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Journal articles on the topic 'PSO (Particle Swarm Optimization) and Loss reduction'

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Published: 3 June 2025

Last updated: 31 July 2025

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1

Lenin, K. "REDUCTION OF ACTIVE POWER LOSS BY VOLITION PARTICLE SWARM OPTIMIZATION." International Journal of Research -GRANTHAALAYAH 6, no. 6 (2018): 346–56. http://dx.doi.org/10.29121/granthaalayah.v6.i6.2018.1379.

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This paper projects Volition Particle Swarm Optimization (VP) algorithm for solving optimal reactive power problem. Particle Swarm Optimization algorithm (PSO) has been hybridized with the Fish School Search (FSS) algorithm to improve the capability of the algorithm. FSS presents an operator, called as collective volition operator, which is capable to auto-regulate the exploration-exploitation trade-off during the algorithm execution. Since the PSO algorithm converges faster than FSS but cannot auto-adapt the granularity of the search, we believe the FSS volition operator can be applied to the

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2

Dr.K.Lenin. "REDUCTION OF ACTIVE POWER LOSS BY VOLITION PARTICLE SWARM OPTIMIZATION." International Journal of Research - Granthaalayah 6, no. 6 (2018): 346–56. https://doi.org/10.5281/zenodo.1308981.

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This paper projects Volition Particle Swarm Optimization (VP) algorithm for solving optimal reactive power problem. Particle Swarm Optimization algorithm (PSO) has been hybridized with the Fish School Search (FSS) algorithm to improve the capability of the algorithm. FSS presents an operator, called as collective volition operator, which is capable to auto-regulate the exploration-exploitation trade-off during the algorithm execution. Since the PSO algorithm converges faster than FSS but cannot auto-adapt the granularity of the search, we believe the FSS volition operator can be applied to the

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3

Dr.K.Lenin. "ACTIVE POWER LOSS REDUCTION BY BETTER-QUALITY PARTICLE SWARM OPTIMIZATION ALGORITHM." International Journal of Research - Granthaalayah 6, no. 1 (2018): 329–37. https://doi.org/10.5281/zenodo.1167554.

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In this paper Better-Quality Particle Swarm Optimization (BPSO) algorithm is proposed to solve the optimal reactive power Problem. Proposed algorithm is obtained by combining particle swarm optimization (PSO), Cauchy mutation and an evolutionary selection strategy. The idea is to introduce the Cauchy mutation into PSO in the hope of preventing PSO from trapping into a local optimum through long jumps made by the Cauchy mutation. In order to evaluate the efficiency of the proposed Better-Quality Particle Swarm Optimization (BPSO) algorithm, it has been tested on IEEE 57 bus system. Simulation R

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4

Lenin, K. "ACTIVE POWER LOSS REDUCTION BY BETTER-QUALITY PARTICLE SWARM OPTIMIZATION ALGORITHM." International Journal of Research -GRANTHAALAYAH 6, no. 1 (2018): 329–37. http://dx.doi.org/10.29121/granthaalayah.v6.i1.2018.1626.

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In this paper Better-Quality Particle Swarm Optimization (BPSO) algorithm is proposed to solve the optimal reactive power Problem. Proposed algorithm is obtained by combining particle swarm optimization (PSO), Cauchy mutation and an evolutionary selection strategy. The idea is to introduce the Cauchy mutation into PSO in the hope of preventing PSO from trapping into a local optimum through long jumps made by the Cauchy mutation. In order to evaluate the efficiency of the proposed Better-Quality Particle Swarm Optimization (BPSO) algorithm, it has been tested on IEEE 57 bus system. Simulation R

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5

Eshan, Karunarathne, Pasupuleti Jagadeesh, Ekanayake Janaka, and Almeida Dilini. "Comprehensive learning particle swarm optimization for sizing and placement of distributed generation for network loss reduction." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2020): 16–23. https://doi.org/10.11591/ijeecs.v20.i1.pp16-23.

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With the technological advancements, distributed generation (DG) has become a common method of overwhelming the issues like power losses and voltage drops which accompanies with the leaf of the feeders of radial distribution networks. Many researchers have used several optimization techniques and tools which could be used to locate and size the DG units in the system. Particle swarm optimization (PSO) is one of the famous optimization techniques. However, the premature convergence is identified as a fundamental adverse effect of this optimization technique. Therefore, the optimization problem

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6

Lenin, Kanagabasai. "Factual power loss reduction by dynamic membrane evolutionary algorithm." International Journal of Advances in Applied Sciences (IJAAS) 10, no. 2 (2021): 99–106. https://doi.org/10.11591/ijaas.v10.i2.pp99-106.

