the proposed technique is presented. The proposed work further explores comparison between fuzzy based image fusion and neuro fuzzy fusion technique along with quality evaluation indices.
Graphs have become the dominant life-form of many tasks as they advance a
structure to represent many tasks and the corresponding relations. A powerful
role of networks/graphs is to bridge local feats that exist in vertices as they
blossom into patterns that help explain how nodal relations and their edges
impacts a complex effect that ripple via a graph. User cluster are formed as a
result of interactions between entities. Many users can hardly categorize their
contact into groups today such as “family”, “friends”, “colleagues” etc. Thus,
the need to analyze such user social graph via implicit clusters, enables the
dynamism in contact management. Study seeks to implement this dynamism
via a comparative study of deep neural network and friend suggest algorithm.
We analyze a user’s implicit social graph and seek to automatically create
custom contact groups using metrics that classify such contacts based on a
user’s affinity to contacts. Experimental results demonstrate the importance
of both the implicit group relationships and the interaction-based affinity in
suggesting friends.
In the wake of the sudden replacement of wood and kerosene by gas cookers for several purposes in Nigeria, gas leakage has caused several damages in our homes, Laboratories among others. installation of a gas leakage detection device was globally inspired to eliminate accidents related to gas leakage. We present an alternative approach to developing a device that can automatically detect and control gas leakages and also monitor temperature. The system detects the leakage of the LPG (Liquefied Petroleum Gas) using a gas sensor, then triggred the control system response which employs ventilator system, Mobile phone alert and alarm when the LPG concentration in the air exceeds a certain level. The performance of two gas sensors (MQ5 and MQ6) were tested for a guided decision. Also, when the temperature of the environment poses a danger, LED (indicator), buzzer and LCD (16x2) display was used to indicate temperature and gas leakage status in degree Celsius and PPM respectively. Attension was given to the response time of the control system, which was ascertained that this system significantly increases the chances and efficiency of eliminating gas leakage related accident.
Feature selection problem is one of the main important problems in the text and data mining domain. This paper presents a comparative study of feature selection methods for Arabic text classification. Five of the feature selection methods were selected: ICHI square, CHI square, Information Gain, Mutual Information and Wrapper. It was tested with five classification algorithms: Bayes Net, Naive Bayes, Random Forest, Decision Tree and Artificial Neural Networks. In addition, Data Collection was used in Arabic consisting of 9055 documents, which were compared by four criteria: Precision, Recall, F-measure and Time to build model. The results showed that the improved ICHI feature selection got almost all the best results in comparison with other methods.
In this paper Gentoo Penguin Algorithm (GPA) is proposed to solve optimal reactive power problem. Gentoo Penguins preliminary population possesses heat radiation and magnetizes each other by absorption coefficient. Gentoo Penguins will move towards further penguins which possesses low cost (elevated heat concentration) of absorption. Cost is defined by the heat concentration, distance. Gentoo Penguins penguin attraction value is calculated by the amount of heat prevailed between two Gentoo penguins. Gentoo Penguins heat radiation is measured as linear. Less heat is received in longer distance, in little distance, huge heat is received. Gentoo Penguin Algorithm has been tested in standard IEEE 57 bus test system and simulation results show the projected algorithm reduced the real power loss considerably.
08 20272 academic insight on applicationIAESIJEECS
This research has thrown up many questions in need of further investigation.There was an expressive quantitative-qualitative research, which a common investigation form was used in.The dialogue item was also applied to discover if the contributors asserted the media-based attitude supplements their learning of academic English writing classes or not.Data recounted academic” insights toward using Skype as a sustaining implement for lessons releasing based on chosen variables: their occupation, year of education, and knowledge with Skype discovered that there were no important statistical differences in the use of Skype units because of medical academics major knowledge. There are statistically important differences in using Skype units. The findings also, disclosed that there are statistically significant differences in using Skype units due to the practice with Skype variable, in favors of academics with no Skype use practice. Skype instrument as an instructive media is a positive medium to be employed to supply academic medical writing data and assist education. Academics who do not have enough time to contribute in classes believe comfortable using the Skype-based attitude in scientific writing. They who took part in the course claimed that their approval of this media is due to learning academic innovative medical writing.
Cloud computing has sweeping impact on the human productivity. Today it’s used for Computing, Storage, Predictions and Intelligent Decision Making, among others. Intelligent Decision-Making using Machine Learning has pushed for the Cloud Services to be even more fast, robust and accurate. Security remains one of the major concerns which affect the cloud computing growth however there exist various research challenges in cloud computing adoption such as lack of well managed service level agreement (SLA), frequent disconnections, resource scarcity, interoperability, privacy, and reliability. Tremendous amount of work still needs to be done to explore the security challenges arising due to widespread usage of cloud deployment using Containers. We also discuss Impact of Cloud Computing and Cloud Standards. Hence in this research paper, a detailed survey of cloud computing, concepts, architectural principles, key services, and implementation, design and deployment challenges of cloud computing are discussed in detail and important future research directions in the era of Machine Learning and Data Science have been identified.
