Enhanced superposition determination for weighted superposition attraction algorithm

BAYKASOĞLU A., AKPINAR Ş.

SOFT COMPUTING, cilt.24, sa.19, ss.15015-15040, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 24 Sayı: 19
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1007/s00500-020-04853-4
  • Dergi Adı: SOFT COMPUTING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, zbMATH
  • Sayfa Sayıları: ss.15015-15040
  • Anahtar Kelimeler: WSA algorithm, Superposition principle, Performance enhancement, Functional optimization, PARTICLE SWARM OPTIMIZATION, HARMONY SEARCH ALGORITHM, ENGINEERING OPTIMIZATION, DESIGN OPTIMIZATION, GLOBAL OPTIMIZATION, EVOLUTIONARY OPTIMIZATION, INTELLIGENCE ALGORITHM, GENETIC ALGORITHM, SIMULATION, CHAOS
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

This paper argues the efficiency enhancement study of a recent meta-heuristic algorithm, WSA, by modifying one of its operators, superposition (target point) determination procedure. The original operator is based on the weighted vector summation and has some potential disadvantages with regard to domain of the decision variables such that determining a superposition out of the search space. Such potential disadvantages may cause WSA to behave as a random search and result in an unsatisfactory performance for some problems. In order to eliminate such potential disadvantages, we propose a new superposition determination procedure for the WSA algorithm. Thus, the mWSA algorithm will be able to behave more consistent during its search and its robustness will improve significantly in comparison to its original version. The mWSA algorithm is compared against the WSA algorithm and some other algorithms taken from the existing literature on both the constrained and unconstrained optimization problems. The experimental results clearly indicate that the mWSA algorithm is an improvement for the original WSA algorithm, and also prove that the mWSA algorithm is more robust and consistent search procedure in solving complex optimization problems.