A non-dominated sorting particle swarm optimizer for multiobjective optimization

Li, X 2003, 'A non-dominated sorting particle swarm optimizer for multiobjective optimization', in Proceedings of the Genetic and Evolutionary Computation Conference, Chicago, USA, 3 August 2003, pp. 37-48.


Document type: Conference Paper
Collection: Conference Papers

Title A non-dominated sorting particle swarm optimizer for multiobjective optimization
Author(s) Li, X
Year 2003
Conference name Genetic and Evolutionary Computation Conference
Conference location Chicago, USA
Conference dates 3 August 2003
Proceedings title Proceedings of the Genetic and Evolutionary Computation Conference
Publisher Springer
Place of publication Berlin, Germany
Start page 37
End page 48
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.
Subjects Neural, Evolutionary and Fuzzy Computation
DOI - identifier 10.1007/3-540-45105-6_4
Copyright notice © Springer-Verlag Berlin Heidelberg 2003
ISBN 9783540406020
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