Abstract
Co-evolutionary techniques for evolutionary algorithms allow for the application of such algorithms to problems for which it is difficult or even impossible to formulate explicit fitness function. These techniques also maintain population diversity, allows for speciation and help overcoming limited adaptive capabilities of evolutionary algorithms. In this paper the idea of co-evolutionary multi-agent system with predator-prey mechanism for multi-objective optimization is introduced. In presented system the Pareto frontier is located by the population of agents as a result of co-evolutionary interactions between two species: predators and prey. Results from runs of presented system against test problem and comparison to classical multi-objective evolutionary algorithms conclude the paper.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bäck, T.: Handbook of Evolutionary Computation. Oxford University Press, Oxford (1997)
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley, Chichester (2001)
Dreżewski, R.: Co-evolutionary multi-agent system with speciation and resource sharing mechanisms. Computing and Informatics 25(4), 305–331 (2006)
Dreżewski, R., Siwik, L.: Co-evolutionary multi-agent system with sexual selection mechanism for multi-objective optimization. In: Proceedings of the IEEE World Congress on Computational Intelligence (WCCI 2006), IEEE Computer Society Press, Los Alamitos (2006)
Dreżewski, R., Siwik, L.: Multi-objective optimization using co-evolutionary multi-agent system with host-parasite mechanism. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006. LNCS, vol. 3993, pp. 871–878. Springer, Heidelberg (2006)
Laumanns, M., Rudolph, G., Schwefel, H.-P.: A spatial predator-prey approach to multi-objective optimization: A preliminary study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, p. 241. Springer, Heidelberg (1998)
Paredis, J.: Coevolutionary algorithms. In: Bäck, T. (ed.) Handbook of Evolutionary Computation (1st supplement), Oxford University Press, Oxford (1998)
Siwik, L., Kisiel-Dorohinicki, M.: Balancing of production lines: evolutionary, agent-based approach. In: Proceedings of Conference on Management and Control of Production and Logistics, pp. 319–324 (2004)
Siwik, L., Kisiel-Dorohinicki, M.: Semi-elitist Evolutionary Multi-agent System for Multiobjective Optimization. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006. LNCS, vol. 3993, pp. 831–838. Springer, Heidelberg (2006)
Zitzler, E.: Evolutionary algorithms for multiobjective optimization: methods and applications. PhD thesis, Swiss Federal Institute of Technology, Zurich (1999)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Dreżewski, R., Siwik, L. (2007). Co-evolutionary Multi-agent System with Predator-Prey Mechanism for Multi-objective Optimization. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71618-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-540-71618-1_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71589-4
Online ISBN: 978-3-540-71618-1
eBook Packages: Computer ScienceComputer Science (R0)