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A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization
Xiaodong Li
School of Computer Science and Information Technology
RMIT University,
VIC 3001, Melbourne, Australia
xiaodong@cs.rmit.edu.au
http://www.cs.rmit.edu.au/~xiaodong
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 non-domination 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.
LNCS 2723, p. 37 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003