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Intelligent particle swarm optimization in multiobjective optimization

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3 Author(s)
Zhang Xiao-hua ; Inst. of Intelligent Inf. Process., Xidian Univ., Xi''an ; Meng Hong-yun ; Jiao Li-cheng

How to find a sufficient number of uniformly distributed and representative Pareto optimal solutions is very important for multiobjective optimization (MO) problems. A new model for particle swarm optimization is constructed firstly, and then an intelligent particle swarm optimization (IPSO) for MO problems is proposed based on AER (agent-environment-rules) model, in which competition operator and clonal selection operator are designed to provide an appropriate selection pressure to propel the swarm population towards the Pareto-optimal front. The quantitative and qualitative comparisons indicate that the proposed approach is highly competitive and that can be considered as a viable alternative to solve MO problems

Published in:

Evolutionary Computation, 2005. The 2005 IEEE Congress on  (Volume:1 )

Date of Conference:

5-5 Sept. 2005

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