Hybrid real-code population-based incremental learning and approximate gradients for multi-objective truss design

作者：Pholdee Nantiwat; Bureerat Sujin^*

来源：Engineering Optimization, 2014, 46(8): 1032-1051.

DOI：10.1080/0305215X.2013.823194

• comparative performance
• multi-objective evolutionary algorithms
• multi-objective truss optimization
• hybrid evolutionary algorithms
• population-based incremental learning

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.

• 出版日期2014-8-3