Genetic Algorithm Based on K-means-Clustering Technique for Multi-objective Resource Allocation Problems
Mai A. Farag, M. A. El-Shorbagy, I. M. El-Desoky, A. A. El-Sawy, A. A. Mousa
Current Journal of Applied Science and Technology · pp. 80–96 · Published 20 Mar 2015
10.9734/BJAST/2015/16570Abstract
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.
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M. A. El-Shorbagy, A. Y. Ayoub, I. El-Desoky · International Conference on Advanced Machine Learning Technologies and Applications · 2018
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Caiping Hou, Xiyu Liu · International Conference on Human Centered Computing · 2016
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