[1]
M. Gen and A. Syarif, Hybrid genetic algorithm for multi-time period production/distribution planning, Computers and Industrial Engineering, 48 (4), 799-809, (2005).
DOI: 10.1016/j.cie.2004.12.012
Google Scholar
[2]
R. T. Nakatsu, Designing business logistics networks using model-based reasoning and heuristic-based searching, Expert Systems with Applications, 29 (4), 735-745, (2005).
DOI: 10.1016/j.eswa.2005.06.011
Google Scholar
[3]
M. Gen, F. Altiparamak, and L. Lin, A genetic algorithm for two-stage transportation problem using priority-based encoding, OR Spectrum, 28 (3), 337-354, (2006).
DOI: 10.1007/s00291-005-0029-9
Google Scholar
[4]
F. Altiparamak, M. Gen, L. Lin and T. Paksoy, A genetic algorithm approach for multiobjective optimization of supply chain networks, Computers and Industrial Engineering, 51 (1), 197-216, (2006).
DOI: 10.1016/j.cie.2006.07.011
Google Scholar
[5]
M. Gen, R. Cheng, and L. Lin, Logistics network models, In Network models and optimization, London: Springer, 2008 pp.135-228.
Google Scholar
[6]
L. Lin, M. Gen and X. Wang, Integrated multistage logistics network design by using hybrid evolutionary algorithm, Computers and Industrial Engineering, 56 (3), 854-873, (2009).
DOI: 10.1016/j.cie.2008.09.037
Google Scholar
[7]
Jerzy Balicki, Multi-criterion evolutionary algorithm with model of the immune system to handle constraints for task assignments, In L. Rutkowski, J. Siekmann, R. Tadeusiewicz, and L.A. Zadeh, editors, Artificial Intelligence and Soft Computing – ICAISC 2004 7th International Conference, Proceedings, volume 3070, pages 394–399.
DOI: 10.1007/978-3-540-24844-6_57
Google Scholar
[8]
F. Burnet, The Clonal Selection Theory of Acquired Immunity, Cambridge press, (1959).
Google Scholar
[9]
J. Kennedy and R. Eberhart, Particle swarm optimization" Proceeding of IEEE International Conference on Neural Networks (ICNN, 95), vol. 4, Perth, Western Australia, IEEE, 1995, p.1942–(1947).
DOI: 10.1109/icnn.1995.488968
Google Scholar
[10]
R. Eberhart, A new optimizer using particle swarm theory, Sixth International Symposium on Micro Machine and Human Science, 1995, pp.39-43.
DOI: 10.1109/mhs.1995.494215
Google Scholar
[11]
M. Clerc and J. Kennedy, The particle swarm: Explosion, stability, and convergence in a multi-dimensional complex space, IEEE Transactions on Evolutionary Computation, vol. 6, p.58–73, Feb. (2002).
DOI: 10.1109/4235.985692
Google Scholar
[12]
Del Valle, Y.; Digman, M.; Gray, A.; Perkel, J.; Venayagamoorthy, G.K.; Harley, R.G., Enhanced particle swarm optimizer for power system applications, Swarm Intelligence Symposium, 2008. SIS 2008, IEEE 21-23 Sept. 2008 Page(s): 1 - 7 Digital Object Identifier 10. 1109/SIS. 2008. 4668333.
DOI: 10.1109/sis.2008.4668333
Google Scholar
[13]
Jiao Wei; Liu Guangbin; Liu Dong Elite Particle Swarm Optimization with mutation, System Simulation and Scientific Computing, 2008. ICSC 2008, Asia Simulation Conference - 7th International Conference on 10-12 Oct. 2008 Page(s): 800 - 803 Digital Object Identifier 10. 1109/ASC-ICSC. 2008. 4675471.
DOI: 10.1109/asc-icsc.2008.4675471
Google Scholar
[14]
Marco A. Montes de Oca, et al Frankenstein's PSO: A Composite Particle Swarm Optimization Algorithm, IEEE Transactions on Evolutionary Computation, Vol. 13, No. 5, October 2009, pp.1120-1132.
DOI: 10.1109/tevc.2009.2021465
Google Scholar
[15]
Ratnaweera, A.; Halgamuge, S.K.; Watson, H.C., Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, Evolutionary Computation, IEEE Transactions on Volume 8, Issue 3, June 2004 Page(s): 240 - 255.
DOI: 10.1109/tevc.2004.826071
Google Scholar
[16]
Y. Shi and R.A. Krohling, Co-evolutionary panicle swarm optimization to solving min-max problems, in Proc. of the IEEE Conference on Evolutionary Computation, Hawaii, May, pp.1682-1687, (2002).
DOI: 10.1109/cec.2002.1004495
Google Scholar