Interactive truss design using Particle Swarm Optimization and NURBS curves

Felkner, Juliana; Chatzi, Eleni; Kotnik, Toni

doi:10.1016/j.jobe.2015.08.004

Cited by 15 publications

(8 citation statements)

References 47 publications

(54 reference statements)

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“…The literature also contains some recent techniques [36] to interactively prune the Pareto Front solution set at each generation of multi-objective GA, which helps to reduce the size of the Pareto front and to obtain the desired Pareto optimal solutions at the end of the evolutionary process. Recently, few researchers have diverted also their attention in utilising other meta-heuristic algorithms such as Particle Swarm [20] and Teaching-Learning-Based Optimisation [18] to develop IECbased design systems.…”

Section: Interactive Designmentioning

confidence: 99%

“…In the latter approaches, users are interactively involved at each iteration/generation of an optimiser and guide the optimisation process towards the promising regions of the design space. In this approach, an initial population is first created consisting of randomly sampled designs, and a user then performs interaction for selecting a design [20] or he/she can rate all the designs shown [21]. The optimiser then performs an iteration to generate designs similar to the selected or highly-rated design(s).…”

Section: Introductionmentioning

confidence: 99%

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GenYacht: An interactive generative design system for computer-aided yacht hull design

2019

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In the present work, a new digital design system, GenYacht, is proposed for the creation of optimal and user-centred yacht hull forms. GenYacht is a hybrid system involving generative and interactive design approaches, which enables users to create a variety of design alternatives. Among them, a user can select a hull design with desirable characteristics based on its appearance and hydrostatics/hydrodynamic performance. GenYacht first explores a given design space using a generative design technique (GDT), which creates uniformly distributed designs satisfying the given design constraints. These designs are then presented to a user and single or multiple designs are selected based on the user's requirements. Afterwards, based on the selections, the design space is refined using a novel space-shrinking technique (SST). In each interaction, SST shrinks the design space, which is then fed into GDT to create new designs in the shrank space for the next interaction. This shrinkage of design space guides the exploration process and focuses the computational efforts on user-preferred regions. The interactive and generative design steps are repeated until the user reaches a satisfactory design(s). The efficiency of GenYacht is demonstrated via experimental and user studies and its performance is compared with interactive genetic algorithms.

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Section: Interactive Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GenYacht: An interactive generative design system for computer-aided yacht hull design

2019

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“…According to Takagi, this is a category of methods where the user plays the role of the evaluator in an evolutionary process (Takagi, 2001) (Petiot et al). The approach of IECs has already been used in the context of product design for eyeglass frames (Yanagisawa and Fukuda, 2004), car's silhouettes (Cluzel et al, 2010), Cola bottles (Kelly, 2008) , truss stuctures (Felkner et al, 2015), and html style sheets (Takagi, 2001).…”

Section: Current State Of Research On Interactive Optimizationmentioning

confidence: 99%

Adding Implicit Measurement Methods to Interactive Optimizations in Industrial Design - A Concept, First Tests, and Comparison Using Two Simple Case Studies

Wiesner

Petrow

Vajna

2019

Proc. Int. Conf. Eng. Des.

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In this article, a new approach to interactive optimization in industrial design is presented in which, for the first time, implicit preference acquisition methods are integrated. Suitable methods for preference acquisition will be selected, adapted and combined with an own PSO-inspired algorithm. The application of implicit preferences as well as the combined application of implicit and explicit preferences in an interactive optimization represents the main novelty of this contribution since this has not yet been carried out according to the current state of knowledge.Two case studies will be used to test this new approach with regard to convergence and acceptance, and a comparison will be made between the three different kind of optimization (implicit, explicit as well as a combination of both) in terms of their results.

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“…In the same time, the number K is the rank of the linear system of eqns (10). In this sense, the optimization problem reduces to finding appropriate combination of K members appropriately selected from all N members, the forces in which obey the condition (12); all forces which respect the latter conditions create the basis.…”

Section: Preliminary Considerationsmentioning

confidence: 99%

“…In [10], modified teaching -learning based optimization is presented, while in [11], the charged system search algorithm and its enhanced version are used for optimizing various truss structures with multiple frequency constraints. Interactive truss design using Particle Swarm Optimization and NURBS curves is applied in [12]. In the publication, [13], simulated annealing, and in [14], hybrid genetic algorithms, are used to optimize trusses.…”

Section: Introductionmentioning

confidence: 99%

Optimal Shape of Truss Structures Supporting Protective Walls

Procházka

Válek

2017

WIT Transactions on the Built Environment

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In this paper, design of trusses supporting a protective wall is discussed. The wall is assumed as sufficiently bearable and fully distributes the load caused by explosion. The problem of the assessment is narrowed to the optimal design for minimum overall mass of the trusses. The reason why the problem of such a protective system is often focused on the optimal shape of trusses is the fact that their members may be considered as a system of springs, which are a suitable means of dissipating energy induced by the effect of load due to explosion. The explosion is initiated in space or directly on the surface of the wall. In our case, vertical walls are considered in 2D; simple generalization of the proposed procedure described below can lead to the optimization of trusses that are curved both in the plane and in space. The algorithms put forward can be applied well for these generalized cases of geometry of the trusses. For various load scenarios, depending on the center of explosion, the optimal shape of truss columns are found using a nonlinear programming, based on the extended Simplex method. An important circumstance is the introduction of buckling effect in the compressed members. The joints are fixed in the plane of the truss and the joint connections are selected in such a way that the minimum mass of the whole truss is attained. The approach leading to the optimal shape is briefly described. It is taken into account that the optimal structure of the truss is achieved exclusively for a statically determinate structure, which always exhibits better results than the indeterminate one. Several typical examples accompany the proposed theory.

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Interactive truss design using Particle Swarm Optimization and NURBS curves

Cited by 15 publications

References 47 publications

GenYacht: An interactive generative design system for computer-aided yacht hull design

GenYacht: An interactive generative design system for computer-aided yacht hull design

Adding Implicit Measurement Methods to Interactive Optimizations in Industrial Design - A Concept, First Tests, and Comparison Using Two Simple Case Studies

Optimal Shape of Truss Structures Supporting Protective Walls

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