Abstract
An Intelligent Particle Swarm Optimization (IPSO) for MO problems is proposed based on AER (Agent-Environment-Rules) model, in which Competition and Clonal Selection operator are designed to provide an appropriate selection pressure to propel the swarm population towards the Pareto-optimal front. Simulations and comparison with NSGA-II and MOPSO indicate that IPSO is highly competitive.
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Zhang, X., Meng, H., Jiao, L. (2005). Improving PSO-Based Multiobjective Optimization Using Competition and Immunity Clonal. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596448_124
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DOI: https://doi.org/10.1007/11596448_124
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30818-8
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