Article detail · 2025
Prioritized Multi-Step Decision-Making Gray Wolf Optimization Algorithm for Engineering Applications
- Year
- 2025
- Type
- article
Data source split
- YÖKSİS YÖKSİS article record
- YÖKSİS venue ENGINEERING REPORTS
- Catalog match (ISSN) Engineering Reports
- OpenAlex OpenAlex enrichment (abstract, citations, topics)
Abstract
English (OpenAlex)
ABSTRACT This article introduces the Prey‐Movement Strategy Gray Wolf Optimizer (PMS‐GWO), an enhanced version of the Gray Wolf Optimizer (GWO) designed to improve optimization efficiency through a novel multi‐step decision‐making process. By integrating adaptive exploration–exploitation strategies, PMS‐GWO dynamically manages leadership roles, balances local and global searches, and introduces a prey escape mechanism, significantly improving solution diversity. Comparative analysis across 23 benchmark functions demonstrates PMS‐GWO's superior performance, achieving up to 28.6% faster convergence and a 55.5%–93.8% increase in solution accuracy compared to the standard GWO. Notably, PMS‐GWO enhances computational efficiency by 21.7%–27.4% and shows a 168.8% improvement in solution accuracy for the complex Michalewicz function over the baseline GWO. Visual convergence speed analysis, evidenced by a rapid fitness value decline within 100 iterations, reveals PMS‐GWO's quickest convergence time of 0.02 s among tested algorithms. Furthermore, a comparison of runtime for several algorithms, including PMS‐GWO, MMCCS‐GWO, CC‐GWO, MGWO, and GWO, clearly indicates that PMS‐GWO achieves the lowest runtime of 2.364 s, significantly faster than CC‐GWO and MGWO, which both exceed 5 s. This visual representation highlights the computational efficiency of PMS‐GWO compared to other algorithms. PMS‐GWO also outperforms advanced GWO variants like MMSCC‐GWO, MGWO, and CCS‐GWO, particularly in complex optimization landscapes, highlighting its adaptability and effectiveness for real‐world applications in energy systems and engineering design. The multi‐step decision‐making process implemented in PMS‐GWO is critical to achieving these improved convergence and diversity metrics.
Topics
- Metaheuristic Optimization Algorithms Research
- Advanced Multi-Objective Optimization Algorithms
- Advanced Optimization Algorithms Research
Primary topic Metaheuristic Optimization Algorithms Research