Modified crayfish optimization algorithm for solving multiple engineering application problems

Jia H., Zhou X., Zhang J., Abualigah L., YILDIZ A. R., Hussien A. G.

Artificial Intelligence Review, cilt.57, sa.5, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 57 Sayı: 5
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1007/s10462-024-10738-x
  • Dergi Adı: Artificial Intelligence Review
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, Educational research abstracts (ERA), Index Islamicus, INSPEC, Library and Information Science Abstracts, Library, Information Science & Technology Abstracts (LISTA), Metadex, Psycinfo, zbMATH, Civil Engineering Abstracts
  • Anahtar Kelimeler: Constrained engineering design problems, Crayfish Optimization Algorithm, Environmental updating mechanism, Ghost opposition-based learning strategy, Global optimization problem, High dimensional feature selection
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

Crayfish Optimization Algorithm (COA) is innovative and easy to implement, but the crayfish search efficiency decreases in the later stage of the algorithm, and the algorithm is easy to fall into local optimum. To solve these problems, this paper proposes an modified crayfish optimization algorithm (MCOA). Based on the survival habits of crayfish, MCOA proposes an environmental renewal mechanism that uses water quality factors to guide crayfish to seek a better environment. In addition, integrating a learning strategy based on ghost antagonism into MCOA enhances its ability to evade local optimality. To evaluate the performance of MCOA, tests were performed using the IEEE CEC2020 benchmark function and experiments were conducted using four constraint engineering problems and feature selection problems. For constrained engineering problems, MCOA is improved by 11.16%, 1.46%, 0.08% and 0.24%, respectively, compared with COA. For feature selection problems, the average fitness value and accuracy are improved by 55.23% and 10.85%, respectively. MCOA shows better optimization performance in solving complex spatial and practical application problems. The combination of the environment updating mechanism and the learning strategy based on ghost antagonism significantly improves the performance of MCOA. This discovery has important implications for the development of the field of optimization. Graphical Abstract: (Figure presented.)