期刊名称：International Journal of Soft Computing & Engineering
电子版ISSN：2231-2307
出版年度：2013
卷号：3
期号：4
页码：42-46
出版社：International Journal of Soft Computing & Engineering
摘要：Rotating machines are one of the most important elements in almost all th e industries and continuous condition monitoring of these crucial parts is essential for preventing early failure, production line breakdown, improving plant safety, efficiency and reliability. Faults may also be developed over a long period of time or even suddenly. However manual fault detection techniques are error prone. Th is paper identifies and utilizes the distribution information of the population to estim ate the evolutionary states. Based on the states, Adaptive control strategies are developed for the inertia weight and acceleration coefficients for faster convergence speed. The Particle Swarm Optimization (PSO) is thus systematically extended to Adaptive Particle Swarm Optimization (APSO), so as to bring about outstanding performance when solving global optimization problems. This paper proposes an adaptive particle swarm optimization with adaptive parameters. Adaptive control strategies are developed for the inertia weight and acceleration coefficients for faster convergence speed
