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<p>This paper presents the Relationship-Inspired Evolutionary Algorithm (RIEA), a novel optimization method inspired by interpersonal dynamics such as trust, deception, and allegiance. RIEA models agent interactions within male–female pairs to guide adaptive exploration and exploitation. Core mechanisms include trust-based crossover, elite-influenced blending, and allegiance-modulated learning. Experiments on the CEC 2022 benchmark suite demonstrate that RIEA outperforms numerous standard nature-inspired optimization algorithms in 10D problems and remains competitive in 20D cases. The algorithm’s dynamic behaviour, rooted in human relational traits, offers a new direction in nature-inspired optimization, combining stability, diversity, and responsiveness. These results establish RIEA as a versatile framework for solving complex numerical optimization problems.</p>

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Relationship-Inspired Evolutionary Algorithm (RIEA): leveraging male–female collaborative dynamics for optimization

  • 期刊论文

  • 发表日期：

    2025年11月21日

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  • Debojyoti Sarkar,
  • Anupam Biswas

摘要

This paper presents the Relationship-Inspired Evolutionary Algorithm (RIEA), a novel optimization method inspired by interpersonal dynamics such as trust, deception, and allegiance. RIEA models agent interactions within male–female pairs to guide adaptive exploration and exploitation. Core mechanisms include trust-based crossover, elite-influenced blending, and allegiance-modulated learning. Experiments on the CEC 2022 benchmark suite demonstrate that RIEA outperforms numerous standard nature-inspired optimization algorithms in 10D problems and remains competitive in 20D cases. The algorithm’s dynamic behaviour, rooted in human relational traits, offers a new direction in nature-inspired optimization, combining stability, diversity, and responsiveness. These results establish RIEA as a versatile framework for solving complex numerical optimization problems.