A New Multiobjective Particle Swarm Optimizer with Fuzzy Learning Sub-Swarms and Self-Adaptive Parameters

2012 ◽  
Vol 7 (1) ◽  
pp. 696-699 ◽  
Author(s):  
Xunlin Jiang ◽  
Haifeng Ling
Keyword(s):  
Particle Swarm ◽  
Particle Swarm Optimizer ◽  
Adaptive Parameters ◽  
Fuzzy Learning ◽  
Self Adaptive

scholarly journals Adaptive Parameters for a Modified Comprehensive Learning Particle Swarm Optimizer

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Yu-Jun Zheng ◽  
Hai-Feng Ling ◽  
Qiu Guan
Keyword(s):  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Method ◽  
Evolutionary Information ◽  
Adaptive Mechanism ◽  
Particle Swarm Optimizer ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Adaptive Parameters ◽  
Individual Particles

Particle swarm optimization (PSO) is a stochastic optimization method sensitive to parameter settings. The paper presents a modification on the comprehensive learning particle swarm optimizer (CLPSO), which is one of the best performing PSO algorithms. The proposed method introduces a self-adaptive mechanism that dynamically changes the values of key parameters including inertia weight and acceleration coefficient based on evolutionary information of individual particles and the swarm during the search. Numerical experiments demonstrate that our approach with adaptive parameters can provide comparable improvement in performance of solving global optimization problems.

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