Abstract
The multi-objective optimization problem is an important research direction in the field of optimization. Because the traditional mathematical programming method often cannot achieve the optimal global solution, the researchers introduced the heuristic method into the multi-objective optimization problem. The heuristic method is a method of searching based on empirical rules, which can get the optimal solution or solution set of problems in the limited search space. In this paper, we proposed a multi-objective evolutionary algorithm based on uniform design and differential evolution, which use the uniform design table to construct the weight vector and utilize the crossover in differential evolution and mutation process to replace the simulated binary intersection and the simulated polynomial variation. Compared with the classical algorithm, the experimental results show that the improved algorithm is superior to the original algorithm.
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References
Anirban, M., Ujjwal, M., Sanghamitra, B., Carlos, A.C.C.: A survey of multi-objective evolutionary algorithms for data mining. IEEE Trans. Evol. Comput. 18(1), 20–35 (2014)
Rosenberg, R.S.: Simulation of genetic populations with biochemical properties. Ph.D. thesis, University of Michigan, Michigan (1967)
Schaffer, J.D.: Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of the International Conference on Genetic Algorithms and Their Applications, pp. 93–100. L. Erlbaum Associates, Hillsdale (1985)
Fonseca, C.M., Fleming, P.J.: Genetic algorithm for multi-objective optimization: formulation, discussion and generation. In: Forrest, S., (ed.) Proceedings of the 5th International Conference on Genetic Algorithms, pp. 416–423. Morgan Kauffman Publishers, San Mateo (1993)
Srinivas, N., Deb, K.: Multi-objective optimization using non-dominated sorting in genetic algorithms. Evol. Comput. 2(3), 221–248 (1994)
Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched Pareto genetic algorithm for multi-objective optimization. In: Fogarty, T.C., (ed.) Proceedings of the 1st IEEE Congress on Evolutionary Computation, pp. 82–87. IEEE, Piscataway (1994)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength Pareto evolutionary algorithm. In: Giannakoglou, K., Tsahalis, D.T., Périaux, J., Papailiou, K.D., Fogarty, T., (eds.) Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, pp. 95–100. Springer, Berlin (2002)
Knowles, J.D., Corne, D.W.: Approximating the non-dominated front using the Pareto archived evolution strategy. Evol. Comput. 8(2), 149–172 (2000)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Moore, J., Chapman, R.: Application of particle swarm to multi-objective optimization. In: International Conference on Computer Science and Software Engineering (2003)
Ray, T., Liew, K.M.: A swarm metaphor for multi-objective design optimization. Eng. Optim. 34(2), 141–153 (2002)
Coello, C.C.A., Pulido, G.T., Lechuga, M.S.: Handing multiple objectives with particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 256–279 (2004)
Coello, C.C.A., Cortes, N.C.: Solving multi-objective optimization problems using an artificial immune system. Genet. Program. Evolv. Mach. 6(2), 163–190 (2005). https://doi.org/10.1007/s10710-005-6164-x
Luh, G.C., Chueh, C.H., Liu, W.: MOIA: multi-objective immune algorithm. Eng. Optim. 35(2), 143–164 (2003)
Khan, N., Goldberg, D.E., Pelikan, M.: Multi-objective Bayesian optimization algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference, p. 684. Morgan Kaufmann, New York (2002)
Laumanns, M., Ocenasek, J.: Bayesian optimization algorithms for multi-objective optimization. In: Guervós, J.J.M., Adamidis, P., Beyer, H.-G., Schwefel, H.-P., Fernández-Villacañas, J.-L. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 298–307. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45712-7_29
Cai, Z., Wang, Y.: A multi-objective optimization based evolutionary algorithm for constrained optimization. IEEE Trans. Evol. Comput. 10(6), 658–675 (2006)
Li, X.: A non-dominated sorting particle swarm optimizer for multiobjective optimization. In: Cantú-Paz, E., et al. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 37–48. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-45105-6_4
Jiao, L., Gong, M., Shang, R., Du, H., Lu, B.: Clonal Selection with Immune Dominance and Anergy Based Multiobjective Optimization. In: Coello Coello, Carlos A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 474–489. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-31880-4_33
Gong, M.G., Jiao, L.C., Du, H.F., et al.: Multi-objective immune algorithm with non-dominated neighbor-based selection. Evol. Comput. 16(2), 225–255 (2008)
Zhang, Q.F., Zhou, A.M., Jin, Y.: RM-MEDA: a regularity model based multi-objective estimation of distribution algorithm. IEEE Trans. Evol. Comput. 12(1), 41–63 (2007)
Liu, J.: Research on Organizational Coevolutionary Algorithm and its Applications. Ph.D. thesis. Xidian University Xi’an (2004)
Tan, K.C., Yang, Y.J., Goh, C.K.: A distributed cooperative evolutionary algorithm for multi-objective optimization. IEEE Trans. Evol. Comput. 10(5), 527–549 (2006)
Tan, Y., Zhu, Y.: Fireworks algorithm for optimization. In: Tan, Y., Shi, Y., Tan, K.C. (eds.) ICSI 2010. LNCS, vol. 6145, pp. 355–364. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13495-1_44
Zheng, Y.J., Song, Q., Chen, S.Y.: Multi-objective fireworks optimization for variable-rate fertilization in oil crop production. Appl. Soft Comput. 13(11), 4253–4263 (2013)
Xie, C., Xu, L., Xia, X., Wei, B., et al.: Multi-objective fireworks optimization algorithm using elite opposition-based learning. Acta Electronica Sinica 44(5), 1180–1188 (2016)
Acknowledgement
This work was partially supported by National Natural Science Foundation of China (61902339, 61876136), the China Postdoctoral Science Foundation (2018M633585), Natural Science Basic Research Plan in Shaanxi Province of China (No. 2018JQ6060), and Google Supported Industry-University Cooperation and Education Project, Doctoral Starting up Foundation of Yan’an University (YDBK2019-06).
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He, J., He, D., Shi, A., He, G. (2020). Multi-objective Optimization Algorithm Based on Uniform Design and Differential Evolution. 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_14
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DOI: https://doi.org/10.1007/978-981-15-5577-0_14
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