Abstract
In order to balance relationships between objective functions and constraints, this paper proposes a multi-strategy mutation constrained differential evolution algorithm based on the replacement and restart mechanism (MCODE). Due to the feasible rule as the constraint processing technology, MCODE utilizes multi-strategy mutation to balance the relationship between the constraints and the objective functions. Moreover, MCODE employs the replacement and restart mechanism to improve the diversity for jumping out of the local solution of the infeasible area. The comparison with the other four constrained optimization methods on the 18 CEC2010 test functions shows that MCODE achieves a relatively competitive result.
Supported by the National Natural Science Foundation of China (61563012, 61203109), Guangxi Natural Science Foundation (2014GXNSFAA118371, 2015GXNSFBA139260).
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References
Wang, Y., Cai, Z.: Constrained evolutionary optimization by means of \((u + \lambda )\)-differential evolution and improved adaptive trade-off model. Evol. Comput. 19, 249–285 (2011)
Kusakci, A.O., Can, M.: An adaptive penalty based covariance matrix adaptation-evolution strategy. Comput. Oper. Res. 40, 2398–2417 (2013)
Takahama, T., Sakai, S.: Efficient constrained optimization by the \(\varepsilon \) constrained adaptive differential evolution. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2010)
Takahama, T., Sakai, S.: Constrained optimization by the \(\varepsilon \) constrained differential evolution with an archive and gradient-based mutation. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1–9 (2010)
Wang, Y., Cai, Z.: A dynamic hybrid framework for constrained evolutionary optimization. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 42, 203–217 (2012)
Wang, Y., Cai, Z.: Combining multiobjective optimization with differential evolution to solve constrained optimization problems. IEEE Trans. Evol. Comput. 16, 117–134 (2012)
Deb, K.: An efficient constraint handling method for genetic algorithms. Comput. Methods Appl. Mech. Eng. 186, 311–338 (2000)
Wang, Y., Cai, Z., Guo, G., Zhou, Y.: Multiobjective optimization and hybrid evolutionary algorithm to solve constrained optimization problems. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 37, 560–575 (2007)
Wang, Y., Wang, B.C., Li, H.X., Yen, G.G.: Incorporating objective function information into the feasibility rule for constrained evolutionary optimization. IEEE Trans. Cybern. 46, 2938–2952 (2016)
Wang, B.C., Li, H.X., Li, J.P., Wang, Y.: Composite differential evolution for constrained evolutionary optimization. IEEE Trans. Syst. Man Cybern.: Syst. 1–14 (2018)
Mallipeddi, R., Suganthan, P.N.: Problem definitions and evaluation criteria for the CEC 2010: competition on constrained real-parameter optimization. Nanyang Technological University, Singapore, vol. 24 (2010)
Mallipeddi, R., Suganthan, P.N.: Differential evolution with ensemble of constraint handling techniques for solving CEC 2010 benchmark problems. In: IEEE Congress on Evolutionary Computation, pp. 1–8 (2010)
Zhang, W., Yen, G.G., He, Z.: Constrained optimization via artificial immune system. IEEE Trans. Cybern. 44, 185–198 (2014)
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Tong, L., Dong, M., Jing, C. (2019). Multi-strategy Mutation Constrained Differential Evolution Algorithm Based on Replacement and Restart Mechanism. In: Sun, Y., Lu, T., Xie, X., Gao, L., Fan, H. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2018. Communications in Computer and Information Science, vol 917. Springer, Singapore. https://doi.org/10.1007/978-981-13-3044-5_6
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DOI: https://doi.org/10.1007/978-981-13-3044-5_6
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