Abstract
Particle Swarm Optimization (PSO) is a population-based search methodology inspired by social behavior observed in nature, such as flocks of birds and schools of fish. In many studies, PSO has been successful in a variety of optimization problems. The purpose of this paper is to improve performance of the PSO algorithm in case of high-dimensional problems. We propose a novel PSO model, the Rotated Particle Swarm (RPS), which is introduced the coordinate conversion. The numerical simulation results show the RPS is effective in optimizing high-dimensional functions.
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