Multi-strategy and self-adaptive differential sine–cosine algorithm for multi-robot path planning

Akay R., Yildirim M. Y.

EXPERT SYSTEMS WITH APPLICATIONS, cilt.232, ss.1-19, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 232
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1016/j.eswa.2023.120849
  • Dergi Adı: EXPERT SYSTEMS WITH APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Computer & Applied Sciences, INSPEC, Metadex, Public Affairs Index, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1-19
  • Anahtar Kelimeler: Multi-robot, Path planning, Sine-cosine algorithm, Multi-strategy, Self-adaptive
  • Erciyes Üniversitesi Adresli: Evet

Özet

Researchers have conducted studies using various metaheuristic algorithms for multi-robot path planning recently. One of these algorithms, sine-cosine algorithm (SCA) cannot produce satisfactory results in path planning problems, due to a single update strategy. It is necessary to adopt multiple update strategies and improve its performance for a wider set of problems. We have proposed a new multi-strategy self-adaptive differential sine-cosine algorithm (sdSCA) that uses a pool of strategies and allows for more frequent selection of strategies that lead to better solutions. Thus, dependency of SCA on a single strategy has been removed and it has become a more stable for a wider set of problems and convergence of SCA is improved. Firstly, effectiveness of sdSCA was tested in CEC2015 benchmark functions and CEC2020 real-world optimization problems. Performance of sdSCA at these tests is satisfactory. Secondly, sdSCA was applied to online multi-robot path planning in complex environments with static and dynamic obstacles. In this path planning simulation, the proposed algorithm achieved an average improvement of 42% compared to SCA. It also appears to produce results superior to state-of-the-art metaheuristic algorithms.