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Learning unified mutation operator for differential evolution by natural evolution strategies. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.03.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2023]
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Zou L, Pan Z, Gao Z, Gao J. Improving the search accuracy of differential evolution by using the number of consecutive unsuccessful updates. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Mathematical formulation and two-phase optimisation methodology for the constrained double-row layout problem. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06817-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Li C, Deng L, Qiao L, Zhang L. An efficient differential evolution algorithm based on orthogonal learning and elites local search mechanisms for numerical optimization. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2021.107636] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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A dual opposition-based learning for differential evolution with protective mechanism for engineering optimization problems. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107942] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Sun G, Li C, Deng L. An adaptive regeneration framework based on search space adjustment for differential evolution. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-05708-1] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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An Approach to Chance Constrained Problems Based on Huge Data Sets Using Weighted Stratified Sampling and Adaptive Differential Evolution. COMPUTERS 2020. [DOI: 10.3390/computers9020032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In this paper, a new approach to solve Chance Constrained Problems (CCPs) using huge data sets is proposed. Specifically, instead of the conventional mathematical model, a huge data set is used to formulate CCP. This is because such a large data set is available nowadays due to advanced information technologies. Since the data set is too large to evaluate the probabilistic constraint of CCP, a new data reduction method called Weighted Stratified Sampling (WSS) is proposed to describe a relaxation problem of CCP. An adaptive Differential Evolution combined with a pruning technique is also proposed to solve the relaxation problem of CCP efficiently. The performance of WSS is compared with a well known method, Simple Random Sampling. Then, the proposed approach is applied to a real-world application, namely the flood control planning formulated as CCP.
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Wang SL, Morsidi F, Ng TF, Budiman H, Neoh SC. Insights into the effects of control parameters and mutation strategy on self-adaptive ensemble-based differential evolution. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2019.11.046] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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