Dynamic opposition learning enhanced attaining and refining knowledge-based optimization algorithm for application to engineering design problems
Journal of Computational and Applied Mathematics, cilt.484, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 484
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.cam.2026.117493
- Dergi Adı: Journal of Computational and Applied Mathematics
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Compendex, INSPEC, MathSciNet, zbMATH, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO)
- Anahtar Kelimeler: ARKO, Dynamic oppositional based learning, Engineering design problems, Metaheuristic algorithms, Optimization
- Bursa Uludağ Üniversitesi Adresli: Evet
Özet
Achieving the right trade-off between exploration and exploitation is a critical hurdle in nature-inspired optimization algorithms, particularly when addressing complex, real-world global optimization problems. Maintaining this balance allows the algorithm's agents to efficiently investigate unexplored regions of the search space. The Attaining and Refining Knowledge-Based Optimization Algorithm (ARKO) is a recently developed metaheuristic that draws inspiration from human learning behavior, utilizing shared knowledge across the population to guide the search process. However, ARKO sometimes fails to locate the best solutions, getting trapped in suboptimal points, which results in early convergence and obstructs the discovery of global optima. To overcome these shortcomings, this research introduces an improved version called, DOL (Dynamic Opposition Learning) inspired Attaining-Refining Knowledge based Optimization (DARKO), aimed at enhancing the interplay between exploration and exploitation while avoiding local traps. The approach strengthens ARKO's exploratory capacity by incorporating Dynamic Oppositional Learning (DOL) that ensures a harmonious balance between the two phases by using its dynamic search process. With its adaptable search domain and minimal computational overhead, the DOL-inspired ARKO mechanism boosts exploration efficiency and supports robust convergence toward superior solutions, effectively curbing premature convergence. The effectiveness of DARKO is assessed using the widely recognized IEEE CEC 2019 and IEEE CEC 2022 benchmark suites, complemented by three practical engineering applications. Comparative analysis with established metaheuristic methods, supported by quantitative data, demonstrates DARKO's remarkable proficiency and optimization ability.