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<p>To further improve upon the deficiencies of traditional algorithms in terms of population diversity, convergence accuracy, and speed, this paper introduces a Dynamic Multi-Population Hybrid Metaheuristic Algorithm (DHA). DHA dynamically categorizes the population into Elite, Follower, and Explorer subgroups, applying specific strategies: a novel dimension-wise Gaussian mutation combined with the Sine Cosine Algorithm (SCA) for the Elite, a randomized spiral search for the Explorer, and Lévy flight for the Follower. Rigorous testing on benchmark sets like CEC2005, CEC2017, and CEC2019, alongside practical application in Service Function Chain (SFC) mapping, underscores DHA’s superior performance and applicability.</p>

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A Multi-group Meta-heuristic Optimization with Dynamic Population Partition and Hybrid Strategies: Algorithm and Applications

  • 期刊论文

  • 发表日期：

    2025年4月2日

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  • Dongshuai Niu,
  • Guangwen Yi,
  • Long Chen,
  • Zhenzhou Tang

摘要

To further improve upon the deficiencies of traditional algorithms in terms of population diversity, convergence accuracy, and speed, this paper introduces a Dynamic Multi-Population Hybrid Metaheuristic Algorithm (DHA). DHA dynamically categorizes the population into Elite, Follower, and Explorer subgroups, applying specific strategies: a novel dimension-wise Gaussian mutation combined with the Sine Cosine Algorithm (SCA) for the Elite, a randomized spiral search for the Explorer, and Lévy flight for the Follower. Rigorous testing on benchmark sets like CEC2005, CEC2017, and CEC2019, alongside practical application in Service Function Chain (SFC) mapping, underscores DHA’s superior performance and applicability.