EMoSOA: a new evolutionary multi-objective seagull optimization algorithm for global optimization

Dhiman G., Singh K. K., Slowik A., Chang V., YILDIZ A. R., Kaur A., ...Daha Fazla

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, cilt.12, sa.2, ss.571-596, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 12 Sayı: 2
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1007/s13042-020-01189-1
  • Dergi Adı: INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC
  • Sayfa Sayıları: ss.571-596
  • Anahtar Kelimeler: Seagull Optimization Algorithm, Multi-objective Optimization, Evolutionary, Pareto, Engineering Design Problems, Convergence, Diversity, SPOTTED HYENA OPTIMIZER, COMPUTATIONAL INTELLIGENCE, DESIGN OPTIMIZATION, PLACEMENT, MODEL, COST
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

This study introduces the evolutionary multi-objective version of seagull optimization algorithm (SOA), entitled Evolutionary Multi-objective Seagull Optimization Algorithm (EMoSOA). In this algorithm, a dynamic archive concept, grid mechanism, leader selection, and genetic operators are employed with the capability to cache the solutions from the non-dominatedPareto. The roulette-wheel method is employed to find the appropriate archived solutions. The proposed algorithm is tested and compared with state-of-the-art metaheuristic algorithms over twenty-four standard benchmark test functions. Four real-world engineering design problems are validated using proposedEMoSOAalgorithm to determine its adequacy. The findings of empirical research indicate that the proposed algorithm is better than other algorithms. It also takes into account those optimal solutions from theParetowhich shows high convergence.