<p>This paper addresses the Cluster Editing Problem (CEP), which involves transforming an input graph into a disjoint union of complete graphs by adding or deleting edges. The objective of the CEP is to minimize the number of edge modifications. The CEP has applications in fields such as biological network analysis and text mining. Several solution methods have been proposed for the CEP, and these methods have demonstrated the ability to find good solutions. However, we believe that these solutions can be further improved by applying advanced optimization techniques. Therefore, we propose a local search algorithm for the CEP that incorporates several metaheuristics, including simulated annealing, tabu search, and multi-start methods, to quickly find high-quality solutions. Our computational experiments show that the proposed algorithm outperforms existing methods in most (input) graphs.</p>