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

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 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. Categories and Subject Descriptors G.1 [Numerical Analysis]: Optimization; F.2.1 [Analysis of Algorithms and Problem Complexity]: Numerical Algorithms and Problems General Terms A...

• Type: Conference
• Year: 2007
• Where: GECCO
• Authors: Xiaodong Li
• Keywords: GECCO 2007, Multimodal Evolutionary Algorithms, Multimodal Optimization, Niching Parameters