Hybrid real-code population-based incremental learning and approximate gradients for multi-objective truss design |
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Authors: | Nantiwat Pholdee |
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Affiliation: | Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen, Thailand |
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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. |
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Keywords: | population-based incremental learning hybrid evolutionary algorithms multi-objective truss optimization comparative performance multi-objective evolutionary algorithms |
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