Coalition of metaheuristics through parallel computing for solving unconstrained continuous optimization problems

ŞENOL M. E., BAYKASOĞLU A.

ENGINEERING COMPUTATIONS, cilt.39, sa.8, ss.2895-2927, 2022 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 39 Sayı: 8
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1108/ec-10-2021-0612
  • Dergi Adı: ENGINEERING COMPUTATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ABI/INFORM, Aerospace Database, Communication Abstracts, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.2895-2927
  • Anahtar Kelimeler: Metaheuristic algorithms, Parallel computing, Continuous optimization, Weighted superposition attraction-repulsion algorithm, GENETIC ALGORITHM, SEARCH
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

Purpose The purpose of this study is to develop a new parallel metaheuristic algorithm for solving unconstrained continuous optimization problems. Design/methodology/approach The proposed method brings several metaheuristic algorithms together to form a coalition under Weighted Superposition Attraction-Repulsion Algorithm (WSAR) in a parallel computing environment. The proposed approach runs different single solution based metaheuristic algorithms in parallel and employs WSAR (which is a recently developed and proposed swarm intelligence based optimizer) as controller. Findings The proposed approach is tested against the latest well-known unconstrained continuous optimization problems (CEC2020). The obtained results are compared with some other optimization algorithms. The results of the comparison prove the efficiency of the proposed method. Originality/value This study aims to combine different metaheuristic algorithms in order to provide a satisfactory performance on solving the optimization problems by benefiting their diverse characteristics. In addition, the run time is shortened by parallel execution. The proposed approach can be applied to any type of optimization problems by its problem-independent structure.