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This paper presents Dynamic Membrane Evolutionary Algorithm (DMEA) has been applied to solve optimal reactive power problem. Proposed methodology merges the fusion and division rules of P systems with active membranes and with adaptive differential evolution (ADE), particle swarm optimization (PSO) exploration stratagem. All elementary membranes are amalgamated into one membrane in the computing procedure. Furthermore, integrated membrane are alienated into the elementary membranes 1, 2,_ m. In particle swarm optimization (PSO) 𝑪<sub>𝟏</sub>, 𝑪<sub>𝟐</sub> (acceleration constants) are vital pa

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7

Dr, K. Lenin. "HYBRIDIZATION OF ANT COLONY ALGORITHM AND PARTICLE SWARM OPTIMIZATION ALGORITHM FOR REDUCTION OF REAL POWER LOSS." International Journal of Research - Granthaalayah 6, no. 12 (2018): 121–27. https://doi.org/10.5281/zenodo.2532382.

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In this work Ant colony optimization algorithm (ACO) & particle swarm optimization (PSO) algorithm has been hybridized (called as APA) to solve the optimal reactive power problem. In this algorithm, initial optimization is achieved by particle swarm optimization algorithm and then the optimization process is carry out by ACO around the best solution found by PSO to finely explore the design space. In order to evaluate the proposed APA, it has been tested on IEEE 300 bus system and compared to other standard algorithms. Simulations results show that proposed APA algorithm performs well in r

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8

Dr.K.Lenin. "DRAG & AVERSION PARTICLE SWARM OPTIMIZATION ALGORITHM FOR REDUCTION OF REAL POWER LOSS." International Journal of Research - Granthaalayah 5, no. 11 (2017): 168–76. https://doi.org/10.5281/zenodo.1069425.

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This paper projects Drag & Aversion Particle Swarm Optimization (DAPSO) algorithm is applied to solve optimal reactive power problem. In DAPSO the idea of decreasing and increasing diversity operators used to control the population into the basic Particle Swarm Optimization (PSO) model. The modified model uses a diversity measure to have the algorithm alternate between exploring and exploiting behavior. The results show that both Drag & Aversion Particle Swarm Optimization (DAPSO) prevents premature convergence to enhanced level but still keeps a rapid convergence. Proposed Drag &

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9

Lenin, K. "ACTUAL POWER LOSS REDUCTION BY AUGMENTED PARTICLE SWARM OPTIMIZATION ALGORITHM." International Journal of Research -GRANTHAALAYAH 6, no. 9 (2018): 212–19. http://dx.doi.org/10.29121/granthaalayah.v6.i9.2018.1222.

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This paper presents an advanced particle swarm optimization Algorithm for solving the reactive power problem in power system. Bacterial Foraging Optimization Algorithm (BFOA) has recently emerged as a very powerful technique for real parameter optimization. In order to overcome the delay in optimization and to further enhance the performance of BFO, this paper proposed a new hybrid algorithm combining the features of BFOA and Particle Swarm Optimization (PSO) called advanced bacterial foraging-oriented particle swarm optimization (ABFPSO) algorithm for solving reactive power problem. The simul

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10

Dr., K. Lenin. "ACTUAL POWER LOSS REDUCTION BY AUGMENTED PARTICLE SWARM OPTIMIZATION ALGORITHM." International Journal of Research - Granthaalayah 6, no. 9 (2018): 212–19. https://doi.org/10.5281/zenodo.1442419.

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This paper presents an advanced particle swarm optimization Algorithm for solving the reactive power problem in power system. Bacterial Foraging Optimization Algorithm (BFOA) has recently emerged as a very powerful technique for real parameter optimization. In order to overcome the delay in optimization and to further enhance the performance of BFO, this paper proposed a new hybrid algorithm combining the features of BFOA and Particle Swarm Optimization (PSO) called advanced bacterial foraging-oriented particle swarm optimization (ABFPSO) algorithm for solving reactive power problem. The simul

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11

Lenin, K. "DRAG & AVERSION PARTICLE SWARM OPTIMIZATION ALGORITHM FOR REDUCTION OF REAL POWER LOSS." International Journal of Research -GRANTHAALAYAH 5, no. 11 (2017): 168–76. http://dx.doi.org/10.29121/granthaalayah.v5.i11.2017.2344.

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This paper projects Drag & Aversion Particle Swarm Optimization (DAPSO) algorithm is applied to solve optimal reactive power problem. In DAPSO the idea of decreasing and increasing diversity operators used to control the population into the basic Particle Swarm Optimization (PSO) model. The modified model uses a diversity measure to have the algorithm alternate between exploring and exploiting behavior. The results show that both Drag & Aversion Particle Swarm Optimization (DAPSO) prevents premature convergence to enhanced level but still keeps a rapid convergence. Proposed Drag &

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12

Kanagabasai, Lenin. "Factual power loss reduction by dynamic membrane evolutionary algorithm." International Journal of Advances in Applied Sciences 10, no. 2 (2021): 99. http://dx.doi.org/10.11591/ijaas.v10.i2.pp99-106.

