Chaos Adaptive Improved Particle Swarm Optimization Algorithm and its application in Multi-objective Optimization

Volume 3, Issue 1, February 2018     |     PP. 1-15      |     PDF (366 K)    |     Pub. Date: January 2, 2018
DOI:    378 Downloads     7469 Views  

Author(s)

CHEN Bingsheng, Gannan Normal University, Ganzhou, Jiangxi 341000, China
LIU Liang, Gannan Normal University, Ganzhou, Jiangxi 341000, China
SU Keming, Gannan Normal University, Ganzhou, Jiangxi 341000, China
ZHANG Huaijin, Gannan Normal University, Ganzhou, Jiangxi 341000, China
LI Mengshan, Gannan Normal University, Ganzhou, Jiangxi 341000, China

Abstract
To overcome the problem of premature convergence on particle swarm optimization (PSO), this paper proposes an improved particle swarm optimization method (IPSO) that based on self-adaptive regulation strategy and chaos theory. For a given the effective balance of particles’ searching and development ability, self-adaptive regulation strategy is employed to optimize the inertia weight. To improve efficiency and quality of search, learning factor is optimized by generating Chaotic Sequences by Chaos Theory. The proposed improved methods achieve better convergence performance and increases searching speed. Simulation results of some typical optimization problems and comparisons with typical multi-objective optimization algorithms show that IPSO has an ability of fast convergence speed, and the diversity of non-dominated and the convergence are ideal. The algorithm meets requirements of multi-objective optimization Problem.

Keywords
Particle Swarm Optimization, multi-objective optimization, Chaos Theory, self-adaptive regulation strategy

Cite this paper
CHEN Bingsheng, LIU Liang, SU Keming, ZHANG Huaijin, LI Mengshan, Chaos Adaptive Improved Particle Swarm Optimization Algorithm and its application in Multi-objective Optimization , SCIREA Journal of Computer. Volume 3, Issue 1, February 2018 | PP. 1-15.

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