Notary is an official authorized to make an authentic deed regarding all deeds, agreements and stipulations required by a general rule. Activities carried out at the notary office such as recording client data and file data still use traditional systems that tend to be manual. The problem that occurs is the inefficiency in data processing and providing information to clients. Clients have difficulty getting information related to the progress of documents that are being taken care of at the notary's office. The client must take the time to arrive to the notary's office repeatedly to check the progress of the work of the document file. The purpose of this study is to facilitate clients in obtaining information about the progress of the work in progress, and make it easier for employees to process incoming documents by implementing an administrative system. This system was developed with the waterfall system development method and uses the Multi-Channel Access Technology integrated in the website to simplify the process of delivering information and requesting information from clients and to clients with Telegram and SMS Gateway. Clients will come to the office only when there is a notification from the system via Telegram or SMS notifying that the client must come directly to the notary's office, thus leading to an efficient time and avoiding excessive transportation costs. The overall functional system can function properly based on the results of alpha testing. The results of beta testing conducted by distributing the system feasibility test questionnaire to end users, get a percentage of 96% of users agree the system is feasible to be implemented.
In this work Predestination of Particles Wavering Search (PPS) algorithm has been applied to solve optimal reactive power problem. PPS algorithm has been modeled based on the motion of the particles in the exploration space. Normally the movement of the particle is based on gradient and swarming motion. Particles are permitted to progress in steady velocity in gradient-based progress, but when the outcome is poor when compared to previous upshot, immediately particle rapidity will be upturned with semi of the magnitude and it will help to reach local optimal solution and it is expressed as wavering movement. In standard IEEE 14, 30, 57,118,300 bus systems Proposed Predestination of Particles Wavering Search (PPS) algorithm is evaluated and simulation results show the PPS reduced the power loss efficiently.
In this paper, Mine Blast Algorithm (MBA) has been intermingled with Harmony Search (HS) algorithm for solving optimal reactive power dispatch problem. MBA is based on explosion of landmines and HS is based on Creativeness progression of musicians-both are hybridized to solve the problem. In MBA Initial distance of shrapnel pieces are reduced gradually to allow the mine bombs search the probable global minimum location in order to amplify the global explore capability. Harmony search (HS) imitates the music creativity process where the musicians supervise their instruments’ pitch by searching for a best state of harmony. Hybridization of Mine Blast Algorithm with Harmony Search algorithm (MH) improves the search effectively in the solution space. Mine blast algorithm improves the exploration and harmony search algorithm augments the exploitation. At first the proposed algorithm starts with exploration & gradually it moves to the phase of exploitation. Proposed Hybridized Mine Blast Algorithm with Harmony Search algorithm (MH) has been tested on standard IEEE 14, 300 bus test systems. Real power loss has been reduced considerably by the proposed algorithm. Then Hybridized Mine Blast Algorithm with Harmony Search algorithm (MH) tested in IEEE 30, bus system (with considering voltage stability index)- real power loss minimization, voltage deviation minimization, and voltage stability index enhancement has been attained.
Artificial Neural Networks have proved their efficiency in a large number of research domains. In this paper, we have applied Artificial Neural Networks on Arabic text to prove correct language modeling, text generation, and missing text prediction. In one hand, we have adapted Recurrent Neural Networks architectures to model Arabic language in order to generate correct Arabic sequences. In the other hand, Convolutional Neural Networks have been parameterized, basing on some specific features of Arabic, to predict missing text in Arabic documents. We have demonstrated the power of our adapted models in generating and predicting correct Arabic text comparing to the standard model. The model had been trained and tested on known free Arabic datasets. Results have been promising with sufficient accuracy.
In the present-day communications speech signals get contaminated due to
various sorts of noises that degrade the speech quality and adversely impacts
speech recognition performance. To overcome these issues, a novel approach
for speech enhancement using Modified Wiener filtering is developed and
power spectrum computation is applied for degraded signal to obtain the
noise characteristics from a noisy spectrum. In next phase, MMSE technique
is applied where Gaussian distribution of each signal i.e. original and noisy
signal is analyzed. The Gaussian distribution provides spectrum estimation
and spectral coefficient parameters which can be used for probabilistic model
formulation. Moreover, a-priori-SNR computation is also incorporated for
coefficient updation and noise presence estimation which operates similar to
the conventional VAD. However, conventional VAD scheme is based on the
hard threshold which is not capable to derive satisfactory performance and a
soft-decision based threshold is developed for improving the performance of
speech enhancement. An extensive simulation study is carried out using
MATLAB simulation tool on NOIZEUS speech database and a comparative
study is presented where proposed approach is proved better in comparison
with existing technique.