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<p class="papertitle">This paper presents Dynamic Membrane Evolutionary Algorithm (DMEA) has been applied to solve optimal reactive power problem.Proposed methodology merges the fusion and division rules of P systems with active membranes and with adaptive differential evolution (ADE), particle swarm optimization (PSO) exploration stratagem. All elementary membranes are amalgamated into one membrane in the computing procedure. Furthermore, integrated membrane are alienated into the elementary membranes 1, 2,_ m. In particle swarm optimization (PSO) 𝑪<sub>𝟏</sub>, 𝑪<sub>

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13

Karunarathne, Eshan, Jagadeesh Pasupuleti, Janaka Ekanayake, and Dilini Almeida. "Comprehensive learning particle swarm optimization for sizing and placement of distributed generation for network loss reduction." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2020): 16. http://dx.doi.org/10.11591/ijeecs.v20.i1.pp16-23.

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Abstract:

With the technological advancements, Distributed Generation (DG) has become a common method of overwhelming the issues like power losses and voltage drops which accompanies with the leaf of the feeders of radial distribution networks. Many researchers have used several optimization techniques and tools which could be used to locate and size the DG units in the system. Particle Swarm Optimization (PSO) is one of the famous optimization techniques. However, the premature convergence is identified as a fundamental adverse effect of this optimization technique. Therefore, the optimization problem

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14

Lenin, K. "HYBRIDIZATION OF ANT COLONY ALGORITHM AND PARTICLE SWARM OPTIMIZATION ALGORITHM FOR REDUCTION OF REAL POWER LOSS." International Journal of Research -GRANTHAALAYAH 6, no. 12 (2018): 121–27. http://dx.doi.org/10.29121/granthaalayah.v6.i12.2018.1092.

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In this work Ant colony optimization algorithm (ACO) & particle swarm optimization (PSO) algorithm has been hybridized (called as APA) to solve the optimal reactive power problem. In this algorithm, initial optimization is achieved by particle swarm optimization algorithm and then the optimization process is carry out by ACO around the best solution found by PSO to finely explore the design space. In order to evaluate the proposed APA, it has been tested on IEEE 300 bus system and compared to other standard algorithms. Simulations results show that proposed APA algorithm performs well in r

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15

Lenin, K. "AMENDED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR REAL POWER LOSS REDUCTION AND STATIC VOLTAGE STABILITY MARGIN INDEX ENHANCEMENT." International Journal of Research -GRANTHAALAYAH 6, no. 2 (2018): 146–56. http://dx.doi.org/10.29121/granthaalayah.v6.i2.2018.1555.

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In this paper, Amended Particle Swarm Optimization Algorithm (APSOA) is proposed with the combination of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) for solving the optimal reactive power dispatch Problem. PSO is one of the most widely used evolutionary algorithms in hybrid methods due to its simplicity, convergence speed, an ability of searching Global optimum. GSA has many advantages such as, adaptive learning rate, memory-less algorithm and, good and fast convergence. Proposed hybridized algorithm is aimed at reduce the probability of trapping in local optimum

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16

Dr.K.Lenin. "AMENDED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR REAL POWER LOSS REDUCTION AND STATIC VOLTAGE STABILITY MARGIN INDEX ENHANCEMENT." International Journal of Research - Granthaalayah 6, no. 2 (2018): 146–56. https://doi.org/10.5281/zenodo.1189217.

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In this paper, Amended Particle Swarm Optimization Algorithm (APSOA) is proposed with the combination of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) for solving the optimal reactive power dispatch Problem. PSO is one of the most widely used evolutionary algorithms in hybrid methods due to its simplicity, convergence speed, an ability of searching Global optimum. GSA has many advantages such as, adaptive learning rate, memory-less algorithm and, good and fast convergence. Proposed hybridized algorithm is aimed at reduce the probability of trapping in local optimum

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17

Dr, K. Lenin. "BACK PROPAGATION NEURAL NETWORK TECHNIQUE FOR REDUCTION OF REAL POWER LOSS." International Journal of Research - Granthaalayah 6, no. 12 (2018): 140–46. https://doi.org/10.5281/zenodo.2532396.

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In this work particle swarm optimization algorithm has been hybridized with Back propagation neural network (PSBP) to solve the reactive power problem. Proposed PSBP methodology improves search. PSO algorithm to optimize the original weight, threshold value and when the algorithm ends, optimal point can be found- on the base of PSO algorithm; Back propagation neural network algorithm to search overall situation and then achieve the network training goal. In the particle swarm, every particle’s position represents weights set among the network during the resent iteration. In order to eval

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18

Lenin, K. "ACTIVE POWER LOSS REDUCTION BY ASSORTED ALGORITHMS." International Journal of Research -GRANTHAALAYAH 6, no. 5 (2018): 263–75. http://dx.doi.org/10.29121/granthaalayah.v6.i5.2018.1448.

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This paper presents assorted algorithms for solving optimal reactive power problem. Symbiosis modeling (SM), which extends the dynamics of the canonical PSO algorithm by adding a significant ingredient that takes into account the symbiotic co evolution between species, Hybridization of Evolutionary algorithm with Conventional Algorithm (HCA) that uses the abilities of evolutionary and conventional algorithm and Genetical Swarm Optimization (GS), which combines Genetic Algorithms (GA) and Particle Swarm Optimization (PSO).All the above said SM, HCA,GS algorithms are used to augment the converge

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19

Dr.K.Lenin, *1. "ACTIVE POWER LOSS REDUCTION BY ASSORTED ALGORITHMS." International Journal of Research - Granthaalayah 6, no. 5 (2018): 263–75. https://doi.org/10.5281/zenodo.1270459.

