WSEAS Transactions on Systems
Print ISSN: 1109-2777, E-ISSN: 2224-2678
Volume 12, 2013
Orthogonal Permutation Particle Swarm Optimizer with Switching Learning Strategy for Global Optimization
Authors: , ,
Abstract: This paper aims to improve the performance of original particle swarm optimization (PSO) so that the consequent method can be more robust and statistically sound for global optimization. A variation of PSO called the orthogonal permutation particle swarm optimization (OPPSO) is presented. An orthogonal permutation strategy, based on the orthogonal experimental design, is developed as a metabolic mechanism to enhance the diversity of the whole population, where the energetic offspring generated from the superior group will replace the inferior individuals. In addition, a switching learning strategy is introduced to exploit the particles’ historical experience and drive individuals more efficiently. Seven state-of-the-art PSO variants were adopted for comparison on fifteen benchmark functions. Experimental results and statistical analyses demonstrate a significant improvement of the proposed algorithm.
Search Articles
Keywords: Soft Computing, Particle swarm optimization, Orthogonal experimental design, Learning strategy, Global optimization