Cultural Particle Swarm Algorithms for Constrained Multi-objective Optimization

Authors:
Fang Gao;Qiang Zhao;Hongwei Liu;Gang Cui
Affiliations:
School of Computer Science and Technology, Harbin Institute of Technology, 150001 Harbin, China;School of traffic, Northeast forestry university, 150040 Harbin, China;School of Computer Science and Technology, Harbin Institute of Technology, 150001 Harbin, China;School of Computer Science and Technology, Harbin Institute of Technology, 150001 Harbin, China
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
Year:
2007

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Abstract

In this paper, we propose to integrate particle swarm optimization algorithm into cultural algorithms frame to develop a more efficient cultural particle swarm algorithms (CPSA) for constrained multi-objective optimization problem. In our CPSA, the population space of cultural algorithms consists of n+1 subswarms which are used to search for the n single-objective optimums and an additional multiobjective optimum. The belief space accepts 20% elite particles form each subswarm and further takes crossover to create Pareto optimums. Niche Pareto tournament selection is further executed to ensure Pareto set to distribute uniformly along Pareto frontier. Additional memory of Pareto optimums spool is allocated and updated in each iteration to keep resultant Pareto solutions. Besides, a direct comparison method is employed to handle constraints without needing penalty functions. Two examples are presented to demonstrate the effectiveness of the proposed algorithm.