A novel artificial fish swarm algorithm based on multi-objective optimization

Authors:
Yi-Kui Zhai;Ying Xu;Jun-Ying Gan
Affiliations:
College of Information and Engineering, Wuyi University, Jiangmen, Guangdong, China;College of Information and Engineering, Wuyi University, Jiangmen, Guangdong, China;College of Information and Engineering, Wuyi University, Jiangmen, Guangdong, China
Venue:
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Year:
2012

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Abstract

As is well known, there isn't exists only the global optimal solution making all objective functions are optimized in multi-objective optimization problem. In this paper, a novel global artificial fish swarm algorithm is proposed in order to finding the Pareto approximate solution of Mop. The chaotic search initialization and improved differential evolution methods were proposed to lead artificial fish into global optimum value. The experimental results show that the proposed algorithm is superior to traditional one and feasible for multi-objective optimization problem.