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., ...More

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, vol.12, no.2, pp.571-596, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 12 Issue: 2
  • Publication Date: 2021
  • Doi Number: 10.1007/s13042-020-01189-1
  • Journal Name: INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC
  • Page Numbers: pp.571-596
  • Keywords: Seagull Optimization Algorithm, Multi-objective Optimization, Evolutionary, Pareto, Engineering Design Problems, Convergence, Diversity, SPOTTED HYENA OPTIMIZER, COMPUTATIONAL INTELLIGENCE, DESIGN OPTIMIZATION, PLACEMENT, MODEL, COST
  • Bursa Uludag University Affiliated: Yes

Abstract

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.