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This paper presents assorted algorithms for solving optimal reactive power problem. Symbiosis modeling (SM), which extends the dynamics of the canonical PSO algorithm by adding a significant ingredient that takes into account the symbiotic co evolution between species, Hybridization of Evolutionary algorithm with Conventional Algorithm (HCA) that uses the abilities of evolutionary and conventional algorithm and Genetical Swarm Optimization (GS), which combines Genetic Algorithms (GA) and Particle Swarm Optimization (PSO).All the above said SM, HCA,GS algorithms are used to augment the converge

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20

Lenin, K. "BACK PROPAGATION NEURAL NETWORK TECHNIQUE FOR REDUCTION OF REAL POWER LOSS." International Journal of Research -GRANTHAALAYAH 6, no. 12 (2018): 140–46. http://dx.doi.org/10.29121/granthaalayah.v6.i12.2018.1100.

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In this work particle swarm optimization algorithm has been hybridized with Back propagation neural network (PSBP) to solve the reactive power problem. Proposed PSBP methodology improves search. PSO algorithm to optimize the original weight, threshold value and when the algorithm ends, optimal point can be found- on the base of PSO algorithm; Back propagation neural network algorithm to search overall situation and then achieve the network training goal. In the particle swarm, every particle’s position represents weights set among the network during the resent iteration. In order to evaluate t

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21

Polara, Vishal, and Jagdish Rathod. "Cost Optimization Approach for MANET using Particle Swarm Optimization." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 8 (2023): 90–98. http://dx.doi.org/10.17762/ijritcc.v11i8.7927.

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This paper present the approach require to increase the QoS of MANET network using particle swarm optimization algorithm. To improve data communication between two nodes we propose an efficient algorithm for AODV protocol using PSO where instead of suppling all default parameter with default value of AODV protocol we try to provide selective parameters with optimum value so that overall requirement of control packet get decrease that in turn result in to increase quality of service parameters of MANET. For the enhancement of reliability and reduction of cost, node speed control mechanism is im

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Dr.K.Lenin, *1. "A REDUCTION OF REAL POWER LOSS BY ENRICHED GENETIC ALGORITHM." International Journal of Research - Granthaalayah 6, no. 5 (2018): 167–76. https://doi.org/10.5281/zenodo.1255286.

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In this paper Enriched Genetic Algorithm (EGA) is proposed to solve the optimal reactive power problem. In order to overcome the drawbacks of standard genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, some improved mechanisms based on non-linear ranking selection, competition and selection among several crossover offspring and adaptive change of mutation scaling are adopted in the genetic algorithm, and dynamical parameters are adopted in PSO. The new population is produced through three approaches to improve the global optimization performance. Proposed algorithm has bee

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23

Rekha, E., D. Sattianadan, and M. Sudhakaran. "Maximum Loss Reduction and Voltage Profile Improvement with Placement of Hybrid Solar-Wind System." Advanced Materials Research 768 (September 2013): 371–77. http://dx.doi.org/10.4028/www.scientific.net/amr.768.371.

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Distributed generators (DG) are much beneficial in reducing the losses effectively compared to other methods of loss reduction. It is expected to become more important in future generation. This paper deals with the multi DGs placement in radial distribution system to reduce the system power loss and improve the voltage profile by using the optimization technique of particle swarm optimization (PSO). The PSO provides a population-based search procedure in which individuals called particles change their positions with time. Initially, the algorithm randomly generates the particle positions repr

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Dr.K.Lenin. "IMPROVED GREY WOLF OPTIMIZATION ALGORITHM FOR REDUCTION OF REAL POWER LOSS." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 5, no. 8 (2018): 139–44. https://doi.org/10.5281/zenodo.1343479.

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This paper projects Improved Grey Wolf Optimization (IGWO) algorithm for solving optimal reactive power problem. Projected IGWO algorithm hybridizes the wolf optimization (WO) algorithm with particle swarm optimization (PSO) algorithm. This algorithm is inspired by the hunting behavior and social leadership of grey wolves in nature. Due to the hybridization of both WO with PSO exploration ability of the proposed Grey wolf optimization algorithm has been enhanced. Efficiency of the projected Improved Grey Wolf Optimization (IGWO) algorithm is tested in practical 191 bus test system. Simulation

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25

Enitan Ogunbowale, Peter, Isaiah Adediji Adejumobi, Oluwaseun Ibrahim Adebisi, Idowu Ademola Osinuga, and Taiwo Olalekan Fehintola. "OPTIMAL PLACEMENT OF UNIFIED POWER FLOW CONTROLLER USING PARTICLE SWARM OPTIMIZATION TECHNIQUE." International Journal of Advanced Research 12, no. 05 (2024): 507–20. http://dx.doi.org/10.21474/ijar01/18742.

