An adaptive learning particle swarm optimizer for function optimization.
Traditional particle swarm optimization (PSO) suffers from the premature convergence problem, which usually results in PSO being trapped in local optima. This paper presents an adaptive learning PSO (ALPSO) based on a variant PSO learning strategy. In ALPSO, the learning mechanism of each particle is separated into three parts: its own historical best position, the closest neighbor and the global best one. By using this individual level adaptive technique, a particle can well guide its behavior of exploration and exploitation. A set of 21 test functions were used including un-rotated, rotated and composition functions to test the performance of ALPSO. From the comparison results over several variant PSO algorithms, ALPSO shows an outstanding performance on most test functions, especially the fast convergence characteristic.
Citation:Li, C. and Yang, S. (2009) An adaptive learning particle swarm optimizer for function optimization. In: Proceedings of the 2009 IEEE Congress on Evoluationary Computation, Trondheim, 2009. New York: IEEE, pp. 381-388.
Research Group:Centre for Computational Intelligence