Multiobjective optimization using dynamic neighborhood particle swarm optimization

Authors:
Xiaohui Hu;R. Eberhart
Affiliations:
Dept. of Biomed. Eng., Purdue Univ., West Lafayette, IN, USA;Dept. of Comput. Sci. & Inf. Eng., Nat. Central Univ., Chungli, Taiwan
Venue:
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Year:
2002

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Abstract

This paper presents a particle swarm optimization (PSO) algorithm for multiobjective optimization problems. PSO is modified by using a dynamic neighborhood strategy, new particle memory updating, and one-dimension optimization to deal with multiple objectives. Several benchmark cases were tested and showed that PSO could efficiently find multiple Pareto optimal solutions.