Music-Inspired Harmony Search Algorithm: Theory and Applications
Music-Inspired Harmony Search Algorithm: Theory and Applications
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
Survey A review of opposition-based learning from 2005 to 2012
Engineering Applications of Artificial Intelligence
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Harmony search (HS), mimicking the musician's improvisation behavior, has demonstrated strong efficacy in optimization. To deal with the deficiencies in the original HS, a dynamic regional harmony search (DRHS) algorithm with opposition and local learning is proposed. DRHS utilizes opposition-based initialization, and performs independent harmony searches with respect to multiple groups created by periodically regrouping the harmony memory. An opposition-based harmony creation scheme is used in DRHS to update each group memory. Any prematurely converged group is restarted with its size being doubled to enhance exploration. Local search is periodically applied to exploit promising regions around top-ranked candidate solutions. DRHS consistently outperforms HS on 12 numerical test problems from the CEC2005 benchmark at both 10D and 30D.