Previous research work has highlighted that neuro-signals of Alzheimer’s disease patients are least complex and have low synchronization as compared to that of healthy and normal subjects. The changes in EEG signals of Alzheimer’s subjects start at early stage but are not clinically observed and detected. To detect these abnormalities, three synchrony measures and wavelet-based features have been computed and studied on experimental database. After computing these synchrony measures and wavelet features, it is observed that Phase Synchrony and Coherence based features are able to distinguish between Alzheimer’s disease patients and healthy subjects. Support Vector Machine classifier is used for classification giving 94% accuracy on experimental database used. Combining, these synchrony features and other such relevant features can yield a reliable system for diagnosing the Alzheimer’s disease.
Attenuation correction designed for PET/MR hybrid imaging frameworks along with portion making arrangements used for MR-based radiation treatment remain testing because of lacking high-energy photon weakening data. We present a new method so as to uses the learned nonlinear neighborhood descriptors also highlight coordinating toward foresee pseudo-CT pictures starting T1w along with T2w MRI information. The nonlinear neighborhood descriptors are acquired through anticipating the direct descriptors interested in the nonlinear high-dimensional space utilizing an unequivocal constituent guide also low-position guess through regulated complex regularization. The nearby neighbors of every near descriptor inside the data MR pictures are looked during an obliged spatial extent of the MR pictures among the training dataset. By that point, the pseudo-CT patches are evaluated through k-closest neighbor relapse. The planned procedure designed for pseudo-CT forecast is quantitatively broke downward on top of a dataset comprising of coordinated mind MRI along with CT pictures on or after 13 subjects.
The cognitive radio prototype performance is to alleviate the scarcity of spectral resources for wireless communication through intelligent sensing and quick resource allocation techniques. Secondary users (SU’s) actively obtain the spectrum access opportunity by supporting primary users (PU’s) in cognitive radio networks (CRNs). In present generation, spectrum access is endowed through cooperative communication-based link-level frame-based cooperative (LLC) principle. In this SUs independently act as conveyors for PUs to achieve spectrum access opportunities. Unfortunately, this LLC approach cannot fully exploit spectrum access opportunities to enhance the throughput of CRNs and fails to motivate PUs to join the spectrum sharing processes. Therefore, to overcome this con, network level cooperative (NLC) principle was used, where SUs are integrated mutually to collaborate with PUs session by session, instead of frame based cooperation for spectrum access opportunities. NLC approach has justified the challenges facing in LLC approach. In this paper we make a survey of some models that have been proposed to tackle the problem of LLC. We show the relevant aspects of each model, in order to characterize the parameters that we should take in account to achieve a spectrum access opportunity.
In this paper, the author provides insights and lessons that can be learned from colleagues at American universities about their online education experiences. The literature review and previous studies of online educations gains are explored and summarized in this research. Emerging trends in online education are discussed in detail, and strategies to implement these trends are explained. The author provides several tools and strategies that enable universities to ensure the quality of online education. At the end of this research paper, the researcher provides examples from Arab universities who have successfully implemented online education and expanded their impact on the society. This research provides a strategy and a model that can be used by universities in the Middle East as a roadmap to implement online education in their regions.
In this paper, a low pass filter based on T-Shaped resonator is presented. The T-Shaped resonator consists of meandered lines and rectangular patches. Also, the LC model and transfer function of the proposed resonator is presented. For suppression of spurious harmonics, a bandstop structure consists of hexangular patches and open stubs has been utilized. Finally, the wide stopband microstrip lowpass filter with cutoff frequency 2.72 GHz has been simulated, fabricated and measured. The LPF has good characteristics such as wide stopband and insertion loss lower than 0.18 dB in the passband region. The rejection level is less than -20 dB from 2.98 up to 21.3 GHz. The filter size is 10.5 mm×12.7 mm, or 0.131 λg× 0.158 λg, where λg is the guided wavelength. The measured and simulated results of the filter is in good agreement with each other, which show the merits of low insertion loss and wide stopband.
Today’s popularity of the short messages services (SMS) has created a propitious environment for spamming to thrive. Spams are unsolicited advertising, adult-themed or inappropriate content, premium fraud, smishing and malware. They are a constant reminder of the need for an effective spam filter. However, SMS limitations of 160-charcaters and 140-bytes size as well as its being rippled with slangs, emoticons and abbreviations further inhibits effective training of models to aid accurate classification. The study proposes Genetic Algorithm Trained Bayesian Network solution that seeks to normalize noisy feats, expand text via use of lexicographic and semantic dictionaries that uses word sense disambiguation technique to train the underlying learning heuristics. And in turn, effectively help to classify SMS in spam and legitimate classes. Hybrid model comprises of text preprocessing, feature selection as well as training and classification section. Study uses a hybrid Genetic Algorithm trained Bayesian model for which the GA is used for feature selection; while, the Bayesian algorithm is used as classifier.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 6
01 20257 ijict
1. International Journal of Informatics and Communication Technology (IJ-ICT)
Vol.9, No.1, April 2020, pp. 1~8
ISSN: 2252-8776, DOI: 10.11591/ijict.v9i1.pp1-8 1
Journal homepage: http://ijict.iaescore.com
Enhanced wormhole optimizer algorithm for solving optimal
reactive power problem
Kanagasabai Lenin
Department of EEE, Prasad V. Potluri Siddhartha Institute of Technology, India
Article Info ABSTRACT
Article history:
Received Nov 11, 2019
Revised Nov 13, 2019
Accepted Dec 27, 2019
In this paper Enhanced Wormhole Optimizer (EWO) algorithm is used to
solve optimal reactive power problem. Proposed algorithm based on the
Wormholes which exploits the exploration space. Between different
universes objects are exchanged through white or black hole tunnels.
