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<p>This study introduces the Pray Optimization Algorithm (POA), a novel metaheuristic inspired by the procedural rituals of Islamic pray, designed to solve complex engineering and robotic manipulator problems. The mathematical model is structured into three distinct phases: Phase I simulates searching for a suitable mosque; Phase II models congregational alignment (lining up for pray); and Phase III implements a cumulative scoring system to drive convergence. The efficacy of the POA is initially validated on the CEC2017 benchmark suite through comparative analysis with six high-performing metaheuristic algorithms. The algorithm’s statistical superiority is subsequently confirmed using the Wilcoxon rank-sum and Friedman tests. To verify its practical applicability, the POA is deployed to solve three classical real-world engineering optimization problems. Ultimately, the algorithm demonstrates superior performance when compared against five state-of-the-art methods in optimizing the trajectory planning of a 6-DOF industrial robotic arm. These findings substantiate the effectiveness of the proposed POA in navigating constrained, real-world engineering search spaces.</p>

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A novel pray optimization algorithm for six degree of freedom robotic arm trajectory planning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月28日

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  • Assem F. Alabu-Husain,
  • Mostafa A. ElBahloul,
  • Mahmoud M. Saafan,
  • Eman M. El-Gendy

摘要

This study introduces the Pray Optimization Algorithm (POA), a novel metaheuristic inspired by the procedural rituals of Islamic pray, designed to solve complex engineering and robotic manipulator problems. The mathematical model is structured into three distinct phases: Phase I simulates searching for a suitable mosque; Phase II models congregational alignment (lining up for pray); and Phase III implements a cumulative scoring system to drive convergence. The efficacy of the POA is initially validated on the CEC2017 benchmark suite through comparative analysis with six high-performing metaheuristic algorithms. The algorithm’s statistical superiority is subsequently confirmed using the Wilcoxon rank-sum and Friedman tests. To verify its practical applicability, the POA is deployed to solve three classical real-world engineering optimization problems. Ultimately, the algorithm demonstrates superior performance when compared against five state-of-the-art methods in optimizing the trajectory planning of a 6-DOF industrial robotic arm. These findings substantiate the effectiveness of the proposed POA in navigating constrained, real-world engineering search spaces.