Scholarlink

<p>The backtracking search algorithm (BSA) is a popular metaheuristic algorithm known for its simplicity and effectiveness in solving complex optimization problems. However, like many other algorithms, BSA has some weaknesses, such as getting stuck in local optima and converging too quickly to suboptimal solutions. To address these issues, this study introduces a new variant of BSA called the combined power mutation-based backtracking search algorithm (CPMBSA). The performance of the proposed CPMBSA approach is enhanced through the integration of a modified power mutation operator, which strengthens the exploration capability, and a combined mutation operator. This can improve convergence while preserving population diversity. These modifications help CPMBSA achieve a better balance of exploration and exploitation during the search. The CPMBSA has been tested on 86 benchmark functions to verify its performance and robustness. In addition, the algorithm has been applied to six real-world chemical engineering optimization problems and two engineering design optimization problems to evaluate its practical use in complex design challenges. The analysis of results shows that CPMBSA performs better than the original BSA and other advanced metaheuristic algorithms in terms of solution accuracy, convergence speed, and stability. The present research demonstrates that the proposed CPMBSA is a powerful optimization tool for both theoretical benchmark test problems and real-world engineering applications.</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

Backtracking Search Algorithm with Mutation Strategy for Engineering Optimization

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月3日

查看全文
  • Sanjib Debnath,
  • Swapan Debbarma,
  • Sukanta Nama,
  • Apu Kumar Saha,
  • Ghanshyam G. Tejani,
  • Seyed Jalaleddin Mousavirad

摘要

The backtracking search algorithm (BSA) is a popular metaheuristic algorithm known for its simplicity and effectiveness in solving complex optimization problems. However, like many other algorithms, BSA has some weaknesses, such as getting stuck in local optima and converging too quickly to suboptimal solutions. To address these issues, this study introduces a new variant of BSA called the combined power mutation-based backtracking search algorithm (CPMBSA). The performance of the proposed CPMBSA approach is enhanced through the integration of a modified power mutation operator, which strengthens the exploration capability, and a combined mutation operator. This can improve convergence while preserving population diversity. These modifications help CPMBSA achieve a better balance of exploration and exploitation during the search. The CPMBSA has been tested on 86 benchmark functions to verify its performance and robustness. In addition, the algorithm has been applied to six real-world chemical engineering optimization problems and two engineering design optimization problems to evaluate its practical use in complex design challenges. The analysis of results shows that CPMBSA performs better than the original BSA and other advanced metaheuristic algorithms in terms of solution accuracy, convergence speed, and stability. The present research demonstrates that the proposed CPMBSA is a powerful optimization tool for both theoretical benchmark test problems and real-world engineering applications.