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Evolutionary multi-agent system with crowding factor and mass center mechanisms for multiobjective optimisation

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Języki publikacji
EN
Abstrakty
EN
This work presents some additional mechanisms for Evolutionary Multi-Agent Systems for Multiobjective Optimisation trying to solve problems with population stagnation and loss of diversity. Those mechanisms reward solutions located in a less crowded neighborhood and on edges of the frontier. Both techniques have been described and also some preliminary results have been shown.
Wydawca
Czasopismo
Rocznik
Strony
343--367
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
  • AGH University of Science and Technology, Krakow, Poland
autor
  • AGH University of Science and Technology, Krakow, Poland
Bibliografia
  • [1] Abraham A., Jain L.C., Goldberg R.:Evolutionary Multiobjective OptimizationTheoritical Advances and Applications, Springer, 2005.
  • [2] Byrski A., Dreżewski R., Siwik L., Kisiel-Dorohinicki M.: Evolutionary multi-agent systems,The Knowledge Engineering Review, vol. 30(2), pp. 171–186, 2015.
  • [3] Byrski A., Siwik L., Kisiel-Dorohinicki M.: Designing population-structuredevolutionary computation systems. In:METHods of Artificial Intelligence(AI-METH 2003), pp. 91–96, Gliwice, 2003.
  • [4] Chen S.H., Kambayashi Y., Sato H.:Multi-Agent Applications with EvolutionaryComputation and Biologically Inspired Technologies: Intelligent Techniques for Ubiquity and Optimization, IGI Global, 2011.
  • [5] Coello Coello C.A., Lemont G.B., Van Veldhiuzen D.A.:Evolutionary Algorithmsfor Solving Multi-Objective Problems, Springer, New York, 2002.
  • [6] Deb K.:Multi-Objective Optimization Using Evolutionary Algorithms, John Wiley & Sons, 2008.
  • [7] Dreżewski R., Siwik L.: Co-Evolutionary Multi-Agent System with Predator--Prey Mechanism for Multi-Objective Optimization. In:International Conferenceon Adaptive and Natural Computing Algorithms, vol. 4431, pp. 67–76, 2007.
  • [8] Dreżewski R., Siwik L.: Techniques for Maintaining Population Diversity in Classical and Agent-Based Multi-objective Evolutionary Algorithms. In: Shi Y., vanAlbada G.D., Dongarra J., Sloot P.M.A. (eds.),Computational Science – ICCS2007, 7th International Conference, Beijing, China, May 27–30, 2007, Proceedings, Part II,LNCS, vol. 4488, pp. 904–911, Springer-Verlag, Berlin, Heidelberg,2007.https://doi.org/10.1007/978-3-540-72586-2126.
  • [9] Dreżewski R., Siwik L.: Agent-based multi-objective evolutionary algorithm with sexual selection. In:Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2008, June 1–6, 2008, Hong Kong, China, IEEE, 2008.https://doi.org/10.1109/CEC.2008.4631296.
  • [10] Dreżewski R., Siwik L.: A Review of Agent-Based Co-Evolutionary Algorithmsfor Multi-Objective Optimization. In: Tenne Y., Goh C.K. (eds.),Computational Intelligence in Optimization. Application and Implementations, Springer-Verlag,Berlin, Heidelberg, 2010.https://doi.org/10.1007/978-3-642-12775-58.
  • [11] Eiben A.E., Smith J.E.:Introduction to Evolutionary Computing, 2nd edition,Springer Verlag, 2015.
  • [12] Ficici S.G., Pollack J.B.: Pareto Optimality in Coevolutionary Learning. In:Advances in Artificial Life: 6th European Conference (ECAL 2011), vol. 2159,pp. 316–325, Springer-Varlag, 2001.
  • [13] Jong De K.A.:Evolutionary Computation – A Unified Approach, MIT Press,2016.
  • [14] Khaos U.d.M.: jMetal.http://jmetal.sourceforge.net/.
  • [15] Kisiel-Dorohinicki M.:Agentowe architektury populacyjnych systemów inteligencji obliczeniowej, Wydawnictwa AGH, Kraków, 2013.
  • [16] Kisiel-Dorohinicki M., Socha K.: Crowding Factor In Evolutionary Multi-AgentSystem For Multiobjective Optimization. In:Proceedings of International Conference on Artificial Intelligence, CSREA Press, 2001.
  • [17] Schaefer R., Kołodziej J.: Genetic search reinforced by the population hierarchy. In:Proceedings of the Seventh Workshop on Foundations of Genetic Algorithms,Torremolinos, Spain, September 2–4, 2002, 2002.
  • [18] Siwik L., Kisiel-Dorohinicki M.: Elitism in agent-based evolutionary multiobjective optimization, Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial, vol. 9, pp. 41–48, 2005.
  • [19] Siwik L., Natanek S.: Solving Constrained Multi-Criteria Optimization Tasks Using Elitist Evolutionary Multi-Agent System. In:2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence),pp. 3358–3365, 2008.https://doi.org/10.1109/CEC.2008.4631252.
  • [20] Siwik L., Sikorski P.: Efficient Constrained Evolutionary Multi-Agent System for Multi-objective Optimization. In:2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 3212–3219.2008.https://doi.org/10.1109/CEC.2008.4631233.
  • [21] Wooldridge M.:An Introduction to Multiagent Systems, John Wiley & Sons,2009.
  • [22] Wróbel K., Torba P., Paszyński M., Byrski A.: Evolutionary multi-agent computing in inverse problems, Computer Science, vol. 14(3), 2013.https://doi.org/10.7494/csci.2013.14.3.367.
  • [23] Zitzler E., Deb K., Thiele L.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results, Evolutionary Computation, vol. 8(2), pp. 173–195,2000.
  • [24] Zitzler E., Thiele L.: Multiobjective optimization using evolutionary algorithms –A comparative case study. In: Parallel Problem Solving from Nature – PPSN V. Lecture Notes in Computer Science, vol. 1498, pp. 292–301, Springer, Amsterdam,1998.
  • [25] Zitzler K., Thiele L.: An evolutionary algorithm for multiobjective optimization: the strength Pareto approach. In:TIK-Report, vol. 43, 1998.https://doi.org/10.3929/ethz-a-004288833
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-610d482b-7151-4441-9bcd-31aac08d16e7
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