Abstract
When evolutionary algorithms for solving multi-modal optimization problems are applied, the crucial issue to be solved is maintaining population diversity to avoid drifting and focusing individuals around single global optima. A lot of techniques have been used here so far. Simultaneously for last twenty years a lot of effort has been made in the area of evolutionary algorithms for multi-objective optimization. As the result at least several highly efficient algorithms have been proposed such as NSGAII or SPEA2. Obviously, also in this case maintaining of population diversity is crucial but this time, taking the specificity of optimization in the Pareto sense, there are built-in mechanisms to solve this issue effectively. If so, the idea arises of applying of state-of-theart evolutionary multi-objective optimization algorithms for solving not original multi-modal (but single-objective) optimization task but rather its transformed into multi-objective problem form by introducing additional dispersion-oriented criteria. The goal of this paper is to present some further study in this area
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Keywords
- Evolutionary Algorithm
- Multiobjective Optimization
- Pareto Frontier
- Multimodal Problem
- Maintain Population Diversity
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.
References
Abraham, A., Jain, L.C., Goldberg, R.: Evolutionary Multiobjective Optimization Theoretical Advances and Applications. Springer (2005)
Byrski, A., Dreżewski, R., Siwik, L., Kisiel-Dorohinicki, M.: Evolutionary multi-agent systems. The Knowledge Engineering Review (to be published, 2014)
Chakrabarti, D., Kumar, R., Tomkins, A.: Evolutionary clustering. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York (2006)
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons (2008)
Dreżewski, R., Obrocki, K., Siwik, L.: Agent-based co-operative co-evolutionary algorithms for multi-objective portfolio optimization. In: Brabazon, A., O’Neill, M., Maringer, D.G. (eds.) Natural Computing in Computational Finance. SCI, vol. 293, pp. 63–84. Springer, Heidelberg (2010)
Dreżewski, R., Sepielak, J.: Evolutionary system for generating investment strategies. In: Giacobini, M., et al. (eds.) EvoWorkshops 2008. LNCS, vol. 4974, pp. 83–92. Springer, Heidelberg (2008)
Dreżewski, R., Siwik, L.: Techniques for maintaining population diversity in classical and agent-based multi-objective evolutionary algorithms. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007, Part II. LNCS, vol. 4488, pp. 904–911. Springer, Heidelberg (2007)
Dreżewski, R., Siwik, L.: Agent-based co-operative co-evolutionary algorithm for multi-objective optimization. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 388–397. Springer, Heidelberg (2008)
Dreżewski, R., Siwik, L.: Co-evolutionary multi-agent system for portfolio optimization. In: Brabazon, A., O’Neill, M. (eds.) Natural Computing in Computational Finance. SCI, vol. 100, pp. 271–299. Springer, Heidelberg (2008)
Hruschka, E.R., Campello, R.J.G.B., Freitas, A.A., de Carvalho, A.C.P.L.F.: A survey of evolutionary algorithms for clustering. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 39(2) (2009)
Marler, R., Arora, J.: Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization 26(6) (2004)
Preuss, M., Rudolph, G., Tumakaka, F.: Solving multimodal problems via multiobjective techniques with application to phase equilibrium detection. In: IEEE Congress on Evolutionary Computation. IEEE (2007)
Sarafis, I.A., Trinder, P.W., Zalzala, A.: Towards effective subspace clustering with an evolutionary algorithm. In: Sarker, R., et al. (eds.) Proceedings of the 2003 Congress on Evolutionary Computation, vol. 2. IEEE Press (2003)
Srinivas, N., Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation 2(3), 221–248 (1994)
Streichert, F., Stein, G., Ulmer, H., Zell, A.: A clustering based niching method for evolutionary algorithms. In: Cantú-Paz, E., et al. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 644–645. Springer, Heidelberg (2003)
Tasoulis, D.K., Plagianakos, V.P., Vrahatis, M.N.: Clustering in evolutionary algorithms to efficiently compute simultaneously local and global minima. In: Congress on Evolutionary Computation. IEEE (2005)
Tasoulis, D.K., Vrahatis, M.N.: The new window density function for efficient evolutionary unsupervised clustering. In: Congress on Evolutionary Computation. IEEE (2005)
Vrahatis, M.N., Boutsinas, B., Alevizos, P., Pavlides, G.: The new k-windows algorithm for improving the k-means clustering algorithm. J. Complex. 18(1) (March 2002)
Zitzler, E.: Evolutionary algorithms for multiobjective optimization: methods and applications. PhD thesis, Swiss Federal Institute of Technology, Zurich (1999)
Zitzler, E.: Evolutionary algorithms, multiobjective optimization, and applications (September 2003)
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Siwik, L., Dreżewski, R. (2014). Evolutionary Multi-modal Optimization with the Use of Multi-objective Techniques. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8467. Springer, Cham. https://doi.org/10.1007/978-3-319-07173-2_37
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DOI: https://doi.org/10.1007/978-3-319-07173-2_37
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