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The load growth in recent times due to advancements in technology and population increase has led to shortage of reactive power in the power system resulting in key issues such as voltage instability, large line losses, power outages among others. In this work, Particle Swarm Optimization (PSO) technique was used for optimal placement of Unified Power Flow Controller (UPFC) for power loss minimization using the IEEE 6-bus system as a test network. The load flow analysis was performed using Gauss-Seidel iterative method with and without inclusion of PSO-based UPFC. The PSO algorithm was used to

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Messaoud, Garah, Oudira Houcine, Djouane Lotfi, and Hamdiken Nazih. "Particle Swarm Optimization for the Path Loss Reduction in Suburban and Rural Area." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 4 (2017): 2125–31. https://doi.org/10.11591/ijece.v7i4.pp2125-2131.

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In the present work, a precise optimization method is proposed for tuning the parameters of the COST231 model to improve its accuracy in the path loss propagation prediction. The Particle Swarm Optimization is used to tune the model parameters. The predictions of the tuned model are compared with the most popular models. The performance criteria selected for the comparison of various empirical path loss models is the Root Mean Square Error (RMSE). The RMSE between the actual and predicted data are calculated for various path loss models. It turned out that the tuned COST 231 model outperforms

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27

Meseret, Yenesew. "ENHANCED PARTICLE SWARM OPTIMIZATION (PSO) ALGORITHM FOR REACTIVE POWER OPTIMIZATION IN THE DISTRIBUTION SYSTEM." International Journal of Engineering Research and Modern Education 2, no. 2 (2017): 56–64. https://doi.org/10.5281/zenodo.1130820.

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The problem of optimal capacitor allocation in electric distribution systems involves maximizing energy utilization, feeder loss reduction, and voltage profile improvement. The feeder loss can be separated into two parts based on the active and reactive power loss components. This paper presents an optimization method for minimizing the loss associated with the reactive component of branch currents by allocating optimal reactive power in the distribution system. In this paper, particle swarm optimization (PSO) algorithm is used for reactive power optimization problem in the distribution system

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Dr.K.Lenin. "REAL POWER LOSS REDUCTION BY ENHANCED ACCLIMATIZED BACTERIAL EXPLORATION ALGORITHM." International Journal of Research - Granthaalayah 6, no. 3 (2018): 182–90. https://doi.org/10.5281/zenodo.1213612.

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This paper presents Enhanced Acclimatized Bacterial Exploration (EBE) algorithm to solve reactive power problem. Bacterial Search Optimization Algorithm has recently emerged as a very powerful technique based on the behaviour of E-coli bacteria. In order to speed up the convergence of Bacterial search Optimization Algorithm, this paper proposed a new hybridization between Bacterial Search Optimization Algorithm (BSO) and Particle Swarm Optimization (PSO). In order to evaluate the proposed Enhanced Acclimatized Bacterial Exploration (EBE) algorithm, it has been tested in standard IEEE 118 &

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Lenin, K. "REAL POWER LOSS REDUCTION BY ENHANCED ACCLIMATIZED BACTERIAL EXPLORATION ALGORITHM." International Journal of Research -GRANTHAALAYAH 6, no. 3 (2018): 182–90. http://dx.doi.org/10.29121/granthaalayah.v6.i3.2018.1513.

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This paper presents Enhanced Acclimatized Bacterial Exploration (EBE) algorithm to solve reactive power problem. Bacterial Search Optimization Algorithm has recently emerged as a very powerful technique based on the behaviour of E-coli bacteria. In order to speed up the convergence of Bacterial search Optimization Algorithm, this paper proposed a new hybridization between Bacterial Search Optimization Algorithm (BSO) and Particle Swarm Optimization (PSO). In order to evaluate the proposed Enhanced Acclimatized Bacterial Exploration (EBE) algorithm, it has been tested in standard IEEE 118 &

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30

Lenin, K. "A REDUCTION OF REAL POWER LOSS BY ENRICHED GENETIC ALGORITHM." International Journal of Research -GRANTHAALAYAH 6, no. 5 (2018): 167–76. http://dx.doi.org/10.29121/granthaalayah.v6.i5.2018.1438.

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In this paper Enriched Genetic Algorithm (EGA) is proposed to solve the optimal reactive power problem. In order to overcome the drawbacks of standard genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, some improved mechanisms based on non-linear ranking selection, competition and selection among several crossover offspring and adaptive change of mutation scaling are adopted in the genetic algorithm, and dynamical parameters are adopted in PSO. The new population is produced through three approaches to improve the global optimization performance. Proposed algorithm has bee

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Ngang, N. B., C. C. Nwagu, F. Obuye, and J. J. Uket. "Minimization of Power Losses in Distribution Network using Particle Swarm Optimization." International Journal of Information Sciences and Engineering 8, no. 2 (2024): 1–13. https://doi.org/10.5281/zenodo.13743999.

