Abstract
The Harmony Search (HS) method is an emerging meta-heuristic optimization algorithm, which has been widely employed to deal with various optimization problems during the past decade. However, like most of the evolutionary computation techniques, it sometimes suffers from a rather slow search speed, and even fails to find the global optima in an efficient way. In this paper, a new HS method with dual memory, namely DUAL-HS, is proposed and studied. The secondary memory in the DUAL-HS takes advantage of the Opposition-Based Learning (OBL) to evolve so that the quality of all the harmony memory members can be significantly improved. Optimization of 25 typical benchmark functions demonstrate that compared with the regular HS method, our DUAL-HS has an enhanced convergence property.
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