Abstract
The Harmony Search (HS) method is an emerging meta-heuristic optimization algorithm, which has been extensively applied to handle numerous optimization problems during the past decade. However, it usually lacks of an efficient local search capability, and may sometimes suffer from weak convergence. In this paper, a memetic HS method, m-HS, with local search function is proposed and studied. The local search in the m-HS is inspired by the principle of bee foraging, and performs only at selected harmony memory members, which can significantly improve the efficiency of the overall search procedure. Compared with the original HS method, our m-HS has been demonstrated in numerical simulations of 16 typical benchmark functions to yield a superior optimization performance.
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