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.

本研究提出了一种用于全局优化的新的模糊自适应充电系统搜索 (CSS)。所提出的算法包括基于模糊逻辑的参数调整过程，旨在提高其性能。在这方面，定义了四个语言变量，它们配置了用于标准 CSS 算法的参数识别的模糊系统。该过程为算法提供了在初始迭代中更高级别全局搜索的重点，而在最后一次迭代中考虑了局部搜索。利用 20 个数学基准函数、关于 CEC 2020 基准的进化计算竞赛 (CEC)、三个众所周知的约束和两个工程问题来验证新算法。而且，将新算法的性能与其他元启发式算法进行了比较和对比。获得的结果揭示了所提出的方法在处理不同的无约束、有约束和工程设计问题方面的优越性。