Improved harmony search algorithm with perturbation strategy

Abstract

Harmony search (HS) algorithm is a population-based meta-heuristic algorithm, which is conceptualized using the musical improvisation process of searching for a perfect state of harmony. In this paper, an improved harmony search algorithm with perturbation strategy is proposed to enhance the global and local search ability of HS algorithm. A perturbation strategy is presented to improve global search capability. Local opposition-based learning is used to replace pitch adjustment, which aims to enhance local search ability. In addition, elite memory is designed to further escape local minima. Numerical results indicated that the proposed IHSP algorithm has better performance than the state-of-the-art HS algorithms.