论文信息 - Multi-Objective Particle Swarm Optimization Based on Self-Update and Grid Strategy

Multi-Objective Particle Swarm Optimization Based on Self-Update and Grid Strategy

A multi-objective particle swarm optimization based on self-update and grid strategy was proposed for improving the function of Pareto set. The method chooses \( pBest \) with self-update strategy, uses grid method as the selection mode of \( gBest \). It is tested to compare with the performance of the optimal solution’s evaluation and selection. The results show that algorithm has gained better convergence with even distributing and diversity of Pareto set.

[1] Marco Laumanns,et al. SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[2] Lothar Thiele,et al. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[3] J. Teich,et al. The role of /spl epsi/-dominance in multi objective particle swarm optimization methods , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[4] Frank Kursawe,et al. A Variant of Evolution Strategies for Vector Optimization , 1990, PPSN.

[5] Jonathan E. Fieldsend,et al. A MOPSO Algorithm Based Exclusively on Pareto Dominance Concepts , 2005, EMO.

[6] C.A. Coello Coello,et al. MOPSO: a proposal for multiple objective particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[7] Wang Jianguo,et al. Multi-objective Particle Swarm Optimization Algorithm Based on Self-update Strategy , 2012, 2012 International Conference on Industrial Control and Electronics Engineering.

[8] David W. Corne,et al. Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[9] Gary B. Lamont,et al. AN INTRODUCTION TO MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS AND THEIR APPLICATIONS , 2004 .