This paper presents a variant of harmony search algorithm (HS), called best-worst-mean harmony search (BWM_HS). The main difference between the proposed algorithm and the canonical HS is that it employs a modified memory consideration procedure to utilize more efficiently the accumulated knowledge and experience in harmony memory (HM). To this aim, the random harmony selection scheme of this procedure is replaced with three novel pitch selection and production rules. These rules use the information of the current best and worst harmonies as well as the mean of all harmonies to guide the search process. To further utilize the valuable information of HM, two new harmonies are generated at each iteration where the better one will compete with the current worst harmony. The mean of all harmonies is always employed to produce a new harmony. On the other hand, each pitch of the second one is obtained by the rules that consider the information of the best and worst harmonies. These rules can present either explorative or exploitative search behaviors at different stages of search. Thus, a probabilistic self-adaptive selection scheme decides to choose between them to properly balance the exploration and exploitation abilities. The general performance of BWM_HS for solving optimization problems is evaluated against CEC 2017 benchmark functions and its results are compared with HS and eight state-of-the-art variants of HS. The comparison indicates that the performance of BWM_HS is better than or equal to the compared algorithms with respect to the accuracy, robustness, and convergence speed criteria. Moreover, the performance of BWM_HS in solving clustering problems is investigated by applying it for clustering several well-known benchmark datasets. The experimental results show that, in general, the BWM_HS outperforms other well-known algorithms in the literature and in particular, it significantly improves the statistical results for one dataset. (C) 2020 Elsevier B.V. All rights reserved.
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