An Improved Hybrid Multi-objective Particle Swarm Optimization Algorithm

Authors:
Zuan Zhou;Guangming Dai;Pan Fang;Fangjie Chen;Yi Tan
Affiliations:
School of Computer, China University of Geosciences, Wuhan, P.R. China 430074;School of Computer, China University of Geosciences, Wuhan, P.R. China 430074;School of Computer, China University of Geosciences, Wuhan, P.R. China 430074;School of Computer, China University of Geosciences, Wuhan, P.R. China 430074;School of Computer, China University of Geosciences, Wuhan, P.R. China 430074
Venue:
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Year:
2008

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Abstract

Particle Swarm Optimization is a promising evolutionary optimization algorithm. In this paper, an improved hybrid multi-objective particle swarm optimization algorithm (IHMOPSO) is proposed. IHMOPSO uses orthogonal design to initialize population, selects global optimal position from Pareto set. Apply mutation, cross operation and evolutionary selection, and uses two ways to update the position and velocity of particles. Experimental results on many well-known benchmark optimization problems have shown that IHMOPSO is effective and efficient.