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A novel fused optimization algorithm of genetic algorithm and ant colony optimization. (English) Zbl 1400.90316

Summary: A novel fused algorithm that delivers the benefits of both genetic algorithms (GAs) and ant colony optimization (ACO) is proposed to solve the supplier selection problem. The proposed method combines the evolutionary effect of GAs and the cooperative effect of ACO. A GA with a great global converging rate aims to produce an initial optimum for allocating initial pheromones of ACO. An ACO with great parallelism and effective feedback is then served to obtain the optimal solution. In this paper, the approach has been applied to the supplier selection problem. By conducting a numerical experiment, parameters of ACO are optimized using a traditional method and another hybrid algorithm of a GA and ACO, and the results of the supplier selection problem demonstrate the quality and efficiency improvement of the novel fused method with optimal parameters, verifying its feasibility and effectiveness. Adopting a fused algorithm of a GA and ACO to solve the supplier selection problem is an innovative solution that presents a clear methodological contribution to optimization algorithm research and can serve as a practical approach and management reference for various companies.

MSC:

90C59 Approximation methods and heuristics in mathematical programming
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[1] Holland, J. H., Adaptation in Natural and Artificial Systems, (1975), Oxford, UK: University of Michigan Press, Oxford, UK
[2] Peng, P. F., Improvement and simulation of ant colony algorithm based on genetic gene, Computer Engineering & Applications, 46, 4, 43-45, (2010)
[3] Zhu, Q.; Chen, S., A new ant evolution algorithm to resolve TSP problem, Proceedings of the 6th International Conference on Machine Learning and Applications (ICMLA ’07) · doi:10.1109/icmla.2007.6
[4] Dorigo, M.; Gambardella, L. M., Ant colonies for the travelling salesman problem, BioSystems, 43, 2, 73-81, (1997) · doi:10.1016/S0303-2647(97)01708-5
[5] Tsai, Y. L.; Yang, Y. J.; Lin, C.-H., A dynamic decision approach for supplier selection using ant colony system, Expert Systems with Applications, 37, 12, 8313-8321, (2010) · doi:10.1016/j.eswa.2010.05.053
[6] Abbattista, F.; Abbattista, N.; Caponetti, L., An evolutionary and cooperative agents model for optimization, Proceedings of the IEEE International Conference on Evolutionary Computation, IEEE · doi:10.1109/ICEC.1995.487464
[7] Acan, A., GAACO: A GA+ACO hybrid for faster and better search capability, Proceedings of the 3rd International Workshop on Ant Algorithms, ANTS
[8] Gong, D. X.; Ruan, X. G., A hybrid approach of GA and ACO for TSP, Proceedings of the 5th World Congress on Intelligent Control and Automation, IEEE
[9] Zhu, S.; Dong, W.; Liu, W., Logistics distribution route optimization based on genetic ant colony algorithm, Journal of Chemical & Pharmaceutical Research, 6, 6, 2264-2267, (2014)
[10] Zhang, W. G.; Lu, T. Y., The research of genetic ant colony algorithm and its application, Procedia Engineering, 37, 2012, 101-106, (2012) · doi:10.1016/j.proeng.2012.04.210
[11] Zhang, Y. H.; Feng, L.; Yang, Z., Optimization of cloud database route scheduling based on combination of genetic algorithm and ant colony algorithm, Precedia Engineering, 15, 3341-3345, (2011)
[12] Yao, Z.; Liu, J.; Wang, Y.-G., Fusing genetic algorithm and ant colony algorithm to optimize virtual enterprise partner selection problem, Proceedings of the IEEE Congress on Evolutionary Computation (CEC ’08), IEEE · doi:10.1109/cec.2008.4631287
[13] Yao, Z.; Pan, R.; Lai, F., Improvement of the fusing genetic algorithm and ant colony algorithm in virtual enterprise partner selection problem, Proceedings of the World Congress on Computer Science and Information Engineering (CSIE ’09) · doi:10.1109/csie.2009.220
