An Iterative Neighborhood Local Search Algorithm for Capacitated Centered Clustering Problem

The Capacitated Centered Clustering Problem (CCCP) is NP-hard and has many practical applications. In recent years, excellent CCCP solving algorithms have been proposed, but their ability to search in the neighborhood space of clusters still insufficient. Based on adaptive Biased Random-Key Genetic Algorithm (A-BRKGA), this paper proposes an efficient iterative algorithm A-BRKGA_INLS. uses shift swap heuristics iteratively enhance quality solutions. computational experiments were conducted 53 instances. A-BRKGA_INLS improves best-known solutions 23 instances matches 15 Moreover, it achieves better average multiple while spending same time as A-BRKGA+CS.