Truss topology design and sizing optimization with guaranteed kinematic stability

Shahabsafa, Mohammad; Fakhimi, Ramin; Lei, Weiming; He, Sicheng; Martins, Joaquim R. R. A.; Terlaky, Tamás; Zuluaga, Luis F.

doi:10.1007/s00158-020-02698-x

Cited by 12 publications

(2 citation statements)

References 35 publications

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“…The simultaneous topology and discrete cross-section optimization of trusses was also solved by using the NLP with heuristics [47], the relative difference quotient algorithm [48], the genetic algorithm [49,50], the ant colony approach [51,52], the improved genetic algorithm with two-level approximation [53], the mixed-integer second-order cone programming approach [54], the integrated particle swarm optimizer [55], the modified meta-heuristics with random mutation [56], the advanced Jaya algorithm [57], and by the mixed integer linear optimization (MILO) [58].…”

Section: Simultaneous Topology and Discrete Dimension Optimizationmentioning

confidence: 99%

The MINLP Approach to Topology, Shape and Discrete Sizing Optimization of Trusses

Šilih

Kravanja

2022

Applied Sciences

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The paper presents the Mixed-Integer Non-linear Programming (MINLP) approach to the synthesis of trusses. The solution of continuous/discrete non-convex and non-linear optimization problems is discussed with respect to the simultaneous topology, shape and discrete sizing optimization of trusses. A truss MINLP superstructure of different topology and design alternatives has been generated, and a special MINLP model formulation for trusses has been developed. In the optimization model, a mass objective function of the structure has been defined and subjected to design, load and dimensioning constraints. The MINLP problems are solved using the Modified Outer-Approximation/Equality-Relaxation (OA/ER) algorithm. Multi-level MINLP strategies are introduced to accelerate the convergence of the algorithm. The Modified Two-Phase and the Sequential Two-Phase MINLP strategies are proposed in order to solve highly combinatorial topology, shape and discrete sizing optimization problems. The importance of local buckling constraints on topology optimization is also discussed. Some simple numerical examples are shown at the end of the paper to demonstrate the suitability and efficiency of the proposed method.

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Section: Simultaneous Topology and Discrete Dimension Optimizationmentioning

confidence: 99%

The MINLP Approach to Topology, Shape and Discrete Sizing Optimization of Trusses

Šilih

Kravanja

2022

Applied Sciences

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“…Some papers have focused on optimizing two-and three-dimensional trusses, including cyclical parthenogenesis algorithm (CPA) (Kaveh & Zolghadr, 2017), Jaya algorithm (JA) (Degertekin et al, 2018), improved grey-wolf optimizer (IGWO) (Kaveh & Zakian, 2018), mean-variance mapping optimization (MVMO) (Aslani et al, 2018), accelerated multigravitational search algorithm (AMGSA) (Khatibinia & Yazdani, 2018), chaotic coyote algorithm (COA) (Pierezan et al, 2021), spiral water-cycle algorithm (SWCA) (Eid et al, 2021), artificial bee-colony algorithm (ABC) , parameter free Jaya algorithm (PFJA) (Degertekin et al, 2021), interactive fuzzy search algorithm (IFSA) (Mortazavi, 2021), mixed integer linear optimization (MILO) (Shahabsafa et al, 2021), hybrid artificial physics optimization and big bang-big crunch (HPBA) (Shahabsafa et al, 2021) and heuristic dragonfly algorithm (HAD) .…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of Truss Using a Hybrid Method Based on Particle Swarm Optimizer and Harris Hawk Algorithm

Yassami

2024

JJCE

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This paper presents two hybrid optimization methods known as PSOHHO and DPSOHHO optimization algorithms. In the first method, using a number of formulae, the top populations are exchanged between the two algorithms and a new population is created and in the second method, we adopted the parallel optimization and optimized its performance. In this method, unlike other parallel methods, the population does not remain constant. With this ability, the strengths of an algorithm can be used to compensate for the weaknesses of the other algorithm. In these methods, no changes are made to the algorithms. The main goal is to use existing algorithms. These methods attain the optimal solution in the shortest time possible. Two algorithms of particleswarm optimization (PSO) and Harris Hawks's optimization (HHO) are used to present this method and two truss samples and CEC209 are considered to confirm the performance of this method. Based on the results, these methods have rapid convergence speed and acceptable results compared to other methods. KEYWORDS: Meta-heuristic algorithms, Hybrid algorithm, Optimization, Dynamic hybrid algorithm, Truss.

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On optimization of lightweight planar frame structures: an evolving ground structure approach

Toragay,

Silva,

Vinel

2024

Struct Multidisc Optim

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Truss topology design and sizing optimization with guaranteed kinematic stability

Cited by 12 publications

References 35 publications

The MINLP Approach to Topology, Shape and Discrete Sizing Optimization of Trusses

The MINLP Approach to Topology, Shape and Discrete Sizing Optimization of Trusses

Optimal Design of Truss Using a Hybrid Method Based on Particle Swarm Optimizer and Harris Hawk Algorithm

On optimization of lightweight planar frame structures: an evolving ground structure approach

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