Abstract
Multi-objective evolutionary algorithms (MOEAs) are popular for solving many-objective knapsack problems. Among various MOEAs, the multi-objective evolutionary algorithm based on decomposition (MOEA/D) behaves well. However, MOEA/D often retains multiple copies of one individual in the population, which might hamper the diversity of the population. To overcome the disadvantage, a collaborative evolutionary algorithm based on decomposition and dominance, called MOEA/D-DDC, is presented in this paper. It mainly adopts a decomposition-dominance collaboration mechanism. The mechanism consists of a decomposition-based population and a dominance-based archive. The decomposition-based population collects elite individuals for the dominance-based archive. Meanwhile the dominance-based archive assists to repair the decomposition-based population and heighten the diversity. The experiment results show that MOEA/D-DDC obtains the better set of solutions than MOEA/D for many-objective knapsack problems with 4 to 8 objectives.
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References
Ishibuchi, H., Kaige, S., Narukawa, K.: Comparison between Lamarckian and Baldwinian repair on multiobjective 0/1 knapsack problems. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 370–385. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-31880-4_26
Ishibuchi, H., Akedo, N., Nojima, Y.: Behavior of multiobjective evolutionary algorithms on many-objective knapsack problems. IEEE Trans. Evol. Comput. 19(2), 264–283 (2015)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)
Wang, Y., Cai, Z.: A constrained optimization evolutionary algorithm based on multiobjective optimization techniques. In: IEEE Congress on Evolutionary Computation, pp. 1081–1087. IEEE (2005)
Bader, J., Zitzler, E.: HypE: an algorithm for fast hypervolume-based many-objective optimization. Evol. Comput. 19(1), 45–76 (2011)
Köppen, M., Yoshida, K.: Substitute distance assignments in NSGA-II for handling many-objective optimization problems. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 727–741. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-70928-2_55
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Beumea, N., Emmerich, M.: SMS-EMOA: multiobjective selection based on dominated hypervolume. Eur. J. Oper. Res. 181(3), 1653–1669 (2007)
Zhang, Q., Hui, L.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)
Wang, Z., Zhang, Q., Zhou, A., Gong, M., Jiao, L.: Adaptive replacement strategies for MOEA/D. IEEE Trans. Cybern. 46(2), 474–486 (2017)
Singh, H.K., Isaacs, A., Ray, T.: A Pareto corner search evolutionary algorithm and dimensionality reduction in many-objective optimization problems. IEEE Trans. Evol. Comput. 15(4), 539–556 (2011)
Cai, X., Yang, Z., Fan, Z., Zhang, Q.: Decomposition-based-sorting and angle-based-selection for evolutionary multiobjective and many-objective optimization. IEEE Trans. Cybern. 47(9), 2824C–2837 (2017)
Freire, H., de Moura Oliveira, P.B., Solteiro Pires, E.J., Bessa, M.: Many-objective optimization with corner-based search. Memetic Comput. 7(2), 105–118 (2015). https://doi.org/10.1007/s12293-015-0151-4
Das, I., Dennis, J.E.: Normal-boundary intersection: a new method for generating the Pareto surface in nonlinear multicriteria optimization problems. SIAM J. Optim. 8(3), 631–657 (1998)
Li, K., Deb, K., Zhang, Q., Kwong, S.: An evolutionary many-objective optimization algorithm based on dominance and decomposition. IEEE Trans. Evol. Comput. 19(5), 694–716 (2014)
Acknowledgments
This work was supported in part by the Natural Science Foundation of Guangdong Province, China, under Grant 2015A030313204, in part by the Pearl River S&T Nova Program of Guangzhou under Grant 2014J2200052, in part by the National Natural Science Foundation of China under Grant 61203310, Grant 61503087 and Grant 61702239, in part by the Fundamental Research Funds for the Central Universities, SCUT, under Grant 2017MS043, and the Project of Foshan Science and Technology Innovation Foundation under Grant 2016AG100291.
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Huang, H., Ying, W., Wu, Y., Zheng, K., Peng, S. (2020). A Collaborative Evolutionary Algorithm Based on Decomposition and Dominance for Many-Objective Knapsack Problems. In: Li, K., Li, W., Wang, H., Liu, Y. (eds) Artificial Intelligence Algorithms and Applications. ISICA 2019. Communications in Computer and Information Science, vol 1205. Springer, Singapore. https://doi.org/10.1007/978-981-15-5577-0_12
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