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Engineering Optimization Volume 46, 2014 - Issue 3
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Original Articles

Co-evolutionary algorithm: An efficient approach for bilevel programming problems

Hecheng LiDepartment of Mathematics, Key Laboratory of Tibetan Information Processing of Ministry of Education, Qinghai Normal University, Xining, PR China;Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, PR ChinaCorrespondence[email protected]
&
Lei FangBusiness School, Nankai University, Tianjin, PR China
Pages 361-376 | Received 22 Jul 2012, Accepted 10 Jan 2013, Published online: 29 Apr 2013

Citations (9)

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Read on this site (3)

Hecheng Li & Hong Li. (2023) GA/PD: a genetic algorithm based on problem decomposition for solving interval linear bilevel programming problems. Engineering Optimization 55:4, pages 564-579.
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Xiaojie Liu, Gang Du & Roger J. Jiao. (2017) Bilevel joint optimisation for product family architecting considering make-or-buy decisions. International Journal of Production Research 55:20, pages 5916-5941.
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Kai Yang, Ruiqing Zhao & Yanfei Lan. (2016) Incentive contract design in project management with serial tasks and uncertain completion times. Engineering Optimization 48:4, pages 629-651.
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Articles from other publishers (6)

Wenxin Xia, Lianshuan Shi, Rui Zhang, Jiale Zhang & Jiaxing Zhao. (2023) A Co-Evolutionary Genetic Algorithm Based on Improved K-Means Clustering. A Co-Evolutionary Genetic Algorithm Based on Improved K-Means Clustering.
Yu Shen, Hecheng Li & Lijuan Chen. (2021) A Toll-Setting Optimization Approach for Transportation Networks. A Toll-Setting Optimization Approach for Transportation Networks.
Lianghong Wu, Zhenzu Liu, Hua-Liang Wei & Rui Wang. (2021) An efficient bilevel differential evolution algorithm with adaptation of lower level population size and search radius. Memetic Computing 13:2, pages 227-247.
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Stephan Dempe. 2020. Bilevel Optimization. Bilevel Optimization 581 672 .
Nikolaos E. Karkalos, Angelos P. Markopoulos & J. Paulo DavimNikolaos E. Karkalos, Angelos P. Markopoulos & J. Paulo Davim. 2019. Computational Methods for Application in Industry 4.0. Computational Methods for Application in Industry 4.0 11 31 .
Ata Allah Taleizadeh. (2017) Stochastic Multi-Objectives Supply Chain Optimization with Forecasting Partial Backordering Rate: A Novel Hybrid Method of Meta Goal Programming and Evolutionary Algorithms. Asia-Pacific Journal of Operational Research 34:04, pages 1750021.
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