Regardless of the inflation rate, through wormholes objects in all universes
which possess high probability will shift to the most excellent universe. In
the projected Enhanced Wormhole Optimizer (EWO) algorithm in order to
avoid the solution to be get trapped into the local optimal solution Levy flight
has been applied. Projected Enhanced Wormhole Optimizer (EWO)
algorithm has been tested in standard IEEE 14, 30, 57,118,300 bus test
systems and simulation results show that the EWO algorithm reduced the real
power loss efficiently.
Keywords:
Enhanced wormhole optimizer
Optimal reactive power
Transmission loss
This is an open access article under the CC BY-SA license.
Corresponding Author:
Kanagasabai Lenin,
Department of EEE,
Prasad V. Potluri Siddhartha, Institute of Technology,
Kanuru, Vijayawada, Andhra Pradesh -520007, India.
Email: gklenin@gmail.com
1. INTRODUCTION
For secure and economic operations of power system optimal reactive power problem plays vital
role. Several types of techniques [1-6] have been utilized to solve the problem previously. Conversely many
difficulties are found while solving problem due to inequality constraints. Evolutionary techniques [7-15] are
applied to solve the reactive power problem. This paper proposes Enhanced Wormhole Optimizer (EWO)
algorithm for solving optimal reactive power problem. Wormhole Optimizer Algorithm is based on the
Wormholes which exploit the exploration space. Wormhole tunnel are built for local change in each universe
m through most excellent universe then probability of refinement the inflation rate is done through
wormholes. Objects are exchanged through tunnels and wormholes objects which possess high probability
will shift to the most excellent universe. In the projected Enhanced Wormhole Optimizer (EWO) algorithm in
order to avoid the solution to be get trapped into the local optimal solution Levy flight has been applied.
Projected Enhanced Wormhole Optimizer (EWO) algorithm has been tested in standard IEEE 14, 30,
57,118,300 bus test systems and simulation results show that the projected algorithm reduced the real power
loss effectively.
2. PROBLEM FORMULATION
Objective of the problem is to reduce the true power loss:
F = PL = ∑ gkk∈Nbr (Vi
2
+ Vj
2
− 2ViVjcosθij) (1)
2. ISSN: 2252-8776
Int J Inf & Commun Technol, Vol. 9, No. 1, April 2020: 1 – 8
2
Voltage deviation given as follows:
F = PL + ωv × Voltage Deviation (2)
Voltage Deviation = ∑ |Vi − 1|
Npq
i=1 (3)
Constraint (Equality)
PG = PD + PL (4)
Constraints (Inequality)
Pgslack
min
≤ Pgslack ≤ Pgslack
max
(5)
Qgi
min
≤ Qgi ≤ Qgi
max
, i ∈ Ng (6)
Vi
min
≤ Vi ≤ Vi
max
, i ∈ N (7)
Ti
min
≤ Ti ≤ Ti
max
, i ∈ NT (8)
Qc
min
≤ Qc ≤ QC
max
, i ∈ NC (9)
3. Enhanced Wormhole Optimizer Algorithm
Wormhole Optimizer Algorithm is based on the Wormholes which exploit the exploration space.
Through wormholes objects which has high probability will shift to the most excellent universe and it
modeled by using roulette wheel selection methodology as follows,
𝑈 = [
𝑦11 ⋯ 𝑦1𝑑
⋮ ⋱ ⋮
𝑦 𝑛1 ⋯ 𝑦 𝑛𝑑
] (10)
Number of the variables is indicated by “d” and number of universe which is considered as
candidate solution is indicated by”n”.
𝑦𝑖𝑗 = {
𝑦 𝑘𝑗 𝑟𝑎𝑛𝑑𝑜𝑚1 < 𝑁𝐼(𝑈𝑖)
𝑦𝑖𝑗 𝑟𝑎𝑛𝑑𝑜𝑚1 < 𝑁𝐼(𝑈𝑖)
(11)
Through roulette wheel selection 𝑦𝑖𝑗 ′𝑠 “j”th parameter of the “k”th universe will be chosen, in the
“i”th universe “j”th parameter is expressed by 𝑦 𝑘𝑗 , ith universe inflation rate indicated by 𝑁𝐼(𝑈𝑖), ith
universe indicated by 𝑈𝑖 , 𝑟𝑎𝑛𝑑𝑜𝑚1 ∈ [0,1].
