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A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 2723)

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.

Keywords

  • Particle Swarm Optimization
  • Pareto Front
  • Multiobjective Optimization
  • Generational Distance
  • Multiobjective Optimization Problem

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/3-540-45105-6_4
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© 2003 Springer-Verlag Berlin Heidelberg

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Li, X. (2003). A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization. In: , et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45105-6_4

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  • DOI: https://doi.org/10.1007/3-540-45105-6_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40602-0

  • Online ISBN: 978-3-540-45105-1

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