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<em>In this paper, an improved particle swarm optimization (PSO) method is proposed to optimally size and place a DG unit in an electrical power system so as to improve voltage profile and reduce active power losses in the system. The incessant power failure observed in the distribution network has paralyzed business activities. This is primarily caused by power losses in the network that is anchored by the weak buses that their per unit volts could not attain threshold of 0.95 through 1.05.  To stop this persistent power failure in the distribution network constituted by power losses the

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32

Yahiaoui, Merzoug, Abdelkrim Bouanane, and Larbi Boumediene. "Distribution network reconfiguration for loss reduction using PSO method." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 5009–15. https://doi.org/10.11591/ijece.v10i5.pp5009-5015.

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In recent years, the reconfiguration of the distribution network has been proclaimed as a method for realizing power savings, with virtually zero cost. The current trend is to design distribution networks with a mesh network structure, but to operate them radially. This is achieved by the establishment of an appropriate number of switchable branches which allow the realization of a radial configuration capable of supplying all of the normal defects in the box of permanent defect. The purpose of this article is to find an optimal reconfiguration using a Meta heuristic method, namely the particl

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Rehman, Ramesha, Mashood Ul Haq Chishti, and Hamza Yamin. "Efficient GPU Power Management through Advanced Framework Utilizing Optimization Algorithms." Computer Science Journal of Moldova 32, no. 1(94) (2024): 132–52. http://dx.doi.org/10.56415/csjm.v32.08.

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The rapid rise in power usage by GPUs due to advances in machine and deep learning has led to an increase in power consumption of GPUs in Deep Learning workloads. To address this issue, a novel research project focuses on integrating Particle Swarm Optimization into a model training optimization framework to effectively reduce GPU power consumption during machine learning and deep learning training workloads. By utilizing the Particle Swarm Optimization (PSO)\protect\hyperlink{b1}{{[}1{]}} algorithm within the proposed framework, we show the effectiveness of PSO in creating a more efficient po

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Adegoke, Samson Ademola, Yanxia Sun, and Zenghui Wang. "Minimization of Active Power Loss Using Enhanced Particle Swarm Optimization." Mathematics 11, no. 17 (2023): 3660. http://dx.doi.org/10.3390/math11173660.

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Identifying the weak buses in power system networks is crucial for planning and operation since most generators operate close to their operating limits, resulting in generator failures. This work aims to identify the critical/weak node and reduce the system’s power loss. The line stability index (Lmn) and fast voltage stability index (FVSI) were used to identify the critical node and lines close to instability in the power system networks. Enhanced particle swarm optimization (EPSO) was chosen because of its ability to communicate with better individuals, making it more efficient to obtain a p

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Dr., K. Lenin. "REAL POWER LOSS REDUCTION & VOLTAGE STABILITY AMPLIFICATION BY HYBRIDIZATION OF RESTARTED SIMULATED ANNEALING WITH PARTICLE SWARM OPTIMIZATION ALGORITHM." International Journal of Research - Granthaalayah 6, no. 9 (2018): 246–58. https://doi.org/10.5281/zenodo.1442431.

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This paper presents an algorithm for solving the multi-objective reactive power dispatch problem in a power system. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. Evolutionary algorithm and Swarm Intelligence algorithm (EA, SI), a part of Bio inspired optimization algorithm, have been widely used to solve numerous optim

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Diana Mulya Dewi, Nuzul Hikmah, Imam Marzuki, and Ahmad Izzuddin. "Rekonfigurasi Jaringan Menggunakan Binary Particle Swarm Optimization (BPSO) Pada Penyulang Suryagraha." Jurnal JEETech 1, no. 1 (2020): 22–30. http://dx.doi.org/10.48056/jeetech.v1i1.4.

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A radial distribution electrical network at a certain distance will have a large voltage loss due to conductive losses, especially at the endpoint. The tip voltage is determined by the distance of the distribution and the amount of load. The form of configuration also affects the amount of power loss and voltage loss. So that a good configuration is needed in order to obtain good efficiency. Reconfiguration of the distribution network is used to reset the network configuration form by opening and closing switches on the distribution network. Reconfiguration is expected to reduce power losses a

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Napis, Nur Faziera, Mohamad Fani Sulaima, Aida Fazliana Abd Kadir, Chin Kim Gan, Wardiah Mohd Dahalan, and Marizan Sulaiman. "A Comprehensive Study of Improved Evolutionary Particle Swarm Optimization (IEPSO) for Network Reconfiguration with DGs Sizing Concurrently." Applied Mechanics and Materials 785 (August 2015): 19–23. http://dx.doi.org/10.4028/www.scientific.net/amm.785.19.

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This paper deals with the reconfiguration of the distribution network system to investigate the total power losses considering Distribution Generations (DGs) sizing concurrently. To overcome other limitations and enhance the solution performances, a new optimization approach called Improved Evolutionary Particle Swarm Optimization (IEPSO) is proposed. The primary aim of this study is to investigate the contribution of the proposed algorithms towards total power losses by considering the optimum DG size simultaneously. The proposed method is compared with the traditional Particle Swarm Optimiza

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Lenin, K. "REAL POWER LOSS REDUCTION & VOLTAGE STABILITY AMPLIFICATION BY HYBRIDIZATION OF RESTARTED SIMULATED ANNEALING WITH PARTICLE SWARM OPTIMIZATION ALGORITHM." International Journal of Research -GRANTHAALAYAH 6, no. 9 (2018): 246–58. http://dx.doi.org/10.29121/granthaalayah.v6.i9.2018.1229.

