Comparative Study of Particle Swarm Optimization Variants in Complex Mathematics Functions
作者： Juan Carlos Vazquez , Fevrier Valdez , Patricia Melin
DOI: 10.1007/978-3-319-10960-2_11
关键词:
摘要: Particle Swarm Optimization (PSO) is one of the evolutionary computation techniques based on social behaviors birds flocking or fish schooling, biologically inspired computational search and optimization method. Since first introduced by Kennedy Eberhart (A new optimizer using particle swarm theory 39–43, 1995 [1]) in 1995, several variants original PSO have been developed to improve speed convergence, quality solutions found, avoid getting trapped local optima so on. This paper focused performing a comparison different approaches inertia weight such as constant, random adjustments, linear decreasing, nonlinear decreasing fuzzy optimization; we are set 4 mathematical functions validate our approach. These widely used this field study.
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