[1]
Holland J.H., Adaptation of Natural and Artificial Systems. Ann Arbor, MI: The University of Michigan Press, (1975).
Google Scholar
[2]
Kaya, I., A genetic algorithm approach to determine the sample size for control charts with variables and attributes, Expert Systems with Applications, 2009, 36(5), 8719-8734.
DOI: 10.1016/j.eswa.2008.12.011
Google Scholar
[3]
De Giovanni, L., Pezzella, F. , An Improved Genetic Algorithm for the Distributed and Flexible Job-shop Scheduling problem, European Journal of Operational Research, 2010, 200(2), 395-408.
DOI: 10.1016/j.ejor.2009.01.008
Google Scholar
[4]
Abbasgholipour, M., Omid, M., Keyhani, A., Mohtasebi, S.S., Color image segmentation with genetic algorithm in a raisin sorting system based on machine vision in variable conditions, Expert Systems with Applications, 2011, 38(4), 3671-3678.
DOI: 10.1016/j.eswa.2010.09.023
Google Scholar
[5]
Shiwei Yu, Kejun Zhu, Haixiang GUO, A hybrid MPSO-BP structure adaptive algorithm for RBFNs, Neural Computation& Applications , 2009(18), 769-779.
DOI: 10.1007/s00521-008-0214-2
Google Scholar
[6]
Wang, K., Salhi, A., & Fraga, E. S., Process design optimization using embedded hybrid visualization and data analysis techniques within a genetic algorithm optimisation framework, Chemical Engineering and Processing, 2004, 43(5), 657-669.
DOI: 10.1016/j.cep.2003.01.001
Google Scholar
[7]
Kennedy, J., Eberhart, R.C., Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, 1995, p.1942-(1947).
Google Scholar
[8]
Shayeghi H., Jalili A., Shayanfar H. A., Multi-stage fuzzy load frequency control using PSO, Energy Conversion and Management, 2008, 49(10), 2570-2580.
DOI: 10.1016/j.enconman.2008.05.015
Google Scholar
[9]
Kennedy J., The particle swarm: social adaptation of knowledge. In: Proceedings of the IEEE International conference on Evolutionary Computation, Indianapolis, IN, IEEE Service Center, Piscataway, NJ. 1997, pp.303-308.
DOI: 10.1109/icec.1997.592326
Google Scholar
[10]
Trelea I.C., The particle swarm optimization algorithm: convergence analysis and parameter selection, Information Processing Letters, 2003, 85(6), 317-325.
DOI: 10.1016/s0020-0190(02)00447-7
Google Scholar
[11]
Van den Bergh, F., Engelbrecht, A.P., A cooperative approach to particle swarm optimization, IEEE Transactions on Evolutionary Computation, 2004, 8(3), 225-239.
DOI: 10.1109/tevc.2004.826069
Google Scholar
[12]
Premalatha K., Natarajan A.M., Hybrid PSO and GA for Global Maximization, The International Journal of Open Problems in Computer Science and Mathematics, 2009, 2(4), 597-608.
Google Scholar
[13]
Shi X.H., Liang Y.C., Lee H.P., et al. , An improved GA and a novel PSO-GA-based hybrid algorithm, Information Processing Letters, 2005, 93(5), 255-261.
DOI: 10.1016/j.ipl.2004.11.003
Google Scholar
[14]
Gandelli,A., Grimaccia, F., Mussetta M., Development and validation of different hybridization strategies between GA and PSO. In: Proceedings of the IEEE Congress on Evolutionary Computation, Singapore, 2007, pp.2782-2785.
DOI: 10.1109/cec.2007.4424823
Google Scholar
[15]
Yao,K., Li, F., & Liu, X., Hybrid algorithm based on PSO and GA, Computer Engineering and Applications, 2007, 43(6) , 62-64, In Chinese.
Google Scholar
[16]
Kao Y., Zahara E., A hybrid genetic algorithm and particle swarm optimization for multimodal functions, Applied Soft Computing, 2008, 8(2), 849-857.
DOI: 10.1016/j.asoc.2007.07.002
Google Scholar
[17]
Shiwei Yu, Yi-Ming Wei, Ke Wang, Energy demand projection of China using a path-coefficient analysis and PSO-GA approach, Energy Conversion and Management, 2012, 58(1), 142-153.
DOI: 10.1016/j.enconman.2011.08.015
Google Scholar
[18]
Juang C.F., A Hybrid of Genetic Algorithm and Particle Swarm Optimization for Recurrent Network Design, IEEE transactions on systems, man, and cybernetics-Part B: cybernetics, 2004, 34(2), 997-1004.
DOI: 10.1109/tsmcb.2003.818557
Google Scholar
[19]
Kim D.H., Hirota ,K., Vector control for loss minimization of induction motor using GA–PSO, Applied Soft Computing, 2008, 8 (4), 1692-1702.
DOI: 10.1016/j.asoc.2006.09.001
Google Scholar
[20]
Shiwei Yu, Yi-Ming Wei, Ke Wang, A PSO-GA optimal model to estimate primary energy demand of China, Energy Policy, 2012, 42, 329-340.
DOI: 10.1016/j.enpol.2011.11.090
Google Scholar
[21]
Kennedy, J., Eberhart, R.C., Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, 1995, p.1942-(1947).
Google Scholar
[22]
Clerc M., Kennedy J., The particle swarm: explosion stability and convergence in a multi-dimensional complex space, IEEE Transactions on Evolutionary Computation, 2002, 6(1), 58-73.
DOI: 10.1109/4235.985692
Google Scholar
[23]
Shi Y.H., Eberhart R., A modified particle swarm optimizer, Ion: The 1998 IEEE International Conference on Evolutionary Computation, ICEC'98, Anchorage, AK, USA, 1998, pp.69-73.
DOI: 10.1109/icec.1998.699146
Google Scholar
[24]
Pérez-Vázquez, M.E., Gento-Municio, A.M., Lourenço H.R., Solving a concrete sleepers production scheduling by genetic algorithms, European Journal of Operational Research, 2007, 179(3), 605-620.
DOI: 10.1016/j.ejor.2005.03.070
Google Scholar
[25]
Musharavati F., Hamouda A.S.M., Modified genetic algorithms for manufacturing process planning in multiple parts manufacturing lines, Expert Systems with Applications, 2011, 38(9), 10770-10779.
DOI: 10.1016/j.eswa.2011.01.129
Google Scholar
[26]
Deb K., Beyer H., Self-adaptive genetic algorithms with simulated binary crossover, Evolutionary Computation, 2001, 9 (2), 197-221.
DOI: 10.1162/106365601750190406
Google Scholar
