A self-adaptive harmony PSO search algorithm and its performance analysis. Issue 21 (30th November 2015)

• Record Type: Journal Article
• Authors: Zhao, Fuqing; Liu, Yang; Zhang, Chuck; Wang, Junbiao
• Is Part Of: Expert systems with applications. Volume 42:Issue 21(2015)
• Page Start: 7436
• Page End: 7455
• Publication Date: 2015-11-30
• Subjects: Harmony Search algorithm -- PSO algorithm -- Self-adaptive scheme -- Mutation strategy -- Markov model; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2015.05.035
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A self-adaptive harmony particle swarm optimization search algorithm is proposed. PSO algorithm is utilized to initial the harmony memory (HM). Pitch adjusting rate (PAR) and distance bandwidth (bw), are adjusted dynamically. A Gaussian mutation operator is added to reinforce the robustness. The convergence of the SHPSOS algorithm has been proved theoretically. Abstract: Harmony Search (HS) algorithm is a new population-based meta-heuristic which imitates the music improvisation process and has been successfully applied to a variety of combination optimization problems. In this paper, a self-adaptive harmony particle swarm optimization search algorithm, named SHPSOS, is proposed to solve global continuous optimization problems. Firstly, an efficient initialization scheme based on the PSO algorithm is presented for improving the solution quality of the initial harmony memory (HM). Secondly, a new self-adaptive adjusting scheme for pitch adjusting rate (PAR) and distance bandwidth (BW), which can balance fast convergence and large diversity during the improvisation step, are designed. PAR is dynamically adapted by symmetrical sigmoid curve, and BW is dynamically adjusted by the median of the harmony vector at each generation. Meanwhile, a new effective improvisation scheme based on differential evolution and the best harmony (best individual) is developed to accelerate convergence performance and to improve solution accuracy. Besides, Gaussian mutation strategy is presented and embedded in the SHPSOS algorithm to reinforce the robustness and avoid premature convergence in the evolution process of candidates. Finally, the global convergence performance of the SHPSOS is analyzed with the Markov model to testify the stability of algorithm. Experimental results on thirty-two standard benchmark functions demonstrate that SHPSOS outperforms original HS and the other related algorithms in terms of the solution quality and the stability.