Adaptive Balance Factor in Particle Swarm Optimization

Particle Swarm Optimization (PSO) is a refined optimization method, that has drawn interest of researchers in different areas because of its simplicity and efficiency. In standard PSO, particles roam over the search area with the help of two accelerating parameters. The proposed algorithm is tested...

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Bibliographic Details
Published inProceedings of Sixth International Conference on Soft Computing for Problem Solving Vol. 546; pp. 12 - 21
Main Authors Sharma, Siddhi Kumari, Sharma, R. S.
Format Book Chapter
LanguageEnglish
Published Singapore Springer 2017
Springer Singapore
SeriesAdvances in Intelligent Systems and Computing
Subjects
Acceleration coefficients
Artificial intelligence
Imaging systems & technology
Meta-heuristic optimization techniques
Nature inspired algorithm
Particle swarm optimization algorithm
Swarm intelligence
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Summary:Particle Swarm Optimization (PSO) is a refined optimization method, that has drawn interest of researchers in different areas because of its simplicity and efficiency. In standard PSO, particles roam over the search area with the help of two accelerating parameters. The proposed algorithm is tested over 12 benchmark test functions and compared with basic PSO and two other algorithms known as Gravitational search algorithm (GSA) and Biogeography based Optimization (BBO). The result reveals that ABF-PSO will be a competitive variant of PSO.
ISBN:9811033218
9789811033216
ISSN:2194-5357
2194-5365
DOI:10.1007/978-981-10-3322-3_2