[14] Xiao, H. F.; Tan, G. Z., Study improvement of the fusing genetic algorithm and ant colony algorithm in virtual enterprise partner selection problem on fusing genetic algorithm into ant colony algorithm, Journal of Chinese Computer System, 30, 3, 512-517, (2009)
[15] Li, X. M.; Mao, Z.; Qi, E., Research on multi-supplier performance measurement based on genetic ant colony algorithm, Proceedings of the 1st ACM/SIGEVO Summit on Genetic and Evolutionary Computation (GEC ’09)
[16] Gao, S.; Zhang, Z.; Cao, C., A novel ant colony genetic hybrid algorithm, Journal of Software, 5, 11, 1179-1186, (2010) · doi:10.4304/jsw.5.11.1179-1186
[17] Zhang, Y. D.; Wu, L. N., A novel genetic ant colony algorithm, Journal of Convergence Information Technology, 7, 1, 268-274, (2012) · doi:10.4156/jcit.vol7.issue1.33
[18] Bessedik, M.; Tayeb, F. B.-S.; Cheurfi, H.; Blizak, A., An immunity-based hybrid genetic algorithms for permutation flowshop scheduling problems, International Journal of Advanced Manufacturing Technology, 85, 9, 2459-2469, (2016) · doi:10.1007/s00170-015-8052-8
[19] Ahmed, Z. H., Experimental analysis of crossover and mutation operators on the quadratic assignment problem, Annals of Operations Research, (2015) · Zbl 1357.90071 · doi:10.1007/s10479-015-1848-y
[20] Wang, X. M.; Liu, X.; Liu, G., Performance comparison of several kinds of improved genetic algorithm, Journal of Chemical and Pharmaceutical Research, 6, 9, 463-468, (2014)
[21] Lopez-Ibanez, M.; Stutzle, T.; Dorigo, M., Ant colony optimization: a component-wise overview, IRIDIA-Technical Report Series, TR/IRIDIA/2015-006, (2015)
[22] Niu, S. H.; Ong, S. K.; Nee, A. Y. C., An enhanced ant colony optimiser for multi-attribute partner selection in virtual enterprises, International Journal of Production Research, 50, 8, 2286-2303, (2012) · doi:10.1080/00207543.2011.578158
[23] Aliabadi, D. E.; Kaazemi, A.; Pourghannad, B., A two-level GA to solve an integrated multi-item supplier selection model, Applied Mathematics and Computation, 219, 14, 7600-7615, (2013) · Zbl 1290.90002 · doi:10.1016/j.amc.2013.01.046
[24] Simić, D.; Svirčević, V.; Simić, S., A hybrid evolutionary model for supplier assessment and selection in inbound logistics, Journal of Applied Logic, 13, 2, 138-147, (2015) · Zbl 06433917 · doi:10.1016/j.jal.2014.11.007
[25] Yang, P. C.; Wee, H. M.; Pai, S.; Tseng, Y. F., Solving a stochastic demand multi-product supplier selection model with service level and budget constraints using genetic algorithm, Expert Systems with Applications, 38, 12, 14773-14777, (2011) · doi:10.1016/j.eswa.2011.05.041
[26] Mazidi, A.; Fakhrahmad, M.; Sadreddini, M., A meta-heuristic approach to CVRP problem: local search optimization based on GA and ant colony, Journal of Advance in Computer Research, 7, 1, 1-22, (2016)
[27] Dong, G. F.; Guo, W. W.; Tickle, K., Solving the traveling salesman problem using cooperative genetic ant systems, Expert Systems with Applications, 39, 5, 5006-5011, (2012) · doi:10.1016/j.eswa.2011.10.012
[28] Liu, M. J., Research on integration and performance of ant colony algorithm and genetic algorithm [Ph.D. thesis], (2013), Beijing, China: School of Science, China University of Geosciences, Beijing, China
[29] Xiong, Z.-H.; Li, S.-K.; Chen, J.-H., Hardware/software partitioning based on dynamic combination of genetic algorithm and ant algorithm, Journal of Software, 16, 4, 503-512, (2005) · Zbl 1109.68559 · doi:10.1360/jos160503
[30] Ma, Z. J., Partner selection of supply chain alliance based on genetic algorithm, Academic Journal of System Engineering Theory and Practice (Chinese Journal), 9, 81-84, (2003)
[31] Stützle, T.; Hoos, H. H., MAX-MIN ant system, Future Generation Computer Systems, 16, 8, 889-914, (2000) · doi:10.1016/s0167-739x(00)00043-1
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