Paper Title:

Multi-Objective Particle Swarm Optimization with Dynamic Crowding Entropy-Based Diversity Measure

Periodical Advanced Engineering Forum OPEN ACCESS (Volume 1)
Main Theme Emerging Engineering Approaches and Applications
Edited by Elwin Mao and Linli Xu
Pages 9-15
DOI 10.4028/www.scientific.net/AEF.1.9
Citation Yue Lin Gao et al., 2011, Advanced Engineering Forum OPEN ACCESS, 1, 9
Online since September, 2011
Authors Yue Lin Gao, Fan Fan Lei
Keywords Dynamic Crowding Entropy, Elitist Archive, Multi-Objective Optimization Problem, Particle Swarm Optimization Algorithm (PSO)
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Abstract

A multi-objective particle swarm optimization with dynamic crowding entropy-based diversity measure is proposed in this paper. Firstly, the elitist strategy is used in external archive in order to improve the convergence of this algorithm. Then the new diversity strategy called dynamic crowding entropy strategy and the global optimization update strategy are used to ensure sufficient diversity and uniform distribution amongst the solution of the non-dominated fronts. The results show that the proposed algorithm is able to find better spread of solutions with the better convergence to the Pareto front and preserve diversity of Pareto optimal solutions the more efficiently.