An improved adaptive binary Harmony Search algorithm

Harmony Search (HS), inspired by the music improvisation process, is a new meta-heuristic optimization method and has been successfully used to tackle the optimization problems in discrete or continuous space. Although the standard HS algorithm is able to solve binary-coded optimization problems, the pitch adjustment operator of HS is degenerated in the binary space, which spoils the performance of the algorithm. Based on the analysis of the drawback of the standard HS, an improved adaptive binary Harmony Search (ABHS) algorithm is proposed in this paper to solve the binary-coded problems more effectively. Various adaptive mechanisms are examined and investigated, and a scalable adaptive strategy is developed for ABHS to enhance its search ability and robustness. The experimental results on the benchmark functions and 0–1 knapsack problems demonstrate that the proposed ABHS is efficient and effective, which outperforms the binary Harmony Search, the novel global Harmony Search algorithm and the discrete binary Particle Swarm Optimization in terms of the search accuracy and convergence speed.