Abstract
Adapting a niching algorithm for dynamic environments is described. The Vector-Based Particle Swarm Optimizer locates multiple optima by identifying niches and optimizing them in parallel. To track optima effectively, information from previous results should be utilized in order to find optima after an environment change, with less effort than complete re-optimization would entail. The Vector-Based PSO was adapted for this purpose. Several scenarios were set up using a test problem generator, in order to assess the behaviour of the algorithm in various environments. Results showed that the algorithm could track multiple optima with a varying success rate and that results were to a large extent problem-dependent.
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Schoeman, I., Engelbrecht, A. (2006). Niching for Dynamic Environments Using Particle Swarm Optimization. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_18
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DOI: https://doi.org/10.1007/11903697_18
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