Title :
A Self-Adaptive Particle Swarm Optimization Algorithm
Author :
Li, Xiufen ; Fu, Hongjie ; Zhang, Changsheng
Author_Institution :
Coll. of Comput. Sci. & Technol., Jilin Teachers Inst. of Eng. & Technol., Changchun
Abstract :
To combat the problem of premature convergence observed in many applications of PSO, a novel self-adaptive particle swarm optimization algorithm-SAPSO is proposed in this paper. There exist two states for each particle in the SAPSO algorithm and a metric to measure a particlepsilas activity is defined which is used to choose which state it would reside. In order to balance a particlepsilas exploration and exploitation capability for different evolving phase, a self-adjusted inertia weight which varies dynamically with each particlepsilas evolution degree and the current swarm evolution degree is introduced into SAPSO algorithm. Simulation and comparisons based on several well-studied non-noisy problems and noisy problems demonstrate the effectiveness, efficiency and robustness of the proposed algorithm.
Keywords :
convergence; evolutionary computation; particle swarm optimisation; evolutionary algorithm; premature convergence; self-adaptive particle swarm optimization algorithm; self-adjusted inertia weight; Application software; Computational modeling; Computer science; Educational institutions; Noise robustness; Particle measurements; Particle swarm optimization; Simulated annealing; Software algorithms; Software engineering; PSO; inertia weight; noise optimization; swarm;
Conference_Titel :
Computer Science and Software Engineering, 2008 International Conference on
Conference_Location :
Wuhan, Hubei
Print_ISBN :
978-0-7695-3336-0
DOI :
10.1109/CSSE.2008.142