A Multiple-Search Multi-Start Framework for Metaheuristics for Clustering Problems

Abstract

Metaheuristic algorithms have been widely used as an effective and efficient way for solving various complex optimization problems; there is, however, plenty of room for improvement. In this research area, the two most important issues that greatly influence the final results of single-solution-based metaheuristic algorithms are in that: (1) some of them are extremely sensitive to the initial solutions, and (2) some of them are easy to fall into a local optimum at early iterations. For these reasons, an effective framework, called multiple-search multi-start for single-solution-based metaheuristic algorithm (MSMS-S), is presented in this paper to mitigate the impact of these issues. MSMS-S ensures that a search procedure will be given different search directions based on the so-called re-start mechanism. To evaluate the performance of the proposed framework, we compare it with several well-known single-solution-based metaheuristic algorithms for clustering and codebook generation problems. Simulation results show that the proposed framework is capable of improving the performance of single-solution-based metaheuristic algorithms.