The Opposition-based Harmony Search Algorithm

This paper proposes a novel approach to accelerate the harmony search (HS) algorithm. The proposed opposition-based HS of the present work employs opposition-based learning for harmony memory initialization and also for the generation jumping. In the present work, opposite numbers have been utilized to improve the convergence rate of the HS. The potential of the proposed algorithm is assessed by means of an extensive comparative study of numerical results on benchmark test functions. The results obtained confirm the potential and effectiveness of the proposed algorithm compared to some other algorithms surfaced in the recent state-of-the art literatures. Additionally, the opposition concept has been incorporated in an improved variant of HS such as local-best HS algorithm with dynamic subpopulations and the potential of incorporation of opposition concept in evolutionary optimization algorithm is established.