Semi-elitist Evolutionary Multi-agent System for Multiobjective Optimization

  • Leszek Siwik
  • Marek Kisiel-Dorohinicki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3993)


The paper presents some modification of the idea of an evolutionary multi-agent system for multiobjective optimization, dealing simultaneously with the stagnation of evolutionary process and the loss of agents representing high-quality solutions. The main mechanisms proposed follow the idea of elitist operators known from classical evolutionary algorithms, yet in this case the elite does not take part in the evolutionary process. Some preliminary results based on a typical multi-objective problem presenting the most important features of the proposed approach are also discussed.


Pareto Frontier Consecutive Step Nondominated Solution Multiobjective Evolutionary Algorithm Domination Relation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Coello Coello, C.A., Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, Dordrecht (2002)zbMATHGoogle Scholar
  2. 2.
    Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)zbMATHGoogle Scholar
  3. 3.
    Kisiel-Dorohinicki, M.: Agent-oriented model of simulated evolution. In: Grosky, W.I., Plášil, F. (eds.) SOFSEM 2002. LNCS, vol. 2540. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Osyczka, A.: Evolutionary Algorithms for Single and Multicriteria Design Optimization. Physica Verlag (2002)Google Scholar
  5. 5.
    Siwik, L., Kisiel-Dorohinicki, M.: Balancing of production lines – evolutionary agent-based approach. In: Lefranc, G. (ed.) Management and Control of Production and Logistics — MCPL 2004, pp. 319–324 (2004)Google Scholar
  6. 6.
    Socha, K., Kisiel-Dorohinicki, M.: Agent-based evolutionary multiobjective optimisation. In: Proc. of the 2002 Congress on Evolutionary Computation. IEEE, Los Alamitos (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Leszek Siwik
    • 1
  • Marek Kisiel-Dorohinicki
    • 1
  1. 1.Department of Computer ScienceAGH University of Science and TechnologyKrakówPoland

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