Better spread and convergence: Particle swarm multiobjective optimization using the maximin fitness function

Li, X 2004, 'Better spread and convergence: Particle swarm multiobjective optimization using the maximin fitness function', in K. Deb et al. (ed.) Genetic and Evolutionary Computation - GECCO 2004, Seattle, USA, 26-30 June 2004.


Document type: Conference Paper
Collection: Conference Papers

Title Better spread and convergence: Particle swarm multiobjective optimization using the maximin fitness function
Author(s) Li, X
Year 2004
Conference name Annual Genetic and Evolutionary Computation
Conference location Seattle, USA
Conference dates 26-30 June 2004
Proceedings title Genetic and Evolutionary Computation - GECCO 2004
Editor(s) K. Deb et al.
Publisher Springer
Place of publication Berlin
Abstract Maximin strategy has its origin in game theory, but it can be adopted for effective multiobjective optimization. This paper proposes a particle swarm multiobjective optimiser, maximinPSO, which uses a fitness function derived from the maximin strategy to determine Pareto-domination. The maximin fitness function has some very desirable properties with regard to multiobjective optimization. One advantage is that no additional clustering or niching technique is needed, since the maximin fitness of a solution can tell us not only if a solution is dominated or not (with respect to the rest of the population), but also if it is clustered with other solutions, i.e., diversity information. This paper demonstrates that on the ZDT test function series, maximinPSO produces an almost perfect convergence and spread of solutions towards and along the Pareto-optimal front respectively, outperforming one of the state-of-art multiobjective EA algorithms, NSGA II, in all the performance measures used.
Subjects Artificial Intelligence and Image Processing not elsewhere classified
Keyword(s) multiobjective optimisation
particle swarm optimisation
maximin fitness function
Copyright notice © Springer-Verlag Berlin Heidelberg 2004
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