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

Di-Wen Kang1, Li-Ping Mo1, Fang-Ling Wang1

  • 1College of Information Science and Engineering, Jishou Unversity, Jishou 416000, China.

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|July 2, 2021
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Summary
This summary is machine-generated.

An improved adaptive harmony search algorithm (AHS-DE-OBL) enhances fine-tuning and convergence speed. This novel approach uses differential evolution and opposition-based learning to avoid local optima and improve global search capabilities.

Keywords:
adaptive adjustment strategydifferential evolutionharmony search algorithmopposition-based learningoptimization

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristic Computing

Background:

  • The standard harmony search (HS) algorithm suffers from limitations including poor fine-tuning, slow convergence, and a tendency to get stuck in local optima.
  • Addressing these drawbacks is crucial for enhancing the performance of HS in complex optimization problems.

Purpose of the Study:

  • To introduce a novel adaptive harmony search algorithm, termed AHS-DE-OBL, that integrates differential evolution and opposition-based learning.
  • To enhance the fine-tuning ability, accelerate convergence speed, and prevent local optima entrapment in the harmony search algorithm.

Main Methods:

  • Incorporation of differential evolution principles to perturb individuals within the population, boosting fine-tuning capabilities.
  • Adaptive adjustment of the search domain to expedite algorithm convergence.
  • Implementation of an opposition-based learning strategy to mitigate the risk of premature convergence to local optima.

Main Results:

  • The proposed AHS-DE-OBL algorithm demonstrated superior global search ability compared to existing improved harmony search algorithms.
  • Experimental results indicated a significantly faster convergence speed for AHS-DE-OBL.
  • The algorithm effectively overcame the limitations of the standard HS, including improved local optimum avoidance.

Conclusions:

  • The AHS-DE-OBL algorithm represents a significant advancement over the traditional harmony search method.
  • The synergistic integration of differential evolution and opposition-based learning effectively addresses key limitations of HS.
  • This enhanced algorithm offers a promising solution for complex optimization tasks requiring robust global search and rapid convergence.