Variants of Particle Swarm Optimization and Onus of Acceleration Coefficients
Y. V. R. Naga Pawan1, Kolla Bhanu Prakash2
1Y. V. R. Naga Pawan, Research Scholar, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vijayawada, INDIA.
2Kolla Bhanu Prakash, Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vijayawada, INDIA.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 1527-1538 | Volume-8 Issue-5, June 2019 | Retrieval Number: E7883068519/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The Particle Swarm Optimization (PSO) is a widely used optimization algorithm for finding optimized solutions in a diverse gamut of problem domains. The parameters like Initialization, Constriction factor, Inertia Weight, Mutation Operator, Fuzzy Logic and Parallelism have engendered the Particle Swarm Optimization (PSO) with many variants. The variants of PSO have outperformed the Basic Particle Swarm Optimization. In order to comprehend the role of acceleration coefficients in BPSO, an inquiry is carried out. It is observed that the convergence speed of the BPSO is quicker when the acceleration coefficients are not equal than when both are equal.
Keywords: Acceleration Coefficients, Inertia Weight, Particle Swarm Optimization, Variants.
Scope of the Article: Discrete Optimization