Genetic Algorithm Based on K-means-Clustering Technique for Multi-objective Resource Allocation Problems

This paper presents genetic algorithm based on K-means clustering technique for solving multi-objective resource allocation problem (MORAP). By using k-means clustering technique, population can be divided into a specific number of subpopulations with dynamic size. In this way, different GA operators (crossover and mutation) can be applied to each subpopulation instead of one GA operators applied to the whole population. The purpose of implementing K-means clustering technique is preserving and introducing diversity. Also it enable the algorithm to avoid local minima by preventing the population of chromosomes from becoming too similar to each other. Two test problems taken from the literature are used to compare the performance of the proposed approach with the competing algorithms. The results have been demonstrated the superiority of the proposed algorithm and its capability to solve MORAP.