Sizing and layout design of truss structures under dynamic and static constraints with an integrated particle swarm optimization algorithm

[Display omitted] •An integrated particle swarm optimization algorithm for weight minimization of truss structures with sizing and layout variables is developed;.•Optimization problems with frequency constraints and static constraints are solved;.•The new algorithm is competitive with other state-of...

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Bibliographic Details
Published in:	Applied soft computing Vol. 51; pp. 239 - 252
Main Authors:	Mortazavi, Ali, Toğan, Vedat
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.02.2017
Subjects:	Improved fly-back mechanism Layout and sizing optimization of truss structures Particle swarm optimization Weighted particle Layout and sizing optimization of truss structures Improved fly-back mechanism Particle swarm optimization Weighted particle
ISSN:	1568-4946, 1872-9681
Online Access:	Get full text
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Summary:	[Display omitted] •An integrated particle swarm optimization algorithm for weight minimization of truss structures with sizing and layout variables is developed;.•Optimization problems with frequency constraints and static constraints are solved;.•The new algorithm is competitive with other state-of-the-art metaheuristic algorithms. Natural frequencies offer useful knowledge on the dynamic response of the structures. It is possible to avoid from the destructive effects of dynamic loads on the structures by optimizing layout and size of their subject to constraints on natural frequencies. Since optimization problems including frequency constraints are highly nonlinear, this kind of problems forms a challenging area to test the performance of the different optimization techniques. This study tests the performance of an integrated particle swarm optimization algorithm (iPSO), a new particle swarm optimizer integrated with the improved fly-back mechanism and the weighted particle concept, in four weight minimization of truss structures with sizing and layout variables under multiple frequency constraints. Optimization results demonstrate that the new algorithm is competitive with other state-of-the-art metaheuristic algorithms in dynamic and static structural optimization problems.
ISSN:	1568-4946 1872-9681
DOI:	10.1016/j.asoc.2016.11.032