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<p>This paper proposes the Dhole-Inspired Optimization (DIO) algorithm, a novel metaheuristic inspired by the cooperative hunting behavior of dholes (<i>Cuon alpinus</i>). The algorithm employs a hierarchical pack structure, where a Lead Vocalizer guides the search process while subordinate members adapt their movements to balance exploration and exploitation dynamically. This structure enhances search efficiency, prevents premature convergence, and improves solution accuracy across different problem landscapes. DIO is benchmarked on unimodal, multimodal, and composite test functions, demonstrating superior performance compared to established optimization algorithms, including Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Gravitational Search Algorithm (GSA), Fast Evolutionary Programming (FEP), and Differential Evolution (DE). The results show that DIO achieves higher accuracy and faster convergence rates on a majority of test cases, validating its robustness and reliability in tackling complex optimization problems. Furthermore, the algorithm is evaluated on real-world engineering applications, demonstrating its adaptability and effectiveness in practical scenarios. The findings highlight DIO as a versatile and competitive optimization approach, suitable for a wide range of applications in science and engineering.</p>

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Dholes-inspired optimization (DIO): a nature-inspired algorithm for engineering optimization problems

  • 期刊论文

  • 发表日期：

    2025年9月19日

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  • Ali El Romeh,
  • Václav Snášel,
  • Seyedali Mirjalili

摘要

This paper proposes the Dhole-Inspired Optimization (DIO) algorithm, a novel metaheuristic inspired by the cooperative hunting behavior of dholes (Cuon alpinus). The algorithm employs a hierarchical pack structure, where a Lead Vocalizer guides the search process while subordinate members adapt their movements to balance exploration and exploitation dynamically. This structure enhances search efficiency, prevents premature convergence, and improves solution accuracy across different problem landscapes. DIO is benchmarked on unimodal, multimodal, and composite test functions, demonstrating superior performance compared to established optimization algorithms, including Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Gravitational Search Algorithm (GSA), Fast Evolutionary Programming (FEP), and Differential Evolution (DE). The results show that DIO achieves higher accuracy and faster convergence rates on a majority of test cases, validating its robustness and reliability in tackling complex optimization problems. Furthermore, the algorithm is evaluated on real-world engineering applications, demonstrating its adaptability and effectiveness in practical scenarios. The findings highlight DIO as a versatile and competitive optimization approach, suitable for a wide range of applications in science and engineering.