An enhanced particle swarm optimization with levy flight for global optimization

• Published in: Applied soft computing Vol. 43; pp. 248 - 261
• Main Authors: Jensi, R., Jiji, G. Wiselin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2016
• Subjects: Global optimization; Levy flight; Nature-inspired strategy; Particle swarm optimization; Global optimization; Nature-inspired strategy; Levy flight; Particle swarm optimization
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2016.02.018

•Enhanced PSO with levy flight.•Random walk of the particles.•High convergence rate.•Provides solution accuracy and robust. Hüseyin Haklı and Harun Uguz (2014) proposed a novel approach for global function optimization using particle swarm optimization with levy flight (LFPSO) [Hüseyin Haklı, Harun U guz, A novel particle swarm optimization algorithm with levy flight. Appl. Soft Comput. 23, 333–345 (2014)]. In our study, we enhance the LFPSO algorithm so that modified LFPSO algorithm (PSOLF) outperforms LFPSO algorithm and other PSO variants. The enhancement involves introducing a levy flight method for updating particle velocity. After this update, the particle velocity becomes the new position of the particle. The proposed work is examined on well-known benchmark functions and the results show that the PSOLF is better than the standard PSO (SPSO), LFPSO and other PSO variants. Also the experimental results are tested using Wilcoxon's rank sum test to assess the statistical significant difference between the methods and the test proves that the proposed PSOLF method is much better than SPSO and LFPSO. By combining levy flight with PSO results in global search competence and high convergence rate.