Interleaving of particle swarm optimization and differential evolution algorithm for global optimization. Issue 2 (2nd July 2016)

• Record Type: Journal Article
• Authors: Jana, Nanda Dulal; Sil, Jaya
• Is Part Of: International journal of computers and applications. Volume 38:Issue 2/3(2016)
• Page Start: 116
• Page End: 133
• Publication Date: 2016-07-02
• Subjects: Hybridization -- particle swarm optimization (PSO) -- differential evolution (DE) -- adaptive polynomial mutation -- trigonometry mutation -- exploration and exploitation; Computers -- Periodicals; Computer software -- Periodicals; Computer networks -- Periodicals; Multimedia systems -- Periodicals; Internet -- Periodicals; World Wide Web -- Periodicals; Minicomputers -- Periodicals; Microcomputers -- Periodicals; 004.05
• DOI: 10.1080/1206212X.2016.1218242
• Languages: English
• ISSNs: 1206-212X

Abstract

Abstract : Stochastic optimization algorithms have potential to solve optimization problems in various fields of engineering and science. However, increasing non-linearity, non convexity, multi-modality, discontinuity, and even dynamics make the problems more complex and intractable. Classical optimization techniques are not able to determine global solution by analyzing rough non-linear surfaces. Heuristic algorithms have been used for determining global solution for this type of problems. However, heuristic algorithm is knowledge dependent, so finding a unique heuristic optimization algorithm for obtaining optimum solutions for all problems is not feasible. Hybridization is an integrated framework where merits of algorithms are utilized to improve performance of the optimizers. Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithm are two heuristic algorithms despite certain shortcomings have been applied to solve global optimization problems. In this paper, we propose an integrated framework where improved version of PSO and DE (IPSODE) is executed in interleaved fashion for balancing exploration and exploitation dilemma in the evolution process. In IPSODE algorithm, generation starts with improved PSO and switch to either improved DE or continue with improved PSO based on the fitness value. The algorithm is experimented on 20 benchmark/test functions which are uni modal, multi-modal, shifted, and rotated with 10, 20, and 30 different dimensions. The performance of the proposed method is confirmed by comparing with basic PSO, basic DE, and three hybridization methods of PSO and DE based on evaluation criteria like solution quality, robustness, convergence speed, scalability, and statistical t -test.