Scholarlink

<p>This study introduces DSHOA, a novel bio-inspired metaheuristic for solving the NP-hard Quadratic Assignment Problem (QAP). The algorithm combines local search mechanisms with crossover operators to improve both exploration and exploitation, guaranteeing better solution quality and computational efficiency, particularly for large-scale instances. Empirical evaluations on standardized QAP reference instances show that DSHOA outperforms state-of-the-art algorithms in terms of accuracy and efficiency. A detailed analysis was carried out to assess the impact of key algorithmic factors, such as search intensification, diversification, and convergence rate. The results confirm that integrating hybridized local search into a bio-inspired approach improves the quality of QAP solutions, demonstrating DSHOA’s effectiveness as a robust optimization method.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A spotted hyena algorithm for quadratic allocation problems: a novel bio-inspired solution

  • 期刊论文

  • 发表日期：

    2025年7月4日

查看全文
  • Toufik Mzili,
  • Mourad Mzili,
  • Ahmed Abatal,
  • Adarsh Kumar Arya,
  • Nebojsa Bacanin

摘要

This study introduces DSHOA, a novel bio-inspired metaheuristic for solving the NP-hard Quadratic Assignment Problem (QAP). The algorithm combines local search mechanisms with crossover operators to improve both exploration and exploitation, guaranteeing better solution quality and computational efficiency, particularly for large-scale instances. Empirical evaluations on standardized QAP reference instances show that DSHOA outperforms state-of-the-art algorithms in terms of accuracy and efficiency. A detailed analysis was carried out to assess the impact of key algorithmic factors, such as search intensification, diversification, and convergence rate. The results confirm that integrating hybridized local search into a bio-inspired approach improves the quality of QAP solutions, demonstrating DSHOA’s effectiveness as a robust optimization method.