Fuzzy Adaptive Charged System Search for global optimization

Siamak Talatahari, Mahdi Azizi, Mehdi Toloo*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


This study proposes a new fuzzy adaptive Charged System Search (CSS) for global optimization. The suggested algorithm includes a parameter tuning process based on fuzzy logic with the aim of improving its performance. In this regard, four linguistic variables are defined which configures a fuzzy system for parameter identification of the standard CSS algorithm. This process provides a focus for the algorithm on higher levels of global searching in the initial iterations while the local search is considered in the last iterations. Twenty mathematical benchmark functions, the Competitions on Evolutionary Computation (CEC) regarding CEC 2020 benchmark, three well-known constrained, and two engineering problems are utilized to validate the new algorithm. Moreover, the performance of the new algorithm is compared and contrasted with other metaheuristic algorithms. The obtained results reveal the superiority of the proposed approach in dealing with different unconstraint, constrained, and engineering design problems.

Original languageEnglish
Article number107518
JournalApplied Soft Computing
Publication statusPublished - Sept 2021


  • Charged System Search
  • Fuzzy adaptive
  • Global optimization
  • Metaheuristic
  • Optimization

ASJC Scopus subject areas

  • Software

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