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A distance determination wolf pack algorithm for solving high-dimensional complex functions and its application
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DOI：10.1007/s11227-025-07306-7.png)
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Abstract
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The wolf pack algorithm (WPA), a swarm intelligence method inspired by wolf hunting behaviors, faces limitations in convergence accuracy, computational efficiency, and local optima avoidance. This paper proposes a Distance Determination Wolf Pack Algorithm (DDWPA) to address these challenges. Key innovations include: (1) An initial wandering position recording mechanism to prevent premature local convergence; (2) a distance judgment factor integrated into the summoning phase for accelerated population evolution; and (3) a head wolf relative positioning strategy and distance-updated regeneration operator to enhance global search capability. The algorithm's convergence is theoretically verified through Markov process analysis. Extensive evaluations on CEC2022 and CEC2020 benchmark functions demonstrate DDWPA's superiority over existing swarm intelligence algorithms in terms of solution precision and convergence speed. Four engineering case studies further validate its effectiveness in handling high-dimensional real-world optimization problems. Results consistently show that DDWPA achieves superior global convergence and computational efficiency while maintaining robustness against local optima traps.
Keywords:
Wolf pack algorithm
Wander threshold
Population-based optimization
Distance determination factor
High-dimensional function optimization
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