A multimodal particle swarm optimizer based on fitness Euclidean-distance ratio

Xiaodong Li
2007 Proceedings of the 9th annual conference on Genetic and evolutionary computation - GECCO '07  
One of the most critical issues that remains to be fully addressed in existing multimodal evolutionary algorithms is the difficulty in pre-specifying parameters used for estimating how far apart optima are. These parameters are typically represented as some sorts of niching parameters in existing EAs. Without prior knowledge of a problem, it is almost impossible to determine appropriate values for such niching parameters. This paper proposes a PSO for multimodal optimization that removes the
more » ... that removes the need of these niching parameters. Our results show that the proposed algorithm, Fitness Euclidean-distance Ratio based PSO (FER-PSO) is able to reliably locate multiple global optima on the search landscape over some widely used multimodal optimization test functions, given that the population size is sufficiently large.
doi:10.1145/1276958.1276970 dblp:conf/gecco/Li07 fatcat:efk5msjibzajhjdshys7jweznm