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Evolutionary Particle Swarm Optimization: A Metaoptimization Method with GA for Estimating Optimal PSO Models Evolutionary Particle Swarm Optimization – A Meta-Optimization Method with GA forEstimating Optimal PSO Models 30235709 Zhang, H 章, 宏 Ishikawa, Masumi 石川, 眞澄 イシカワ, マスミ The original publication is available at www.springerlink.com Particle swarm optimization (PSO) is an algorithm for swarm intelligence based on stochastic and population-based adaptive optimization inspired by social behavior of bird flocks and fish swarms [5, 10]. To demonstrate the effectiveness of the proposed EPSO method, computer experiments on a two-dimensional optimization problem are carried out. We show experimental results, confirm the characteristics of dependency on initial conditions, and analyze the resulting PSO models. The rest of the chapter is organized as follows. Section 5.2 briefly describes the original PSO and RGA/E. Section 5.3 presents the proposed EPSO method and a key idea about the temporally cumulative fitness that we used in the method. Section 5.4 discusses the results of computer experiments applied to a two-dimensional optimization problem and Sect. 5.5 gives conclusions. Springer 2008-05-01 eng journal article AM http://hdl.handle.net/10228/1225 https://kyutech.repo.nii.ac.jp/records/824 https://doi.org/10.1007/978-0-387-74935-8_5 Trends in Intelligent Systems and Computer Engineering 978-0-387-74934-1 978-0-387-74935-8 1876-1100 1876-1119 Lecture Notes in Electrical Engineering 6 75 90 https://kyutech.repo.nii.ac.jp/record/824/files/springer_z.pdf application/pdf 341.0 kB 2009-01-14