Truss topology, shape and sizing optimization by fully stressed design based on hybrid grey wolf optimization and adaptive differential evolution

ABSTRACT

A hybrid adaptive optimization algorithm based on integrating grey wolf optimization into adaptive differential evolution with fully stressed design (FSD) local search is presented in this article. Hybrid reproduction and control parameter adaptation strategies are employed to increase the performance of the algorithm. The proposed algorithm, called fully stressed design–grey wolf–adaptive differential evolution (FSD-GWADE), is demonstrated to tackle a variety of truss optimization problems. The problems have mixed continuous/discrete design variables that are assigned as simultaneous topology, shape and sizing design variables. FSD-GWADE provides competitive results and gives superior results at a higher success rate than the previous FSD-based algorithm.

KEYWORDS:

• Truss optimization
• differential evolution
• grey wolf optimization
• hybrid evolutionary algorithms
• adaptive evolutionary algorithms
• fully stressed design

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The authors are grateful for support from the Royal Golden Jubilee PhD Program [grant number PHD/0130/2557] and the Thailand Research Fund [BRG5880014].