Improved Harmony Search Algorithm: LHS

Display OmittedThe improvisation process of LHS algorithm. Opposition-based learning (OBL) technique is employed in improvisation process. The purpose is to increase the diversity of solution.The current best harmony and worst harmony are used to adjust the parameter BW. An adaptive global pitch adjustment is designed to enhance the exploitation ability of solution space.In the proposed algorithm, a new harmony and its opposite harmony are generated in iteration. Then a competition selection mechanism is established to improve solution precision.The effects that varying the parameter HMS and HMCR have on the performance of the LHS algorithm is also analyzed in detail. In this paper, we propose an improved harmony search algorithm named LHS with three key features: (i) adaptive global pitch adjustment is designed to enhance the exploitation ability of solution space; (ii) opposition-based learning technique is blended to increase the diversity of solution; (iii) competition selection mechanism is established to improve solution precision and enhance the ability of escaping local optima. The performance of the LHS algorithm with respect to harmony memory size (HMS) and harmony memory considering rate (HMCR) are also analyzed in detail. To further evaluate the performance of the proposed LHS algorithm, comparison with ten state-of-the-art harmony search variants over a large number of benchmark functions with different characteristics is carried out. The numerical results confirm the superiority of the proposed LHS algorithm in terms of accuracy, convergence speed and robustness.

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