Evolutionary Multi-Agent System with Crowding Factor and Mass Center mechanisms for Multiobjective Optimisation

Mateusz Różański; Leszek Siwik

doi:10.7494/csci.2019.20.3.3339

Authors

Mateusz Różański Akademia Górniczo-Hutnicza
Leszek Siwik AGH University of Science and Technology

DOI:

https://doi.org/10.7494/csci.2019.20.3.3339

Keywords:

evolutionary computation, multi-agent system, multiobjective optimisation

Abstract

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.

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Author Biography

Mateusz Różański, Akademia Górniczo-Hutnicza

PhD student, WIET

References

Byrski A., Dreżewski R., Siwik L., Kisiel-Dorohinicki M.: Evolutionary multi-agent systems. In: The Knowledge Engineering Review, vol. 30:2, pp. 171–186,2015.

Byrski A., Siwik L., Kisiel-Dorohinicki M.: Designing population-structured evo-lutionary computation systems. In: Methods of Artificial Intelligence (AI-METH2003), pp. 91–96. Gliwice, 2003.

Coello Coello C.A., Lemont G.B., Van Veldhiuzen D.A.: Evolutionary Algorithmsfor Solving Multi-Objective Problems. Springer, New York, 2002. ISBN 978-0-387-33254-3.

Deb K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley& Sons, 2008.

Dreźewski R., Siwik L.: Co-evolutionary multi-agent system with predator-preymechanism for multi-objective optimization. In: International Conference onAdaptive and Natural Computing Algorithms, vol. 4431, pp. 67–76. Warsaw, 2007.

Dreżewski R., Siwik L.: Techniques for Maintaining Population Diversity in Clas-sical and Agent-Based Multi-objective Evolutionary Algorithms. In: Y. Shi,G.D. van Albada, J. Dongarra, P.M.A. Sloot, eds.,Computational Science -ICCS 2007, 7th International Conference, Beijing, China, May 27 - 30, 2007,Proceedings, Part II,LNCS, vol. 4488, pp. 904–911. Springer-Verlag, Berlin,Heidelberg, 2007.URLhttp://dx.doi.org/http://dx.doi.org/10.1007/978-3-540-72586-2_126. The final publication is available at Springer viahttp://dx.doi.org/10.1007/978-3-540-72586-2_126.

Dreżewski R., Siwik L.:Agent-Based Multi-Objective Evolutionary Algo-rithm with Sexual Selection.In:Proceedings of the IEEE Congress onEvolutionary Computation, CEC 2008, June 1-6, 2008, Hong Kong, China.IEEE, 2008.URLhttp://dx.doi.org/http://dx.doi.org/10.1109/CEC.2008.4631296. The final publication is available viahttp://dx.doi.org/10.1109/CEC.2008.4631296.

Dreżewski R., Siwik L.: A Review of Agent-Based Co-Evolutionary Algorithmsfor Multi-Objective Optimization. In: Y. Tenne, C.K. Goh, eds.,ComputationalIntelligence in Optimization. Aplication and Implementations. Springer-Verlag,Berlin, Heidelberg, 2010. URLhttp://dx.doi.org/http://dx.doi.org/10.1007/978-3-642-12775-5_8. The final publication is available at Springer viahttp://dx.doi.org/10.1007/978-3-642-12775-5_8.

Eiben A.E., Smith J.E.:Introduction to Evolutionary Computing 2nd edition.SpringerVerlag, 2015. ISBN 3662448734.

Jong K.A.D.:Evolutionary computation - a unified approach.MIT Press, 2016.ISBN 9780262041942.

Kisiel-Dorohinicki M.:Agentowe architektury populacyjnych systemw inteligencjiobliczeniowej. Wydawnicta AGH, Krakow, 2013. ISBN 978-83-7464-588-1.

Siwik L., Kisiel-Dorohinicki M.: Elitism in agent-based evolutionary multiobjec-tive optimization. In:Inteligencia Artificial. Revista Iberoamericana de Inteligen-cia Artificial, vol. 9, pp. 41–48, 2005.

Siwik L., Natanek S.: Solving constrained multi-criteria optimization tasks us-ing Elitist Evolutionary Multi-Agent System. In:2008 IEEE Congress on Evo-lutionary Computation (IEEE World Congress on Computational Intelligence),pp. 3358–3365. 2008. ISSN 1089-778X. URLhttp://dx.doi.org/10.1109/CEC.2008.4631252.

Siwik L., Sikorski P.: Efficient Constrained Evolutionary Multi-Agent System forMulti-objective Optimization. In:2008 IEEE Congress on Evolutionary Com-putation (IEEE World Congress on Computational Intelligence), pp. 3212–3219.2008. ISSN 1089-778X. URLhttp://dx.doi.org/10.1109/CEC.2008.4631233.

Zitzler E., Thiele L.: Multiobjective optimization using evolutionary algorithmsA comparative case study. In:Parallel Problem Solving from Nature PPSN V.Lecture Notes in Computer Science, vol. 1498, pp. 292–301. Springer, Amsterdam,1998.