Multi-Objective Particle Swarm Optimization based on particle density

被引：0

作者：

Hasegawa T. ^{[1
]}

Ishigame A. ^{[1
]}

Yasuda K. ^{[2
]}

机构：

[1] Graduate School of Engineering, Osaka Prefecture University, Naka-Ku, Sakai 599-8531, 1-1, Gakuen-Cho

[2] Graduate School of Science and Engineering, Tokyo Metropolitan University, Hachioji, Tokyo 192-0397, 1-1, Minami-Osawa

来源：

IEEJ Transactions on Electronics, Information and Systems | 2010年 / 130卷 / 07期

关键词：

Metaheuristics; Multi-Objective Particle Swarm Optimization; Optimization; Particle density;

D O I：

10.1541/ieejeiss.130.1207

中图分类号：

学科分类号：

摘要：

This paper proposes a Multi-Objective Particle Swarm Optimization (MOPSO) with particle density. In the proposed method, density of particles around every Pareto solution is calculated and a Pareto solution with low particle density is selected as gbest which is a best position visited thus far by all of the particles. Then, it is validated through a simulation with some Multi-Objective problems comparing to the sigma method which is the conventional to select gbest. © 2010 The Institute of Electrical Engineers of Japan.

引用

页码：1207 / 1212+16

共 6 条

[1]

Kitamura S., Mori K., Shindo S., Izui Y., Modified multi-objective particle swarm optimization method and its application to energy management system for factories, T. IEEE Japan, 125, 1, pp. 21-28, (2005)

[2]

Mostaghim S., Teich J., Strategies for finding good local guides in multi-objective particle swarm optimization(MOPSO), 2003 IEEE Swarm Intelligence Symposium Proceedings, pp. 26-33, (2003)

[3]

Pulido G.T., Coello Coello C.A., Using clustering techniques to improve the performance of a particle swarm optimizer, Lecture Notes in Computer Science, 3102, pp. 225-237, (2004)

[4]

Sierra M.R., Coello Coello C.A., Improving PSO-based Multi-Objective Optimization using Crowding, Mutation and e-Dominance, Lecture Notes in Computer Science, 3410, pp. 505-519, (2005)

[5]

Yoshii K., Hiroyasu T., Miki M., Effectiveness of neighborhood crossover in EMO algorithms, IPSJ Journal, 47, 4, pp. 1234-1241, (2006)

[6]

Zitzler E., Thiele L., Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach, IEEE Trans. Computation, 3, 4, pp. 505-519, (1999)

← 1 →