In between two universes wormhole tunnel [16 ,17] are built then the local change for each universe
is done by most excellent universe and the elevated probability of refinement the inflation rate through
wormholes is done by,
𝑦𝑖𝑗 =
{
{
𝑌𝑗 + 𝑇𝑟. 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑟𝑎𝑡𝑒 × ((𝑢𝑏𝑗 − 𝑙𝑏𝑗) × 𝑟𝑎𝑛𝑑4 + 𝑙𝑏𝑗) 𝑟𝑎𝑛𝑑3 < 0.5
𝑌𝑗 − 𝑇𝑟. 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑟𝑎𝑡𝑒 × ((𝑢𝑏𝑗 − 𝑙𝑏𝑗) × 𝑟𝑎𝑛𝑑4 + 𝑙𝑏𝑗) 𝑟𝑎𝑛𝑑3 ≥ 0.5
𝑦𝑖𝑗 𝑟𝑎𝑛𝑑2 ≥ 𝑤 𝑒 𝑝
𝑟𝑎𝑛𝑑2 < 𝑤 𝑒 𝑝 (12)
Wormhole existence probability indicated by “w e p”, “tr.” Indicates the travelling and random
denoted by “ rand”.
During the optimization procedure exploitation has been enhanced as follows,
Wormhole existence probability = wminimum+current iteration (
wmaximum−wminimum
maximum iteration
) (13)
3. Int J Inf & Commun Technol ISSN: 2252-8776
Enhanced wormhole optimizer algorithm for solving optimal reactive … (Kanagasabai Lenin)
3
In order to improve the local search precisely travelling distance rate will be increased over the
iterations as follows,
𝑇𝑟𝑎𝑣𝑒𝑙𝑙𝑖𝑛𝑔 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑟𝑎𝑡𝑒 = 1 −
current iteration1 𝑝⁄
maximum iteration1 𝑝⁄ (14)
In the projected Enhanced Wormhole Optimizer (EWO) algorithm in order to avoid the solution to
be get trapped into the local optimal solution Levy flight has been applied.
Levy flight is a rank of non-Gaussian random procedure whose capricious walks are haggard from Levy
stable distribution. Allocation by L(s) ~ |s|-1-β
where 0 < ß < 2 is an index. Scientifically defined as,
𝐿(𝑠, 𝛾, 𝜇) = {
√
𝛾
2𝜋
0 𝑖𝑓 𝑠 ≤ 0
𝑒𝑥𝑝 [−
𝛾
2(𝑠−𝜇)
]
1
(𝑠−𝜇)3 2⁄ 𝑖𝑓 0 < 𝜇 < 𝑠 < ∞ (15)
In terms of Fourier transform Levy distribution defined as
𝐹(𝑘) = 𝑒𝑥𝑝[−𝛼|𝑘| 𝛽
], 0 < 𝛽 ≤ 2, (16)
Fresh state is calculated as,
𝑌 𝑡+1
= 𝑌 𝑡
+ 𝛼 ⊕ 𝐿𝑒𝑣𝑦 (𝛽) (17)
𝑌 𝑡+1
= 𝑦 𝑡
+ 𝑟𝑎𝑛𝑑𝑜𝑚 (𝑠𝑖𝑧𝑒(𝐷)) ⊕ 𝐿𝑒𝑣𝑦(𝛽) (18)
In the projected Enhanced Wormhole Optimizer (EWO) algorithm while generation of new
solutions 𝑈𝑖
𝑡+1
levy flight (y) will be applied,
𝑈𝑖
𝑡+1
= 𝑈𝑖
𝑡
+ 𝐾(𝑙𝑏 + (𝑢𝑏 − 𝑙𝑏) ∗ 𝑙𝑒𝑣𝑦(𝑦)) × 𝑈𝑖
𝑡
(19)
Levy flight will be applied in the adaptive mode to balance the exploration and exploitation by
applying large levy weight initially and final course the weight of the levy will be decreased,
𝐾 = (
𝑀𝑎𝑥𝑖𝑚𝑢𝑚 𝑖𝑡𝑒𝑟𝑎𝑡𝑖𝑜𝑛−𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑖𝑡𝑒𝑟𝑎𝑡𝑖𝑜𝑛
𝑚𝑎𝑥𝑖𝑚𝑢𝑚 𝑖𝑡𝑒𝑟𝑎𝑡𝑖𝑜𝑛
) (20)
