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

Abstract:

Article Preview

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.

Info:

Periodical:

Edited by:

Elwin Mao and Linli Xu

Pages:

9-15

DOI:

10.4028/www.scientific.net/AEF.1.9

Citation:

Y. L. Gao and F. F. Lei, "Multi-Objective Particle Swarm Optimization with Dynamic Crowding Entropy-Based Diversity Measure", Advanced Engineering Forum, Vol. 1, pp. 9-15, 2011

Online since:

September 2011

Export:

In order to see related information, you need to Login.

In order to see related information, you need to Login.