Sharif Digital Repository

Clustering is one of the useful methods in many scientific fields. It is a classification process to group data in specific clusters based on their similarities. Many heuristic and meta-heuristic algorithms have been successfully applied in the literature to solve clustering problems. Among them, the K-means is one of the best due to its simplicity and computational efficiency. However, it suffers from several drawbacks, the most significant of which is its dependency on the initial state that leads to trapping in local optima. In this paper, the K-means method is combined with a modified electromagnetism-like mechanism (MEM) algorithm to develop a new algorithm called K-MEM in order to... 

Meta-heuristic algorithms have been successfully applied to solve the redundancy allocation problem in recent years. Among these algorithms, the electromagnetism-like mechanism (EM) is a powerful population-based algorithm designed for continuous decision spaces. This paper presents an efficient memory-based electromagnetism-like mechanism called MBEM to solve the redundancy allocation problem. The proposed algorithm employs a memory matrix in local search to save the features of good solutions and feed it back to the algorithm. This would make the search process more efficient. To verify the good performance of MBEM, various test problems, especially the 33 well-known benchmark instances in...