A Hybrid Harmony Search Method Based on OBL

Abstract

The Harmony Search (HS) method is an emerging meta-heuristic optimization algorithm. However, like most of the evolutionary computation techniques, it sometimes suffers from a rather slow search speed, and fails to find the global optima in an efficient way. In this paper, we propose and study a hybrid optimization approach, in which the HS is merged together with the Opposition-Based Learning (OBL). Our modified HS, namely HS-OBL, has an improved convergence property. Simulations of 23 typical benchmark problems demonstrate that the HS-OBL can indeed yield a superior optimization performance over the regular HS method.