Evolutionary multiobjetive optimization in non-stationary environments


  • Victoria S. Aragón Lab. de Investigación y Desarrollo en Inteligencia Computacional (LIDIC), Universidad Nacional de San Luis, San Luis, Argentina
  • Susana Cecilia Esquivel Lab. de Investigación y Desarrollo en Inteligencia Computacional (LIDIC), Universidad Nacional de San Luis, San Luis, Argentina
  • Carlos Coello Coello CINVESTAV-IPN (Evolutionary Computation Group), Electrical Eng. Department, Computer Science Dept., México D.F., México


dynamic environments


This paper proposes an approach, called Multiobjective Algorithm for Dynamic Environments (MADE), which extendes Fonseca and Fleming's MOGA (with an external archive) so that it can deal with dynamic environments. MADE includes two techniques to maintain diversity and also uses specialized functions that implements the dynamism required. In order to validate MADE, we defined a dynamic version of a static test problem (with 3 objectives) previously proposed in the specialized literature. The preliminary results obtained indicate that the proposed approach provides an acceptable response to the type of changes studied.


Download data is not yet available.


[1] Branke, J.: Evolutionary optimization in dynamic environments. Kluwer Academic Publishers (2002)
[2] Yamasaki, K.: Dynamic pareto optimum GA against the changing environments. In Branke, J., Back, T., eds.: Evolutionary Algorithms for Dynamic Optimization Problems, San Francisco, California, USA (2001) 47-50
[3] Jin, Y., Sendho, B.: Constructing dynamic optimization test problems using multiobjective optimization concept (2004) In G. R. Raidl, editor, Aplications of Evolutionary Computing, volume 3005 of LNCS, pages 525-536. Springer
[4] Farina, M., Deb, K., Amato, P.: Dynamic Multiob jective Optimization Problems: Test Cases, Approximation, and Applications. In Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L., eds.: Evolutionary Multi-Criterion Optimization. Second International Conference, EMO 2003, Faro, Portugal, Springer. Lecture Notes in Computer Science. Volume 2632 (2003) 311-326
[5] Fonseca, C.M., Fleming, P.J.: Genetic Algorithms for Multiob jective Optimization: Formulation, Discussion and Generalization. In Forrest, S., ed.: Proceedings of the Fifth International Conference on Genetic Algorithms, San Mateo, California, University of Illinois at Urbana-Champaign, Morgan Kauffman Publishers (1993) 416-423
[6] Raman, N., Talbot, F.B.: The job shop tardiness problem: A descomposition approach. European Journal of Operational Research 69 (1993) 187-199
[7] Coello Coello, C.A., Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Ob jective Problems. Kluwer Academic Publishers, New York (2002) ISBN 0-3064-6762-3.
[8] Kwasnicka, H.: Redundancy of genotypes as the way for some advanced operators in evolutionary algorithms - Simulation Study. VIVEK, A Quarterly in Articial Intelligence 10 (1997) 2-11
[9] Cobb, H., Grefenstette, J.: Genetic algorithms for tracking changing environments. In: Proceeding of the 5th IEEE International Conference on Genetic Algorithms, Morgan Kauffman (1993) 523-530
[10] Grefenstette, J.: Optimization of control parameters for genetic algorithms. IEEE Transaction on Systems, Man and Cybernetic 16 (1986) 122-128
[11] Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable Multi-Ob jective Optimization Test Problems. In: Congress on Evolutionary Computation (CEC'2002). Volume 1., Piscataway, New Jersey, IEEE Service Center (2002) 825{830
[12] Back, T.: On the behavior of evolutionary algorithms in dynamic environnments. In: Proceedings of International Conference on Evolutionary Computation, Piscataway, NJ, IEEE Press (1998) 446{451
[13] Veldhuizen, D.A.V.: Multiob jective Evolutionary Algorithms: Classications, Analyses, and New Innovations. PhD thesis, Department of Electrical and Computer Engineering. Graduate School of Engineering. Air Force Institute of Technology, WrightPatterson AFB, Ohio (1999)
[14] Schott, J.R.: Fault tolerant design using single and multicriteria genetic algorith optimization. Master's thesis, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, Massachussetts (1995)
[15] Morrison, R.W.: Designing Evolutionary Algorithms for Dynamic Environments. Springer-Verlag, Berlin (2004)
[16] Laumanns, M., Thiele, L., Deb, K., Zitzler, E.: Combining Convergence and Diversity in Evolutionary Multi-ob jective Optimization. Evolutionary Computation 10 (2002) 263-282
[17] Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiob jective Genetic Algorithm: NSGA{II. IEEE Transactions on Evolutionary Computation 6 (2002) 182-197




How to Cite

Aragón, V. S., Esquivel, S. C., & Coello Coello, C. (2005). Evolutionary multiobjetive optimization in non-stationary environments. Journal of Computer Science and Technology, 5(03), p. 133–143. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/862



Original Articles

Most read articles by the same author(s)