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This paper presents an algorithm for solving the multi-objective reactive power dispatch problem in a power system. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. Evolutionary algorithm and Swarm Intelligence algorithm (EA, SI), a part of Bio inspired optimization algorithm, have been widely used to solve numerous optim

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39

Diana Mulya Dewi, Nuzul Hikmah, Imam Marzuki, and Ahmad Izzuddin. "Rekonfigurasi Jaringan Radial Distribusi Tenaga Listrik Penyulang Suryagraha Menggunakan Binary Particle Swarm Optimization (BPSO)." Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan 9, no. 2 (2019): 58–66. http://dx.doi.org/10.48056/jintake.v9i2.42.

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A radial distribution electrical network at a certain distance will have a large voltage loss due to conductive losses, especially at the end point. The tip voltage is determined by the distance of the distribution and the amount of load. The form of configuration also affects the amount of power loss and voltage loss. So that a good configuration is needed in order to obtain good efficiency. Reconfiguration of the distribution network is used to reset the network configuration form by opening and closing switches on the distribution network. Reconfiguration is expected to reduce power losses

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Dewi, Diana Mulya, Nuzul Hikmah, Imam Marzuki, and Ahmad Izzuddin. "Rekonfigurasi Jaringan Radial Distribusi Tenaga Listrik Penyulang Suryagraha Menggunakan Binary Particle Swarm Optimization (BPSO)." Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan 9, no. 2 (2018): 58–66. http://dx.doi.org/10.32492/jintake.v9i2.775.

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A radial distribution electrical network at a certain distance will have a large voltage loss due to conductive losses, especially at the end point. The tip voltage is determined by the distance of the distribution and the amount of load. The form of configuration also affects the amount of power loss and voltage loss. So that a good configuration is needed in order to obtain good efficiency. Reconfiguration of the distribution network is used to reset the network configuration form by opening and closing switches on the distribution network. Reconfiguration is expected to reduce power losses

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Merzoug, Yahiaoui, Bouanane Abdelkrim, and Boumediene Larbi. "Distribution network reconfiguration for loss reduction using PSO method." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 5009. http://dx.doi.org/10.11591/ijece.v10i5.pp5009-5015.

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In recent years, the reconfiguration of the distribution network has been proclaimed as a method for realizing power savings, with virtually zero cost. The current trend is to design distribution networks with a mesh network structure, but to operate them radially. This is achieved by the establishment of an appropriate number of switchable branches which allow the realization of a radial configuration capable of supplying all of the normal defects in the box of permanent defect. The purpose of this article is to find an optimal reconfiguration using a Meta heuristic method, namely the particl

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Dr.K.Lenin, *1. "ACTIVE POWER LOSS REDUCTION BY SYNTHESIZED ALGORITHM." International Journal of Research - Granthaalayah 6, no. 5 (2018): 149–56. https://doi.org/10.5281/zenodo.1255264.

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In this paper, Synthesized Algorithm (SA) proposed to solve the optimal reactive power problem. Proposed Synthesized Algorithm (SA) is a combination of three well known evolutionary algorithms, namely Differential Evolution (DE) algorithm, Particle Swarm Optimization (PSO) algorithm, and Harmony Search (HS) algorithm. It merges the general operators of each algorithm recursively. This achieves both good exploration and exploitation in SA without altering their individual properties. In order to evaluate the performance of the proposed SA, it has been tested in Standard IEEE 57,118 bus systems

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Lenin, K. "ACTIVE POWER LOSS REDUCTION BY SYNTHESIZED ALGORITHM." International Journal of Research -GRANTHAALAYAH 6, no. 5 (2018): 149–56. http://dx.doi.org/10.29121/granthaalayah.v6.i5.2018.1436.

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In this paper, Synthesized Algorithm (SA) proposed to solve the optimal reactive power problem. Proposed Synthesized Algorithm (SA) is a combination of three well known evolutionary algorithms, namely Differential Evolution (DE) algorithm, Particle Swarm Optimization (PSO) algorithm, and Harmony Search (HS) algorithm. It merges the general operators of each algorithm recursively. This achieves both good exploration and exploitation in SA without altering their individual properties. In order to evaluate the performance of the proposed SA, it has been tested in Standard IEEE 57,118 bus systems

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Ratsapa, Patcharapol, Kundjanasith Thonglek, Chantana Chantrapornchai, and Kohei Ichikawa. "Automated Pruning Framework for Large Language Models Using Combinatorial Optimization." AI 6, no. 5 (2025): 96. https://doi.org/10.3390/ai6050096.