By using Mantegna's algorithm Non-trivial scheme of engendering step size by,
𝑠 =
𝑢
|𝑣|
1
𝛽
(21)
𝑌 𝑡+1
= 𝑌 𝑡
+ 𝑟𝑎𝑛𝑑𝑜𝑚 (𝑠𝑖𝑧𝑒(𝐷)) ⊕ 𝐿𝑒𝑣𝑦(𝛽)~0.01
𝑢
|𝑣|1 𝛽⁄ (𝑦𝑗
𝑡
− 𝑔𝑏) (22)
𝑢~𝑁(0, 𝜎 𝑢
2) 𝑣~𝑁(0, 𝜎𝑣
2) (23)
with
𝜎 𝑢 = {
Г(1+𝛽)𝑠𝑖𝑛(𝜋𝛽/2)
Г[(1+𝛽)/2]𝛽2(𝛽−1)/2}
1
𝛽⁄
, 𝜎𝑣 = 1 (24)
then,
𝐿𝑒𝑣𝑦(𝑦) = 0.01 ×
𝑢×𝜎
|𝑣|
1
𝛽
(25)
Start
In put ; “d” & “n” ; Lower bound = [Lb1,Lb2,. . .,Lbd] ; Upper bound = [Ub1,Ub2,. . .,Ubd] ; Maximum
number of iterations
Output: Optimal solution
Step a: Initialization of parameters
4. ISSN: 2252-8776
Int J Inf & Commun Technol, Vol. 9, No. 1, April 2020: 1 – 8
4
Engender arbitrary universes “U” by 𝑈𝑃 = {𝑈1, 𝑈2, . . 𝑈𝑖, . . , 𝑈 𝑛}
Initialize Wormhole existence probability, travelling distance rate, objective function
t = 0
Step b: categorization and reorganize; arrange the universes; universe inflation rate (UI) will be
reorganized
Step c: Iteration; while t < Maximum iteration
Compute universe inflation rate; UI (𝑈𝑖
𝑡
) ; i = 1,2,. . .,n
For every universe “Ui”; modernize Wormhole existence probability, travelling distance rate by
Wormhole existence probability = wminimum+current iteration (
wmaximum−wminimum
maximum iteration
)
𝑇𝑟𝑎𝑣𝑒𝑙𝑙𝑖𝑛𝑔 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑟𝑎𝑡𝑒 = 1 −
current iteration1 𝑝⁄
maximum iteration1 𝑝⁄ ; Black hole index value = i
Modernize the value “U” by 𝑈𝑖
𝑡+1
= 𝑈𝑖
𝑡
+ 𝐾(𝑙𝑏 + (𝑢𝑏 − 𝑙𝑏) ∗ 𝑙𝑒𝑣𝑦(𝑦)) × 𝑈𝑖
𝑡
For every object 𝑦𝑖𝑗 ; 𝑟𝑎𝑛𝑑𝑜𝑚1 = random (0,1);
If 𝑟𝑎𝑛𝑑𝑜𝑚1 < UI(Ui) ; white hole index = roulette wheel selection (-UI);
U (black hole index ,j) = SU(white hole index ,j);
End if
𝑟𝑎𝑛𝑑𝑜𝑚2= random (0,1);
If 𝑟𝑎𝑛𝑑𝑜𝑚2 < Wormhole existence probability
𝑟𝑎𝑛𝑑𝑜𝑚3= random (0,1); 𝑟𝑎𝑛𝑑𝑜𝑚4 = random (0,1);
If 𝑟𝑎𝑛𝑑𝑜𝑚3 < 0.5
𝑦𝑖𝑗 = 𝑜𝑝𝑡𝑖𝑚𝑎𝑙 𝑠𝑜𝑙𝑢𝑡𝑖𝑜𝑛 (𝑗) + 𝑇𝑟𝑎𝑣𝑒𝑙𝑙𝑖𝑛𝑔 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑟𝑎𝑡𝑒 ∗ ((𝑢𝑏(𝑗) − 𝑙𝑏(𝑗)) ∗ 𝑟𝑎𝑛𝑑𝑜𝑚4 + 𝑙𝑏(𝑗))
Or else
𝑦𝑖𝑗 = 𝑜𝑝𝑡𝑖𝑚𝑎𝑙 𝑠𝑜𝑙𝑢𝑡𝑖𝑜𝑛 (𝑗) − 𝑇𝑟𝑎𝑣𝑒𝑙𝑙𝑖𝑛𝑔 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑟𝑎𝑡𝑒 ∗ ((𝑢𝑏(𝑗) − 𝑙𝑏(𝑗)) ∗ 𝑟𝑎𝑛𝑑𝑜𝑚4 + 𝑙𝑏(𝑗))
End if
End for
t = t+1
End while
Step d: End; output the optimal solution
4. SIMULATION RESULTS
At first in standard IEEE 14 bus system [18] the validity of the proposed Enhanced Wormhole
Optimizer (EWO) algorithm has been tested, Table 1 shows the constraints of control variables Table 2
shows the limits of reactive power generators and comparison results are presented in Table 3.
