A local-best harmony search algorithm with dynamic subpopulations

This article presents a local-best harmony search algorithm with dynamic subpopulations (DLHS) for solving the bound-constrained continuous optimization problems. Unlike existing harmony search algorithms, the DLHS algorithm divides the whole harmony memory (HM) into many small-sized sub-HMs and the evolution is performed in each sub-HM independently. To maintain the diversity of the population and to improve the accuracy of the final solution, information exchange among the sub-HMs is achieved by using a periodic regrouping schedule. Furthermore, a novel harmony improvisation scheme is employed to benefit from good information captured in the local best harmony vector. In addition, an adaptive strategy is developed to adjust the parameters to suit the particular problems or the particular phases of search process. Extensive computational simulations and comparisons are carried out by employing a set of 16 benchmark problems from the literature. The computational results show that, overall, the proposed DLHS algorithm is more effective or at least competitive in finding near-optimal solutions compared with state-of-the-art harmony search variants.