Applied Mathematics & Information Sciences
Abstract
The process of finding the optimal allocation of limited resources to a number of tasks for optimizing multiple objectives is called multi-objective resource allocation problem (MORAP). This paper presents K-means-clustering based on one of the evolutionary algorithm, genetic algorithm(GA), to solve MORAP. Using the K-means-clustering algorithm to divide the population to a specific number of sub-populations each of them with dynamic size. Therefore, different operators of GA (crossover&mutation) can be implemented to each subpopulation instead of applied the same GA operators to the all population. The aim of dynamic clustering is to preserve and introduce diversity into solutions, instead of the solutions becoming similar each other. Two problems taken from the literature are used to compare the performance of the proposed algorithm with the competing algorithms. Moreover, an example of optimum utilization of human resource in reclamation of derelict land in Toshka-Egypt is solved by our approach. The results of different test problems have showed the superiority of our algorithm to solve MORAP.
Digital Object Identifier (DOI)
http://dx.doi.org/10.18576/amis/110615
Recommended Citation
A. Mousa, A.; A. El-Shorbagy, M.; and A. Farag, M.
(2017)
"K-means-Clustering Based Evolutionary Algorithm for Multi-objective Resource Allocation Problems,"
Applied Mathematics & Information Sciences: Vol. 11:
Iss.
6, Article 15.
DOI: http://dx.doi.org/10.18576/amis/110615
Available at:
https://digitalcommons.aaru.edu.jo/amis/vol11/iss6/15