A comparison study on particle swarm and Evolutionary Particle Swarm Optimization using capacitor placement problem

Oo, Naing Win

doi:10.1109/pecon.2008.4762650

Cited by 8 publications

(2 citation statements)

References 4 publications

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“…In PSO, a population of potential solutions is evolved through successive iterations. Since PSO algorithm has a number of desirable properties, including simplicity of implementation, scalability in dimension, and good empirical performance, it has been applied to solve many real-world problems, such as capacitor placement problem [9], short term load forecasting [10], soft sensor [11], the voltage stability of the electric power distribution systems [12, 13], the orbits of discrete chaotic dynamical systems towards desired target region [14], and the permutation flow-shop sequencing problem [15]. …”

Section: Introductionmentioning

confidence: 99%

A Novel Particle Swarm Optimization Algorithm for Global Optimization

Wang

Liu

2016

Computational Intelligence and Neuroscience

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Particle Swarm Optimization (PSO) is a recently developed optimization method, which has attracted interest of researchers in various areas due to its simplicity and effectiveness, and many variants have been proposed. In this paper, a novel Particle Swarm Optimization algorithm is presented, in which the information of the best neighbor of each particle and the best particle of the entire population in the current iteration is considered. Meanwhile, to avoid premature, an abandoned mechanism is used. Furthermore, for improving the global convergence speed of our algorithm, a chaotic search is adopted in the best solution of the current iteration. To verify the performance of our algorithm, standard test functions have been employed. The experimental results show that the algorithm is much more robust and efficient than some existing Particle Swarm Optimization algorithms.

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Section: Introductionmentioning

confidence: 99%

A Novel Particle Swarm Optimization Algorithm for Global Optimization

Wang

Liu

2016

Computational Intelligence and Neuroscience

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“…One of the powerful and applicable method that is being developed is Evolutionary Particle Swarm Optimization (EPSO) that has been introduced in [13]. There are several research has been reported in application of EPSO on power system according to [14][15][16][17][18]. But there is no paper research yet in distribution network reconfiguration.…”

Section: Introductionmentioning

confidence: 99%

Power Distribution Network Reconfiguration by Using EPSO for Loss Minimizing

Sulaima

Jali

Bukhari

et al. 2014

AMM

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Due to the complexity of modern power distribution network, a hybridization of heuristic method which is called as Evolutionary Particle Swarm Optimization (EPSO) is introduced to identify the open and closed switching operation plans for network reconfiguration. The objectives of this work are to reduce the power losses and improve the voltage profile in the overall system meanwhile minimizing the computational time. The proposed combination of Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) is introduced to make it faster in order to find the optimal solution. The proposed method is applied and it impacts to the network reconfiguration for real power loss and voltage profiles is investigated respectively. The proposed method is tested on a IEEE 33-bus system and it is compared to the traditional PSO and EP method accordingly. The results of this study is hoped to help the power engineer to configure the smart and less lossed network in the future.

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Particle swarm inspired optimization algorithm without velocity equation

El-Sherbiny

2011

Egyptian Informatics Journal

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A comparison study on particle swarm and Evolutionary Particle Swarm Optimization using capacitor placement problem

Cited by 8 publications

References 4 publications

A Novel Particle Swarm Optimization Algorithm for Global Optimization

A Novel Particle Swarm Optimization Algorithm for Global Optimization

Power Distribution Network Reconfiguration by Using EPSO for Loss Minimizing

Particle swarm inspired optimization algorithm without velocity equation

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