Abstract
This study introduces a multiobjective memetic algorithm for multiobjective unconstrained binary quadratic programming problem (mUBQP). It integrates multiobjective evolutionary algorithm based on decomposition and tabu search to search an approximate Pareto front with good convergence and diversity. To further enhance the search ability, uniform generation is introduced to generate different uniform weight vectors for decomposition in every generation. The proposed algorithm is tested on 50 mUBQP instances. Experimental results show the effectiveness of the proposed algorithm in solving mUBQP.
Supported by the Natural Science Foundation of Guangdong Province of China (2018A0303130055, 2018A030310664, 2019A1515012048) and the Opening Project of Guangdong Key Laboratory of Big Data Analysis and Processing (202001).
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Notes
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Eight CPUs with Intel Xeon (Cascade Lake) Platinum 8269CY at 2.5GHz, 16.0 GB of RAM.
References
Kochenberger, G., et al.: The unconstrained binary quadratic programming problem: a survey. J. Comb. Optim. 28(1), 58–81 (2014). https://doi.org/10.1007/s10878-014-9734-0
Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman, New York (1979)
Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. 35(3), 268–308 (2003)
Anacleto, E.A., Meneses, C.N., Ravelo, S.V.: Closed-form formulas for evaluating r-flip moves to the unconstrained binary quadratic programming problem. Comput. Oper. Res. 113, 104774 (2020)
Glover, F., Hao, J.K.: f-Flip strategies for unconstrained binary quadratic programming. Ann. Oper. Res. 238(1), 651–657 (2016)
Shi, J., Zhang, Q., Derbel, B., Liefooghe, A.: A parallel tabu search for the unconstrained binary quadratic programming problem. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 557–564 (2017)
Gu, S., Hao, T., Yao, H.: A pointer network based deep learning algorithm for unconstrained binary quadratic programming problem. Neurocomputing 390, 1–11 (2020)
Chen, M., Chen, Y., Du, Y., Wei, L., Chen, Y.: Heuristic algorithms based on deep reinforcement learning for quadratic unconstrained binary optimization. Knowl. Based Syst. 207, 106366 (2020)
Liefooghe, A., Verel, S., Hao, J.K.: A hybrid metaheuristic for multiobjective unconstrained binary quadratic programming. Appl. Soft Comput. 16, 10–19 (2014)
Zhou, Y., Wang, J., Yin, J.: A directional-biased tabu search algorithm for multi-objective unconstrained binary quadratic programming problem. In: 2013 Sixth International Conference on Advanced Computational Intelligence, pp. 281–286. IEEE (2013)
Liefooghe, A., Verel, S., Paquete, L., Hao, J.-K.: Experiments on local search for Bi-objective unconstrained binary quadratic programming. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C.C. (eds.) EMO 2015, Part I. LNCS, vol. 9018, pp. 171–186. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-15934-8_12
Zhou, Y., Wang, J., Wu, Z., Wu, K.: A multi-objective tabu search algorithm based on decomposition for multi-objective unconstrained binary quadratic programming problem. Knowl. Based Syst. 141, 18–30 (2018)
Zangari, M., Pozo, A., Santana, R., Mendiburu, A.: A decomposition-based binary ACO algorithm for the multiobjective UBQP. Neurocomputing 246, 58–68 (2017)
Zhou, Y., Kong, L., Wu, Z., Liu, S., Cai, Y., Liu, Y.: Ensemble of multi-objective metaheuristic algorithms for multi-objective unconstrained binary quadratic programming problem. Appl. Soft Comput. 81, 105485 (2019)
Zhang, Q., Li, H.: MOEA/D: a multi-objective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)
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)
Paquete, L., Schiavinotto, T., Stützle, T.: On local optima in multiobjective combinatorial problems. Ann. Oper. Res. 156(1), 83–97 (2007)
Tricoire, F.: Multi-directional local search. Comput. Oper. Res. 39(12), 3089–3101 (2012)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength Pareto evolutionary algorithm. In: Giannakoglou, K., Tsahalis, D.T., Periaux, J., Papailiou, K.D., Fogarty, T. (eds.) Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, pp. 95–100. CIMNE, Barcelona (2002)
Shang, K., Ishibuchi, H.: A new hypervolume-based evolutionary algorithm for many-objective optimization. IEEE Trans. Evol. Comput. 24(5), 839–852 (2020)
Wilcoxon, F.: Individual comparisons by ranking methods. Biome. Bull. 1(6), 80–83 (1945)
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Zhou, Y., Kong, L., Yan, L., Liu, S., Hong, J. (2021). A Multiobjective Memetic Algorithm for Multiobjective Unconstrained Binary Quadratic Programming Problem. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science(), vol 12690. Springer, Cham. https://doi.org/10.1007/978-3-030-78811-7_3
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