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<p>In practical optimization, exploring complex search spaces and avoiding local optima demand new strategies. This study presents an improved version of the bio-inspired electric eel foraging optimization (EEFO), which mimics the foraging behavior of electric eels. Although effective, standard EEFO may face slow convergence and risk getting trapped in local optima, particularly in complex and multimodal problems. To overcome these challenges, we propose a hybrid approach by integrating enhanced opposition-based learning (EOBL), which enhances exploration and exploitation capabilities without relying on gradient information and accelerates convergence. The key advantage of EOBL-EEFO is its adaptive mechanism, which expands the search area to prevent early convergence and improve solution accuracy. The proposed EOBL-EEFO algorithm is thoroughly evaluated on CEC2005 and CEC2019 benchmark functions, as well as five practical engineering design problems. Statistical analyses, including the Wilcoxon rank-sum test, demonstrate that EOBL-EEFO performs better than standard algorithms, offering greater reliability and effectiveness across various optimization problems.</p>

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Enhanced opposition-based electric eel foraging optimization for global optimization and engineering problems

  • 期刊论文

  • 发表日期：

    2025年2月16日

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  • P. Rajasekar,
  • M. Jayalakshmi

摘要

In practical optimization, exploring complex search spaces and avoiding local optima demand new strategies. This study presents an improved version of the bio-inspired electric eel foraging optimization (EEFO), which mimics the foraging behavior of electric eels. Although effective, standard EEFO may face slow convergence and risk getting trapped in local optima, particularly in complex and multimodal problems. To overcome these challenges, we propose a hybrid approach by integrating enhanced opposition-based learning (EOBL), which enhances exploration and exploitation capabilities without relying on gradient information and accelerates convergence. The key advantage of EOBL-EEFO is its adaptive mechanism, which expands the search area to prevent early convergence and improve solution accuracy. The proposed EOBL-EEFO algorithm is thoroughly evaluated on CEC2005 and CEC2019 benchmark functions, as well as five practical engineering design problems. Statistical analyses, including the Wilcoxon rank-sum test, demonstrate that EOBL-EEFO performs better than standard algorithms, offering greater reliability and effectiveness across various optimization problems.