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<p>Metaheuristic methods are extensively applied to tackle difficult continuous optimization problems in science and engineering. But many traditional methods still face challenges in achieving a balance between exploration and exploitation, which may result in premature convergence and local optima. This paper introduces the Father-Inspired Metaheuristic (FIM), a human-based hybrid metaheuristic inspired by a father’s guidance to children through discipline, learning, reward, and correction. The proposed FIM is organized into three main phases. The first phase is the neighborhood-repulsion-based discipline phase, which identifies weak local neighborhoods and avoids solutions going to weak nearby solutions, while still being guided by the father solution. The second phase is a hybrid of self-learning and elite guidance, where a solution either performs an adaptive perturbation around its current position or follows an elite solution guided by peer-difference information. In the third phase, reward-correction is applied, where a light Quasi-Newton refinement improves promising solutions, and a periodic correction mechanism redirects weak solutions to restore diversity. The algorithm is evaluated on the IEEE CEC2017 benchmark suite in 10D, 30D, and 50D, classical benchmark functions, and engineering design problems. Comparative results against five well-established metaheuristic algorithms show that FIM achieves strong accuracy, stable convergence, and robust performance across different continuous optimization landscapes.</p>

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Father-inspired metaheuristic: a hybrid framework with neighborhood repulsion and elite learning for continuous optimization

  • 期刊论文

  • 发表日期：

    2026年7月9日

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  • Muhammad Asad Lal,
  • Muhammad Kashif,
  • Bo Huang

摘要

Metaheuristic methods are extensively applied to tackle difficult continuous optimization problems in science and engineering. But many traditional methods still face challenges in achieving a balance between exploration and exploitation, which may result in premature convergence and local optima. This paper introduces the Father-Inspired Metaheuristic (FIM), a human-based hybrid metaheuristic inspired by a father’s guidance to children through discipline, learning, reward, and correction. The proposed FIM is organized into three main phases. The first phase is the neighborhood-repulsion-based discipline phase, which identifies weak local neighborhoods and avoids solutions going to weak nearby solutions, while still being guided by the father solution. The second phase is a hybrid of self-learning and elite guidance, where a solution either performs an adaptive perturbation around its current position or follows an elite solution guided by peer-difference information. In the third phase, reward-correction is applied, where a light Quasi-Newton refinement improves promising solutions, and a periodic correction mechanism redirects weak solutions to restore diversity. The algorithm is evaluated on the IEEE CEC2017 benchmark suite in 10D, 30D, and 50D, classical benchmark functions, and engineering design problems. Comparative results against five well-established metaheuristic algorithms show that FIM achieves strong accuracy, stable convergence, and robust performance across different continuous optimization landscapes.