In this article, real-code population-based incremental learning (RPBIL) is extended for multi-objectiveoptimization. The optimizer search performance is then improved by integrating a mutation operator of evolution ...
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In this article, real-code population-based incremental learning (RPBIL) is extended for multi-objectiveoptimization. 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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