Opposition and reinforcement learning growth-starfish optimization algorithm for engineering design and feature selection

Zhong C., Chen H., Xin D., Xu T., Meng Z., Wang X., ...Daha Fazla

Knowledge-Based Systems, cilt.338, 2026 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 338
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1016/j.knosys.2026.115522
  • Dergi Adı: Knowledge-Based Systems
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Library, Information Science & Technology Abstracts (LISTA)
  • Anahtar Kelimeler: Feature selection, Growth optimizer, Opposition-based learning, Reinforcement learning, Starfish optimization algorithm
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

Starfish optimization algorithm (SFOA) is a bio-inspired metaheuristic algorithm for global optimization, which has demonstrated accuracy and efficiency in popular benchmark functions. However, for complex practical problems such as engineering design and feature selection, SFOA still requires a better balance between exploration and exploitation to ensure robust performance in real-world applications. In this paper, we present an improved SFOA algorithm named ORLGSFOA, which integrates opposition-based learning, reinforcement learning, and the growth optimizer with the basic SFOA. The algorithm first incorporates the opposition-based learning strategy during initialization to improve the diversity and quality of the initial solutions. Then, the updating rule from the growth optimizer is hybridized with SFOA to balance exploration and exploitation. Moreover, ORLGSFOA integrates the reinforcement learning strategy to reward the winner from SFOA and growth optimizer by adding updating positions during optimization to enhance global convergence. Experiments demonstrate the superior performance of ORLGSFOA. In comprehensive benchmark tests on 65 functions from classical, CEC2017, and CEC2022 suites, ORLGSFOA outperformed 15 other metaheuristic algorithms by achieving more accurate solutions. Additionally, this effectiveness translates directly to real-world applications, as is evidenced by tests on seven engineering design problems. Besides, the effectiveness of ORLGSFOA in solving discrete combinatorial optimization problems is verified through 52 feature selection problems, and the algorithm is extended to the wind engineering scenarios. In conclusion, ORLGSFOA demonstrates powerful efficacy in addressing a wide range of challenges, including global optimization, engineering design, and feature selection problems. The source code of ORLGSFOA is publicly available at: https://ww2.mathworks.cn/matlabcentral/fileexchange/183223-orlgsfoa.