Multiobjective particle swarm optimization

Authors:
Jacqueline Moore;Richard Chapman;Gerry Dozier
Affiliations:
Auburn University, Auburn, Alabama;Auburn University, Auburn, Alabama;Auburn University, Auburn, Alabama
Venue:
ACM-SE 38 Proceedings of the 38th annual on Southeast regional conference
Year:
2000

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Abstract

Evolutionary algorithms (EAs) are search procedures based on natural selection [2]. They have been successfully applied to a wide variety of optimization problems [4]. Particle Swarm Optimization (PSO) [1,7] is a new type of evolutionary paradigm that has been successfully used to solve a number of single objective optimization problems (SOPs). However, to date, no one has applied PSO in an effort to solve multiobjective optimization problems (MOPs). The purpose of our research is to demonstrate how PSO can be modified to solve MOPs. In addition to showing how this can be done, we demonstrate its effectiveness on two MOPs.