Abstract
Many practical problems can be classified into constrained optimization problems (COPs). ε constrained differential evolution (εDE) algorithm is an effective method in dealing with the COPs. In this paper, ε constrained differential evolution algorithm with a novel local search operator(εDE-LS) is proposed by utilizing the information of the feasible individuals. In this way, we can guide the infeasible individuals to move into the feasible region more effectively. The performance of the proposed εDE-LS is evaluated by the 22 benchmark test functions. The experimental results empirically show that εDE-LS is highly competitive comparing with some other state-of-the-art approaches in constrained optimization problems.
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Yi, W., Li, X., Gao, L., Zhou, Y. (2015). ε Constrained Differential Evolution Algorithm with a Novel Local Search Operator for Constrained Optimization Problems. In: Handa, H., Ishibuchi, H., Ong, YS., Tan, K. (eds) Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Volume 1. Proceedings in Adaptation, Learning and Optimization, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-13359-1_38
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DOI: https://doi.org/10.1007/978-3-319-13359-1_38
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13358-4
Online ISBN: 978-3-319-13359-1
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