Table 1. Constraints of control variables
System Variables Minimum
(PU)
Maximum
(PU)
IEEE 14
Bus
Generator
Voltage
0.95 1.1
Transformer
Tap
o.9 1.1
VAR Source 0 0.20
Table 2. Constrains of reactive power generators
System Variables Q Minimum
(PU)
Q Maximum
(PU)
IEEE 14
Bus
1 0 10
2 -40 50
3 0 40
6 -6 24
8 -6 24
Table 3. Simulation results of IEEE −14 system
Control variables Base case MPSO [19] PSO [19] EP [19] SARGA [19] EWO
𝑉𝐺−1 1.060 1.100 1.100 NR* NR* 1.013
𝑉𝐺−2 1.045 1.085 1.086 1.029 1.060 1.014
𝑉𝐺−3 1.010 1.055 1.056 1.016 1.036 1.002
𝑉𝐺−6 1.070 1.069 1.067 1.097 1.099 1.017
𝑉𝐺−8 1.090 1.074 1.060 1.053 1.078 1.021
𝑇𝑎𝑝 8 0.978 1.018 1.019 1.04 0.95 0.910
𝑇𝑎𝑝 9 0.969 0.975 0.988 0.94 0.95 0.913
𝑇𝑎𝑝 10 0.932 1.024 1.008 1.03 0.96 0.927
𝑄𝐶−9 0.19 14.64 0.185 0.18 0.06 0.120
𝑃𝐺 272.39 271.32 271.32 NR* NR* 271.78
𝑄𝐺 (Mvar) 82.44 75.79 76.79 NR* NR* 75.79
Reduction in PLoss (%) 0 9.2 9.1 1.5 2.5 25.85
Total PLoss (Mw) 13.550 12.293 12.315 13.346 13.216 10.047
NR* - Not reported.
5. Int J Inf & Commun Technol ISSN: 2252-8776
Enhanced wormhole optimizer algorithm for solving optimal reactive … (Kanagasabai Lenin)
5
Then Enhanced Wormhole Optimizer (EWO) algorithm has been tested, in IEEE 30 Bus system.
Table 4 shows the constraints of control variables, Table 5 shows the limits of reactive power generators and
comparison results are presented in Table 6.
Table 4. Constraints of control variables
System Variables
Minimum
(PU)
Maximum
(PU)
IEEE 30 Bus Generator
Voltage
0.95 1.1
Transformer
Tap
o.9 1.1
VAR Source 0 0.20
Table 5. Constrains of reactive power generators
System Variables
Q Minimum
(PU)
Q Maximum
(PU)
IEEE 30 Bus 1 0 10
2 -40 50
5 -40 40
8 -10 40
11 -6 24
13 -6 24
Table 6. Simulation results of IEEE −30 system
Control variables Base case MPSO [19] PSO [19] EP [19] SARGA [19] EWO
VG −1 1.060 1.101 1.100 NR* NR* 1.013
VG −2 1.045 1.086 1.072 1.097 1.094 1.014
VG −5 1.010 1.047 1.038 1.049 1.053 1.010
VG −8 1.010 1.057 1.048 1.033 1.059 1.021
VG −12 1.082 1.048 1.058 1.092 1.099 1.032
VG-13 1.071 1.068 1.080 1.091 1.099 1.024
Tap11 0.978 0.983 0.987 1.01 0.99 0.934
Tap12 0.969 1.023 1.015 1.03 1.03 0.930
Tap15 0.932 1.020 1.020 1.07 0.98 0.921
Tap36 0.968 0.988 1.012 0.99 0.96 0.923
QC10 0.19 0.077 0.077 0.19 0.19 0.092
QC24 0.043 0.119 0.128 0.04 0.04 0.124
𝑃𝐺 (MW) 300.9 299.54 299.54 NR* NR* 297.68
𝑄𝐺 (Mvar) 133.9 130.83 130.94 NR* NR* 131.41
Reduction in PLoss (%) 0 8.4 7.4 6.6 8.3 19.37
Total PLoss (Mw) 17.55 16.07 16.25 16.38 16.09 14.149
Then the proposed Enhanced Wormhole Optimizer (EWO) algorithm has been tested, in IEEE 57
Bus system. Table 7 shows the constraints of control variables, Table 8 shows the limits of reactive power
generators and comparison results are presented in Table 9.
Table 7. Constraints of control variables
System Variables Minimum (PU) Maximum (PU)
IEEE 57 Bus Generator Voltage 0.95 1.1
Transformer Tap o.9 1.1
VAR Source 0 0.20
Table 8. Constrains of reactive power generators
System Variables Q Minimum (PU) Q Maximum (PU)
IEEE 57 Bus 1 -140 200
2 -17 50
3 -10 60
6 -8 25
8 -140 200
9 -3 9
12 -150 155
8. ISSN: 2252-8776
Int J Inf & Commun Technol, Vol. 9, No. 1, April 2020: 1 – 8
8
5. CONCLUSION
In this paper proposed Enhanced Wormhole Optimizer (EWO) algorithm successfully solved the
optimal reactive power problems. Between different universes objects are exchanged through white or black
hole tunnels. Regardless of the inflation rate, through wormholes objects in all universes which possess high
probability will shift to the most excellent universe. In between two universes wormhole tunnel are built then
the local change for each universe is done by most excellent universe and the elevated probability of
refinement the inflation rate through wormholes. Levy flight has been applied effectively and it leads to the
improvement of the quality of solution. Proposed Enhanced Wormhole Optimizer (EWO) algorithm has
been tested in standard IEEE 14, 30, 57,118,300 bus test systems and simulation results show that the EWO
algorithm reduced the real power loss efficiently. Percentage of real power loss reduction has been enhanced
when compared to other standard algorithms.
