Abstract
In recent years, a general-purpose local-search heuristic method called Extremal Optimization (EO) has been successfully applied in some NP-hard combinatorial optimization problems. In this paper, we present a novel Pareto-based algorithm, which can be regarded as an extension of EO, to solve multiobjective optimization problems. The proposed method, called Multiobjective Population-based Extremal Optimization (MOPEO), is validated by using five benchmark functions and metrics taken from the standard literature on multiobjective evolutionary optimization. The experimental results demonstrate that MOPEO is competitive with the state-of-the-art multiobjective evolutionary algorithms. Thus MOPEO can be considered as a viable alternative to solve multiobjective optimization problems.
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Coello CAC (2001) A short tutorial on evolutionary multiobjective optimization. Proceedings of the 1st international conference on evolutionary multi-criterion optimization (EMO 2001), Lecture notes in computer science, 1993, Zurich, Springer, Heidelberg, pp 21-40
Boettcher S, Percus AG (2000) Nature’s way of optimizing. Artif Intell 119:275–286
Bak P, Sneppen K (1993) Punctuated equilibrium and criticality in a simple model of evolution. Phys Rev Lett 71(24):4083–4086
Bak P, Tang C, Wiesenfeld K (1987) Self-organized criticality. Phys Rev Lett 59:381–384
Boettcher S, Percus AG (2004) Extremal optimization at the phase transition of the 3-coloring problem. Phys Rev E 69:066703
Boettcher S (2005) Extremal Optimization for the Sherrington-Kirkpatrick Spin Glass. Eur Phys J B 46:501–505
Menai ME, Batouche M (2003) Efficient initial solution to extremal optimization algorithm for weighted MAXSAT problem. IEA/AIE 2003, pp 592–603
Ahmed E, Elettreby MF (2004) On multiobjective evolution model. Int J Modern Phys C 15:1189
Das I, Dennis J (1997) A close look at drawbacks of minimizing weighted sum of objectives for Pareto set generation in multicriteria optimization problems. Struct Optim 14:63
Galski RL, de Sousa FL, Ramos FM (2005) Application of a new multiobjective evolutionary algorithm to the optimum design of a remote sensing satellite constelation. In: Proceedings of the 5th international conference on inverse problems in engineering: theory and practice, Cambridge, vol II, G01
Deb K, Pratab A, Agrawal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGACII. IEEE Trans Evolut Comp 6(2):182–197
Knowles J, Corne D (1999) The Pareto archived evolution strategy: a new baseline algorithm for multiobjective optimization. In: Proceedings of the 1999 congress on evolutionary computation. Piscataway, IEEE Press, pp 98–105
Zitzler E, Thiele L (1998) Multiobjective optimization using evolutionary algorithms—a comparative case study. In: Eiben VAE, Bäck T, Schoenauer M, Schwefel H-P (eds) Parallel problem solving from nature. Springer, Berlin, pp 292–301
Fonseca CM, Fleming PJ (1993) Genetic algorithms for multiobjective optimization: formulation, discussion and generalization. In: Forrest S (ed) Proceedings of the fifth international conference on genetic algorithms. Morgan Kauffman, San Mateo, pp 416–423
Coello CAC, Pulido GT, Leehuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evolut Comp 8(3):256–279
Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evolut Comp 3(2):82–102
Janikow C, Michalewicz Z (1991) Experimental comparison of binary and floating point representations in genetic algorithms. In: Proceedings of the fourth international conference of genetic algorithms, pp 151–157
Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: empirical results. Evolut Comp 8(2):173–195
Acknowledgments
The authors would like to thank the anonymous referees for their many useful comments and constructive suggestions. This work is supported by the National Natural Science Foundation of China under Grant No. 60574063.
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Chen, MR., Lu, YZ. & Yang, G. Multiobjective optimization using population-based extremal optimization. Neural Comput & Applic 17, 101–109 (2008). https://doi.org/10.1007/s00521-007-0118-6
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DOI: https://doi.org/10.1007/s00521-007-0118-6