Particle Swarm Optimization Algorithm with Multiple Phases for Solving Continuous Optimization Problems
نویسندگان
چکیده
An algorithm with different parameter settings often performs differently on the same problem. The are difficult to determine before optimization process. variants of particle swarm (PSO) algorithms studied as exemplars intelligence algorithms. Based concept building block thesis, a PSO multiple phases was proposed analyze relation between search strategies and solved problems. Two algorithm, which were termed fixed phase (PSOFP) dynamic (PSODP) compared six standard in experimental study. benchmark functions for single-objective numerical optimization, includes 12 50 100 dimensions, used study, respectively. results have verified generalization ability variants.
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ژورنال
عنوان ژورنال: Discrete Dynamics in Nature and Society
سال: 2021
ISSN: ['1607-887X', '1026-0226']
DOI: https://doi.org/10.1155/2021/8378579