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Currently, large language models (LLMs) have been utilized in many aspects of natural language processing. However, due to their significant size and high computational demands, large computational resources are required for deployment. In this research, we focus on the automated approach for size reduction of such a model. We propose the framework to perform the automated pruning based on combinatorial optimization. Two techniques were particularly studied, i.e., particle swarm optimization (PSO) and whale optimization algorithm (WOA). The model pruning problem was modeled as a combinatorial

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Dr.K.Lenin. "REDUCTION OF ACTIVE POWER LOSS BY IMPROVED FROG LEAPING ALGORITHM." International Journal of Research - Granthaalayah 5, no. 9 (2017): 44–51. https://doi.org/10.5281/zenodo.999199.

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This paper presents Improved Frog Leaping (IFL) algorithm for solving optimal reactive power problem. Comprehensive exploration capability of Particle Swarm Optimization (PSO) and good local search ability of Frog Leaping Algorithm (FLA) has been hybridized to solve the reactive power problem and it overcomes the shortcomings of premature convergence. In order to evaluate the validity of the proposed Improved Frog Leaping (IFL) algorithm, it has been tested in Standard IEEE 57,118 bus systems and compared to other standard algorithms. Simulation results show that proposed Improved Frog Leaping

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Lenin, K. "REDUCTION OF ACTIVE POWER LOSS BY IMPROVED FROG LEAPING ALGORITHM." International Journal of Research -GRANTHAALAYAH 5, no. 9 (2017): 44–51. http://dx.doi.org/10.29121/granthaalayah.v5.i9.2017.2197.

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This paper presents Improved Frog Leaping (IFL) algorithm for solving optimal reactive power problem. Comprehensive exploration capability of Particle Swarm Optimization (PSO) and good local search ability of Frog Leaping Algorithm (FLA) has been hybridized to solve the reactive power problem and it overcomes the shortcomings of premature convergence. In order to evaluate the validity of the proposed Improved Frog Leaping (IFL) algorithm, it has been tested in Standard IEEE 57,118 bus systems and compared to other standard algorithms. Simulation results show that proposed Improved Frog Leaping

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V., Dhana Raj*1 A. M. Prasad2 &. G. M. V. Prasad3. "OKDFNP: OPTIMIZATION FOR KOCH DIPOLE FRACTAL ANTENNA USING PSO." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 6, no. 5 (2019): 145–54. https://doi.org/10.5281/zenodo.2751108.

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In this paper, simulation of PSO (Particle Swarm Optimization) based dipole fractal antenna at different UWB (Ultra Wide Band) frequencies is considered. The antenna design is based on PSO fed with KOCH dipole fractal antenna. Parametric analyses are calculated for the radiation pattern, return loss, power gain, and real, imaginary voltage impedance. The structure of the antenna is four directional star which helps in the improvement of directionality of the antenna along with the gain in power. Comparing the directivity of PSO with genetic algorithm helps in analysing PSO provides better dire

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Zhang, Shuolin, Jiongcheng Yan, Pengteng Xie, Pengming Zhai, and Ye Tao. "Power System Loss Reduction Strategy Considering Security Constraints Based on Improved Particle Swarm Algorithm and Coordinated Dispatch of Source–Grid–Load–Storage." Processes 13, no. 3 (2025): 831. https://doi.org/10.3390/pr13030831.

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Coordinating various controllable distributed resources to reduce network losses is crucial to the secure and economical operation of modern power systems. This paper proposes a bi-level optimization model for power system loss reduction based on “source-grid-load-storage” coordinated optimization. The upper level aims to minimize the total annual planning cost of the system, determining the location and capacity of distributed photovoltaic systems, energy storage devices, and electric vehicle charging stations. The lower level aims to minimize the load curve smoothness and node voltage deviat

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Marwa, M. Marei, and H. Nawer Manal. "Power losses reduction of power transmission network using optimal location of low-level generation." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (2020): 5586–91. https://doi.org/10.11591/ijece.v10i6.pp5586-5591.

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Due to the growth of demand for electric power, electric power loss reduction takes great attention for the power utility. In this paper, a low-level generation or distributed generation (DG) has been used for transmission power losses reduction. Karbala city transmission network (which is the case study) has been represented by using MATLAB m-file to study the load flow and the power loss for it. The paper proposed the particle swarm optimization (PSO) technique in order to find the optimal number and allocation of DG with the objective to decrease power losses as possible. The results show t

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Mohd Ali, N. Z., I. Musirin, and H. Mohamad. "Clonal evolutionary particle swarm optimization for congestion management and compensation scheme in power system." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 2 (2019): 591. http://dx.doi.org/10.11591/ijeecs.v16.i2.pp591-598.

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This paper presents computational intelligence-based technique for congestion management and compensation scheme in power systems. Firstly, a new model termed as Integrated Multilayer Artificial Neural Networks (IMLANNs) is developed to predict congested line and voltage stability index separately. Consequently, a new optimization technique termed as Clonal Evolutionary Particle Swarm Optimization (CEPSO) was developed. CEPSO is initially used to optimize the location and sizing of FACTS devices for compensation scheme. In this study, Static VAR Compensator (SVC) and Thyristor Control Static C

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