Open AccessAdaptive harmony search algorithm utilizing differential evolution and opposition-based learning
Author(s) -
Di-Wen Kang,
Li-Ping Mo,
Fang-Ling Wang,
Yun Ou
Publication year - 2021
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2021212
Subject(s) - harmony search , differential evolution , local optimum , harmony (music) , algorithm , computer science , local search (optimization) , hill climbing , metaheuristic , mathematical optimization , artificial intelligence , mathematics , art , musical , visual arts
An adaptive harmony search algorithm utilizing differential evolution and opposition-based learning (AHS-DE-OBL) is proposed to overcome the drawbacks of the harmony search (HS) algorithm, such as its low fine-tuning ability, slow convergence speed, and easily falling into a local optimum. In AHS-DE-OBL, three main innovative strategies are adopted. First, inspired by the differential evolution algorithm, the differential harmonies in the population are used to randomly perturb individuals to improve the fine-tuning ability. Then, the search domain is adaptively adjusted to accelerate the algorithm convergence. Finally, an opposition-based learning strategy is introduced to prevent the algorithm from falling into a local optimum. The experimental results show that the proposed algorithm has a better global search ability and faster convergence speed than other selected improved harmony search algorithms and selected metaheuristic approaches.