Abstract
Many-objective problems are becoming common in several real-world application domains and there is a growing interest to develop evolutionary many-objective optimizers that can solve them effectively. Studies on selection for many-objective optimization and most recently studies on the characteristics of many-objective landscapes, the effectiveness of operators of variation, and the effects of large populations have proved successful to advance our understanding of evolutionary many-objective optimization. This work proposes an evolutionary many-objective optimization algorithm that uses adaptive ε-dominance principles to select survivors and also to create neighborhoods to bias mating, so that solutions will recombine with other solutions located close by in objective space. We investigate the performance of the proposed algorithm on DTLZ continuous problems, using a short number of generations to evolve the population, varying population size from 100 to 20000 individuals. Results show that the application of adaptive ε-dominance principles for survival selection as well as for mating selection improves considerably the performance of the optimizer.
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References
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)
Coello, C., Van Veldhuizen, D., Lamont, G.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, Boston (2002)
Ishibuchi, H., Tsukamoto, N., Nojima, Y.: Evolutionary Many-Objective Optimization: A Short Review. In: Proc. IEEE Congress on Evolutionary Computation (CEC 2008), pp. 2424–2431. IEEE Press (2008)
Sato, H., Aguirre, H., Tanaka, K.: Genetic Diversity and Effective Crossover in Evolutionary Many-objective Optimization. In: Coello, C.A.C. (ed.) LION 5. LNCS, vol. 6683, pp. 91–105. Springer, Heidelberg (2011)
Aguirre, H., Oyama, A., Tanaka, K.: Adaptive Epsilon-Ranking and Distribution Search on Evolutionary Many-objective Optimization. In: Proc. 2012 Italian Workshop on Artificial Life and Evolutionary Computation: Artificial Life, Evolution and Complexity (WIVACE 2012), CD-ROM, pp. 1–12 (2012)
Kowatari, N., Oyama, A., Aguirre, H., Tanaka, K.: A Study on Large Population MOEA Using Adaptive ε-Box Dominance and Neighborhood Recombination for Many-Objective Optimization. In: Hamadi, Y., Schoenauer, M. (eds.) LION 6. LNCS, vol. 7219, pp. 86–100. Springer, Heidelberg (2012)
Kowatari, N., Oyama, A., Aguirre, H., Tanaka, K.: Analysis on Population Size and Neighborhood Recombination on Many-Objective Optimization. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN XII, Part II. LNCS, vol. 7492, pp. 22–31. Springer, Heidelberg (2012)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II, KanGAL report 200001 (2000)
Laumanns, M., Thiele, L., Deb, K., Zitzler, E.: Combining Convergence and Diversity in Evolutionary Multi-objective Optimization. Evolutionary Computation 10(3), 263–282 (2002)
Aguirre, H., Tanaka, K.: Insights on Properties of Multi-objective MNK-Landscapes. In: Proc. 2004 IEEE Congress on Evolutionary Computation, pp. 196–203. IEEE Service Center (2004)
Aguirre, H., Tanaka, K.: Working Principles, Behavior, and Performance of MOEAs on MNK-Landscapes. European Journal of Operational Research 181(3), 1670–1690 (2007)
Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable Multi-Objective Optimization Test Problems. In: Proc. 2002 Congress on Evolutionary Computation, pp. 825–830. IEEE Service Center (2002)
Zitzler, E.: Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. PhD thesis, Swiss Federal Institute of Technology, Zurich (1999)
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Aguirre, H., Oyama, A., Tanaka, K. (2013). Adaptive ε-Sampling and ε-Hood for Evolutionary Many-Objective Optimization. In: Purshouse, R.C., Fleming, P.J., Fonseca, C.M., Greco, S., Shaw, J. (eds) Evolutionary Multi-Criterion Optimization. EMO 2013. Lecture Notes in Computer Science, vol 7811. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37140-0_26
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DOI: https://doi.org/10.1007/978-3-642-37140-0_26
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