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Genetic and Evolutionary Computation — GECCO 2003

Volume 2723 of the series Lecture Notes in Computer Science pp 37-48

Date:

A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization

  • Xiaodong LiAffiliated withSchool of Computer Science and Information Technology, RMIT University

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Abstract

This paper introduces a modified PSO, Non-dominated Sorting Particle Swarm Optimizer (NSPSO), for better multiobjective optimization. NSPSO extends the basic form of PSO by making a better use of particles’ personal bests and offspring for more effective nondomination comparisons. Instead of a single comparison between a particle’s personal best and its offspring, NSPSO compares all particles’ personal bests and their offspring in the entire population. This proves to be effective in providing an appropriate selection pressure to propel the swarm population towards the Pareto-optimal front. By using the non-dominated sorting concept and two parameter-free niching methods, NSPSO and its variants have shown remarkable performance against a set of well-known difficult test functions (ZDT series). Our results and comparison with NSGA II show that NSPSO is highly competitive with existing evolutionary and PSO multiobjective algorithms.