43
Views
1
CrossRef citations to date
0
Altmetric
Original Articles

Hybrid Multiobjective Differential Evolution Incorporating Preference Based Local Search

&
Pages 733-747 | Received 31 May 2012, Accepted 29 Mar 2013, Published online: 24 Oct 2013

References

  • K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evolutionary Computation, 6(2), 182–197 (2002).
  • E. Zitzler, L. Thiele and L. Thiele, “SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization,” Evolutionary Methods for Design Optimisation and Control, 95–100 (2002).
  • J. D. Knowles and D. W. Corne, “Approximating the nondominated front using the Pareto archive evolution strategy,” Evolutionary Computation, 8(2), 149–172 (2000).
  • R. Storn and K. Price, “Differential evolution–a simple and efficient adaptative scheme for global optimization over continuous spaces,” Technical Report TR-95-12, International Computer Science, Berkeley, California, (1995).
  • H. A. Abbass, R. Sarker and C. Newton, “PDE: A Pareto-frontier differential evolution approach for multiobjective optimization problems,” In Proceedings of the Congress on Evolutionary Computation 2001(CEC'2001), 831-836 (2001).
  • N. K. Madavan, “Multiobjective optimization using a Pareto Differential evolution approach,” In congress of Evolutionary Computation, 2, 1145–1150 (2002).
  • T. Tobič, and B. Filipič, “DEMO: Differential evolution for multiobjective optimization,” C.A.C. Coello et al. (Eds.) EMO 2005, LNCS 3410, 520–533 (2005).
  • L. V. Santana-Quintero, and C. A. C. Coello, “An Algorithm Based on Differential Evolution for Multi-Ojbective Prolbems,” International Journal of Computational Intelligence Research, 1(2), 151–169 (2005).
  • K. Deb, M. Mohan and S. Mishra, “Evaluating the ε-domination based multi-objective evolutionary algorithm for a quick computation of Pareto-optimal solutions,” Evolutionary Computation, 13(4), 501–525 (2005).
  • A. G. Hernández-Díaz, L. V. Santana-Quintero, C. A. C. Coello and J. Molina, “Pareto-adaptive ε- dominance,” Evolutionary Computation, 15(4), 493–517 (2007).
  • N. Dong and Y. P. Wang, “A Hybrid Multiobjective Differential Evolution Algorithm Based on Improved ε-Dominance,” International Conference on Computational Intelligence and Security (CIS), 24–28 (2011).
  • M. Laumanns, L. Thiele, K. Deb and E. Zitzler, “Combining Convergence and Diversity in Evolutionary Multi-objective Optimization,” Evolutionary Computation, 10(3), 263–282 (2002).
  • W. Y. Gong and Z. H. Cai, “An improved multiobjective differential evolution based on Pareto-adaptive ε- dominance and orthogonal design,” European Journal of Operational Research, 198(2), 576–601 (2009).
  • K. Deb, J. Sundar, N. Udaya Bhaskara Roa, S. Chaudhuri, “Reference point based multi-objective optimization using evolutionary algorithms,” International Journal of Computational Intelligence Research, 2(3), 273–286 (2006).
  • Molina J., Santana L.V., Hernndez-Dłaz A.G., Coello Coello C.A., Caballero, R., “g-dominance: Reference point based dominance for multiobjective metaheuristics,” European Journal of Operational Research, 197(2), 685–692 (2009).
  • L. Thiele, K. Miettinen, P.J. Korhonen, J. Molina, “A preference-based evolutionary algorithm for multi-objective optimization,” Evolutionary Computation, 17(3), 411–436 (2009).
  • M. Luque, K. Miettinen, A. B. Ruiz, F. Ruiz, “A twoslope achievement scalarizing function for interactive multiobjective optimization,” Computers and Operations Research, 39, 1673–1681 (2012).
  • E. Zitzler and S. Knzli, “Indicator-based selection in multiobjective search,” In: Proceedings of the Eighth International Conference on Parallel Problem Solving from Nature (PPSN 2004), Springer, Berlin, 832–842 (2004).
  • J. F. Figueira, A. Liefooghe, E.-G. Talbi A. P. Werzbicki, “A parallel multiple reference point approach for multi-objective optimization,” European Journal of Operational Research, 205(2), 390–400 (2010).
  • H. Ishibuchi and T. Murata, “A multi-objective genetic local search algorithm and its application to flowshop scheduling,” IEEE Transactions on Systems, Man and Cybernetics - Part C: Applications and Reviews, 28(3), 392–403 (1998).
  • A. Jaszkiewicz, “Genetic local search for multiple objective combinatorial optimization,” European Journal of Operational research, 137(1), 50–71 (2002).
  • T. Goel and K. Deb, “Hybrid methods for multiobjective evolutionary algorithms,” In Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning (SEAL02), L. Wang, K. C. Tan, T. Furuhashi, J. H. Kim, and X. Yao, Eds., 1, 188–192 (2002).
  • K. Sindhya, A. Sinha, K. Deb and K. Miettinen, “Local Search Based Evolutionary Multi-Objective Optimization Algorithm for Constrained and Unconstrained Problem,” In Congress on Evolutionary Computation (CEC 2009), IEEE Press, 2919–2926 (2009).
  • R. Storn and K. Price, “Home page of differential evolution,” 2003. < http://www.ICSI.Berkeley.edu/storn/code.html>
  • Wierzbicki A. P., “The use of reference objectives in multiobjective optimization,” In: Fandel G, Gal T, editors. Multiple criteria decision making, theory and applications. Berlin:Springer-Verlagp, 468–486 (1980).
  • Miettinen K., “Nonlinear multiobjective optimization,” Boston: Kluwer Academic Publishers, 1999.
  • Ruiz F., Luque M. and Cabello J. M, “A classification of the weighting schemes in reference point procedures for multiobjective programming,” Journal of the Operational Research Society, 60(4), 544–553 (2009).
  • Luque M., Miettinen K., Eskelinen P., Ruiz F., “Incorporating preference information in interactive reference point methods for multiobjective optimization,” Omega, 37(2), 450–462 (2009).
  • J. D. Knowles and D. W. Corne, “Properites of an Adaptive Archiveing Algorithm for Storing Nondominated Vectors,” IEEE Trans. Evolutioanary Computation, 7(2), 100–116 (2003).
  • Y. W. Leung and Y. Wang, “An orthogonal genetic algorithm with quantization for global numerical optimization,” IEEE Trans. Evolutioanary Computation, 5(1), 41–53 (2003).
  • E. Zitzler, K. Deb and L. Thiele, “Comparison of multiobjective evolutionary algorithms: Empirical Results,” Evolutioanary Computation, 8(2), 173–195 (2000).
  • Qingfu Zhang and Hui Li, “MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition,” IEEE Trans. Evolutioanary Computation, 11(6), 712–731 (2007).

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.