4.5 Article

Adaptive harmony search algorithm utilizing differential evolution and opposition-based learning

期刊

MATHEMATICAL BIOSCIENCES AND ENGINEERING
卷 18, 期 4, 页码 4226-4246

出版社

AMER INST MATHEMATICAL SCIENCES-AIMS
DOI: 10.3934/mbe.2021212

关键词

harmony search algorithm; differential evolution; opposition-based learning; adaptive adjustment strategy; optimization

资金

  1. Natural Science Foundation of Hunan Province, China [2019JJ40234, 2020JJ5458]
  2. Research Foundation of Education Bureau of Hunan Province, China [19A414, 18B317]
  3. Special Project of Language and Writing Application Research of Hunan Provincial Language Commission [XYJ2019GB09]
  4. National Natural Science Foundation of China [62066016]
  5. Jishou University Graduate Research and Innovation Project [JGY202032]

向作者/读者索取更多资源

The AHS-DE-OBL algorithm proposed in this study utilizes innovative strategies such as differential evolution, adaptive adjustment of search domain, and opposition-based learning to improve upon the limitations of the harmony search algorithm, resulting in better global search ability and faster convergence speed.
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.

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