This paper proposes a dynamic constrained many-objective optimization method for solving constrained optimization problems. We first convert a constrained optimization problem (COP) into an equivalent dynamic constrained many-objective optimization problem (DCMOP), then present many-objective optimization evolutionary algorithm with dynamic constraint handling mechanism, called MaDC, to solve the DCMOP, thus the COP is addressed. MaDC uses DE as the search engine, and reference-point-based nondominated sorting approach to select individuals to construct next population. The effectiveness of MaDC has been verified by comparing with peer algorithms.
CITATION STYLE
Li, X., Zeng, S., Zhang, L., & Zhang, G. (2016). Combining dynamic constrained many-objective optimization with de to solve constrained optimization problems. In Communications in Computer and Information Science (Vol. 575, pp. 64–73). Springer Verlag. https://doi.org/10.1007/978-981-10-0356-1_7
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