ENTERPRISE AI ANALYSIS

Rank Charged System Search Algorithm for Optimization and Operations Research

This paper introduces CSSRank, an improved version of the Charged System Search (CSS) algorithm, designed to address complex optimization problems more efficiently. CSSRank integrates a rank-based reduction selection strategy to enhance exploitation by progressively reducing the number of charged particles used in electric force calculations. To further balance exploration and exploitation, a ranking-based mutation strategy is incorporated, promoting diversity in early iterations and precision in later stages. We evaluated CSSRank on a set of standard benchmark functions and compared its performance with the original CSS algorithm. In addition, CSSRank was tested on two major benchmark suites, CEC 2014 and CEC 2024, and compared against a wide range of state-of-the-art metaheuristic algorithms. The results show that CSSRank outperforms many existing methods on CEC 2014 and performs competitively and close to the best-performing algorithms on CEC 2024, demonstrating both robustness and scalability. For real-world applications, CSSRank was applied to six UCI clustering datasets, where it consistently achieved higher clustering accuracy and more reliable objective values than baseline methods. It was also tested on three complex reservoir operation optimization problems, yielding superior engineering solutions with high reliability, and contributing to improvements in operational cost and resource efficiency. These results confirm the effectiveness, versatility, and reliability of CSSRank across both theoretical and practical optimization tasks, positioning it as a strong candidate for solving complex problems in optimization and operations research.