Abstract
During the last decade, multi-objective approaches to dealing with constraints in evolutionary algorithms have drawn more and more attention from researchers. In this paper, a conical area differential evolution algorithm (CADE) with dual populations is proposed for constrained optimization by borrowing the ideas of cone decomposition for bi-objective optimization. In CADE, a conical sub-population and a feasible one are designed to search the global feasible optimum along the Pareto front and the feasible segment, respectively. The conical sub-population aims to construct and utilize the Pareto front by a biased cone decomposition strategy in geometric proportion and a conical area indicator. Afterwards, neighbors in both sub-populations are adequately exploited to help each other. 13 benchmark test instances are used to assess the performance of CADE. The result reveals that CADE is capable of producing significantly competitive solutions for constraint optimization problems compared with the other popular approaches.
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References
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)
Fan, L., Liang, Y., Liu, X., Gu, X.: A hybrid evolutionary algorithm based on dominated clustering for constrained bi-objective optimization problems. In: International Conference on Computational Intelligence and Security, pp. 543–546. IEEE (2017)
Deb, K.: An efficient constraint handling method for genetic algorithms. Comput. Methods Appl. Mech. Eng. 186(2–4), 311–338 (2000)
Runarsson, T.P., Yao, X.: Stochastic ranking for constrained evolutionary optimization. IEEE Trans. Evolut. Comput. 4(3), 284–294 (2000)
Liu, J., Fan, Z., Goodman, E.: SRDE: an improved differential evolution based on stochastic ranking. In: ACM/SIGEVO Summit on Genetic and Evolutionary Computation, pp. 345–352. ACM (2009)
Ying, W., Peng, D., Xie, Y., Wu, Y.: An annealing stochastic ranking mechanism for constrained evolutionary optimization. In: International Conference on Information System and Artificial Intelligence, pp. 576–580. IEEE (2017)
Wang, Y., Cai, Z.: Combining multiobjective optimization with differential evolution to solve constrained optimization problems. IEEE Trans. Evolut. Comput. 16(1), 117–134 (2012)
Ying, W., Xu, X., Feng, Y., Wu, Y.: An efficient conical area evolutionary algorithm for bi-objective optimization. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. E95–A(8), 1420–1425 (2012)
Liang, J., Runarsson, T.P., Mezura-Montes, E., Clerc, M., Suganthan, P.N., Coello, C.A., Deb, K.: Problem definitions and evaluation criteria for the CEC 2006 special session on constrained real-parameter optimization. Technical report, Nanyang Technological University, Singapore (2006)
Huang, V., Qin, A., Suganthan, P.N.: Self-adaptive differential evolution algorithm for constrained real-parameter optimization. In: IEEE Congress on Evolutionary Computation, pp. 17–24. IEEE (2006)
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 and Grant 61503087, in part by the Fundamental Research Funds for the Central Universities, SCUT, under Grant 2017MS043, in part by the Guangdong Province Science and Technology Project under Grant 2015B010131003 and in part by the China Scholarship Council (CSC) under Grant 201406155076 and Grant 201408440193.
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Wu, B., Ying, W., Wu, Y., Xie, Y., Wang, Z. (2018). A Conical Area Differential Evolution with Dual Populations for Constrained Optimization. In: Li, K., Li, W., Chen, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information Science, vol 874. Springer, Singapore. https://doi.org/10.1007/978-981-13-1651-7_5
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DOI: https://doi.org/10.1007/978-981-13-1651-7_5
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