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Structural and Multidisciplinary Optimization

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07-06-2019 | Research Paper

A novel self-adaptive hybrid multi-objective meta-heuristic for reliability design of trusses with simultaneous topology, shape and sizing optimisation design variables

Authors: Natee Panagant, Sujin Bureerat, Kang Tai

Published in: Structural and Multidisciplinary Optimization | Issue 5/2019

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Abstract

Performing the design of a truss including topological, shape and sizing (TSS) variables simultaneously is a challenging but important task for a designer. It is even more interesting when the design problem involves random parameters. In this paper, a novel hybrid meta-heuristic based on the whale optimisation algorithm (WOA) and success history–based adaptive differential evolution (SHADE) is developed to solve the multi-objective reliability optimisation of a truss. The reliability design problem is assigned as a bi-objective truss optimisation with mass and reliability index being the objectives. Two novel algorithms called success history–based adaptive multi-objective differential evolution (SHAMODE) and success history–based adaptive multi-objective differential evolution with whale optimisation (SHAMODE-WO) are developed in this paper. The proposed algorithms are used to solve the test problems for TSS truss reliability optimisation along with some established optimisers. Comparative results show that they are among the top algorithms in solving such design problems. Moreover, SHAMODE-WO shows obvious improvement compared to SHAMODE.

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Title: A novel self-adaptive hybrid multi-objective meta-heuristic for reliability design of trusses with simultaneous topology, shape and sizing optimisation design variables
Authors: Natee Panagant
Sujin Bureerat
Kang Tai
Publication date: 07-06-2019
Publisher: Springer Berlin Heidelberg
Published in: Structural and Multidisciplinary Optimization / Issue 5/2019
Print ISSN: 1615-147X
Electronic ISSN: 1615-1488
DOI: https://doi.org/10.1007/s00158-019-02302-x

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