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• W2019435463 abstract "In this article, real-code population-based incremental learning (RPBIL) is extended for multi-objective optimization. The optimizer search performance is then improved by integrating a mutation operator of evolution strategies and an approximate gradient into its computational procedure. RPBIL and its variants, along with a number of established multi-objective evolutionary algorithms, are then implemented to solve four multi-objective design problems of trusses. The design problems are posted to minimize structural mass and compliance while fulfilling stress constraints. The comparative results based on a hypervolume indicator show that the proposed hybrid RPBIL is the best performer for the large-scale truss design problems." @default.
• W2019435463 created "2016-06-24" @default.
• W2019435463 creator A5014397651 @default.
• W2019435463 creator A5059825118 @default.
• W2019435463 date "2013-09-19" @default.
• W2019435463 modified "2023-10-16" @default.
• W2019435463 title "Hybrid real-code population-based incremental learning and approximate gradients for multi-objective truss design" @default.
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• W2019435463 doi "https://doi.org/10.1080/0305215x.2013.823194" @default.
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