REFERENCES
[1] K. Y. Lee, “Fuel-cost minimisation for both real and reactive-power dispatches,” Proceedings Generation,
Transmission and Distribution Conference, vol. 131, no. 3, pp. 85-93, 1984.
[2] N. I. Deeb, “An efficient technique for reactive power dispatch using a revised linear programming approach,”
Electric Power System Research, vol. 15, no. 2, pp. 121–134, 1998.
[3] M. R. Bjelogrlic, M. S. Calovic, B. S. Babic, “Application of Newton’s optimal power flow in voltage/reactive
power control,” IEEE Trans Power System, vol. 5, no. 4, pp. 1447-1454, 1990.
[4] S. Granville, “Optimal reactive dispatch through interior point methods,” IEEE Transactions on Power System, vol.
9, no. 1, pp. 136–146, 1994.
[5] N. Grudinin, “Reactive power optimization using successive quadratic programming method,” IEEE Transactions
on Power System, vol. 13, no. 4, pp. 1219–1225, 1998.
[6] Ng Shin Mei, R., Sulaiman, M.H., Mustaffa, Z., Daniyal, H., “Optimal reactive power dispatch solution by loss
minimization using moth-flame optimization technique,” Appl. Soft Comput., vol. 59, pp. 210–222, 2017.
[7] Chen, G., Liu, L., Zhang, Z., Huang, S., “Optimal reactive power dispatch by improved GSA-based algorithm with
the novel strategies to handle constraints,” Appl. Soft Comput., vol. 50, pp. 58–70, 2017.
[8] Roy, Provas Kumar and Susanta Dutt, “Economic load dispatch: Optimal power flow and optimal reactive power
dispatch concept,” Optimal Power Flow Using Evolutionary Algorithms, IGI Global, pp. 46-64, 2019.
[9] Christian Bingane, Miguel F. Anjos, Sébastien Le Digabel, “Tight-and-cheap conic relaxation for the optimal
reactive power dispatch problem,” IEEE Transactions on Power Systems, vol. 34, no. 6, 2019.
[10] Dharmbir Prasad & Vivekananda Mukherjee, “Solution of Optimal Reactive Power Dispatch by Symbiotic
Organism Search Algorithm Incorporating FACTS Devices,” IETE Journal of Research, vol. 64, no. 1,
pp. 149-160, 2018.
[11] TM Aljohani, et al, “Single and multiobjective optimal reactive power dispatch based on hybrid artificial physics–
particle swarm optimization,” Energies, vol. 12, no. 12, p. 2333, 2019
[12] Ram Kishan Mahate, & Himmat Singh, “Multi-Objective Optimal Reactive Power Dispatch Using Differential
Evolution,” International Journal of Engineering Technologies and Management Research, vol. 6, no. 2,
pp. 27–38, 2019.
[13] Yalçın, E, Taplamacıoğlu, M., Çam, E., “The Adaptive Chaotic Symbiotic Organisms Search Algorithm Proposal
for Optimal Reactive Power Dispatch Problem in Power Systems,” Electrica, vol. 19, no. 1, pp. 37-47, 2019.
[14] Mouassa, S. and Bouktir, T., “Multi-objective ant lion optimization algorithm to solve large-scale multi-objective
optimal reactive power dispatch problem,” COMPEL - The international journal for computation and mathematics
in electrical and electronic engineering, vol. 38, no. 1, pp. 304-324, 2018.
[15] Tawfiq M. Aljohani, Ahmed F. Ebrahim, Osama Mohammed, “Single and multiobjective optimal reactive power
dispatch based on hybrid artificial physics–particle swarm optimization,” Energies, vol. 12, no. 12, pp. 2333, 2019.
[16] Abdechiri M, Meybodi MR and Bahrami H., “Gases Brownian motion optimization: An algorithm for optimization
(GBMO),” Applied Soft Computing, vol. 13, no. 5, pp. 2932–2946, 2013.
[17] Mirjalili SM and Hatamlou A., “Multi-verse optimizer: A nature-inspired algorithm for global optimization,”
Neural Computing and Applications, vol. 27, no. 2, pp. 495–513, 2016.
[18] IEEE, “The IEEE-test systems,” 1993. http://www.ee.washington.edu/trsearch/pstca/.
[19] Ali Nasser Hussain, Ali Abdulabbas Abdullah and Omar Muhammed Neda, “Modified particle swarm optimization
for solution of reactive power dispatch,” Research Journal of Applied Sciences, Engineering and Technology,
vol. 15, no. 8, pp. 316-327, 2018.
[20] S. Surender Reddy, “Optimal reactive power scheduling using cuckoo search algorithm,” International Journal of
Electrical and Computer Engineering (IJECE), vol. 7, no. 5, pp. 2349-2356. 2017.
[21] S.S. Reddy, et al., “Faster evolutionary algorithm based optimal power flow using incremental variables,”
Electrical Power and Energy Systems, vol. 54, pp. 198-210, 2014.