A general indirect representation for optimization of generative design systems by genetic algorithms: Application to a shape grammar-based design system

Granadeiro, Vasco; Pina, Luís; Duarte, José Pinto; Correia, João R.; Leal, Vítor

doi:10.1016/j.autcon.2013.05.012

Cited by 30 publications

(14 citation statements)

References 23 publications

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“…On the other hand, in shape grammar design variations are generated by synthesizing different features into the initial design with the aid of geometric rules, which ensure the creation of feasible shapes [19]. Some common applications of shape grammars are architectural design [23], 2D automotive design [24], embroidery design [25], and wheel shapes with the integration of Finite Element Analysis (FEA) [26]. L-systems [27] are a variation of shape grammars, and utilize a set of production rules based on string rewriting mechanisms to formalize design alternatives.…”

Section: Generative Designmentioning

confidence: 99%

Evolving a Psycho-Physical Distance Metric for Generative Design Exploration of Diverse Shapes

Khan

Günpinar

Moriguchi

et al. 2019

Journal of Mechanical Design

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In this paper, a generative design approach is proposed that involves the users’ psychological aspect in the design space exploration stage to create distinct design alternatives. Users’ perceptual judgment about designs is extracted as a psycho-physical distance metric, which is then integrated into the design exploration step to generate design alternatives for the parametric computer-aided design (CAD) shapes. To do this, a CAD model is first parametrized by defining geometric parameters and determining ranges of these parameters. Initial design alternatives for the CAD model are generated using Euclidean distance-based sampling teaching–learning-based optimization (S-TLBO), which is recently proposed and can sample N space-filling design alternatives in the design space. Similar designs are then clustered, and a user study is conducted to capture the subjects’ perceptual response for the dissimilarities between the cluster pairs. In addition, a furthest-point-sorting technique is introduced to equalize the number of designs in the clusters, which are being compared by the subjects in the user study. Afterward, nonlinear regression analyses are carried out to construct a mathematical correlation between the subjects’ perceptual response and geometric parameters in the form of a psycho-physical distance metric. Finally, a psycho-physical distance metric obtained is utilized to explore distinct design alternatives for the CAD model. Another user study is designed to compare the diversification between the designs when the Euclidean and the suggested psycho-physical distance metrics are utilized. According to the user study, designs generated with the latter metric are more distinct.

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Section: Generative Designmentioning

confidence: 99%

Evolving a Psycho-Physical Distance Metric for Generative Design Exploration of Diverse Shapes

Khan

Günpinar

Moriguchi

et al. 2019

Journal of Mechanical Design

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show abstract

“…Shape grammars are a successful and popular tool for modelling the 3D textual features of building facades in video games [16]. They have also been used to generate floor plans [17][18][19], but their success in this area is limited to particular styles that do not reflect typical modern-day architecture.…”

Section: Previous Approaches To Automating Architectural Designmentioning

confidence: 99%

Automated Design of Architectural Layouts Using a Multi-Objective Evolutionary Algorithm

Chia

While

2014

Lecture Notes in Computer Science

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Abstract. The internal layouts of buildings in video games are usually designed by hand, but the increasingly expansive and realistic nature of virtual worlds introduces scalability issues which make manual design methods impractical. We present a new methodology that uses a multiobjective evolutionary algorithm to automatically generate building layouts. The method accepts highly versatile input constraints, encoding layouts using a flexible binary tree representation and evaluating them on a range of criteria to ensure authentic results. Tests demonstrate that the method works well for a variety of problem instances representing an apartment, a family house, and an office floor; the ability to generate solutions for different types of buildings and to incorporate non-rectangular spaces shows greater versatility than many previous methods.

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“…These algorithms require a lot of time to achieve an optimal solution because they are searching from a single point and attempting to collect much information [1]. Traditional algorithms, such as Hill climbing, Simulated Annealing (SA), and Random Search, and then, metaheuristic algorithms have been used to solve complex optimization problems [2]. However, these algorithms also have some deficiencies in terms of finding global optima due to having different constraints in real-world applications, namely, engineering design problems [3], task planning problems, and economical problems [4].…”

Section: Introductionmentioning

confidence: 99%

Chaotic fitness-dependent optimizer for planning and engineering design

Mohammed¹,

Rashid

2021

Soft Comput

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Fitness Dependent Optimizer (FDO) is a recent metaheuristic algorithm that mimics the reproduction behavior of the bee swarm in finding better hives. This algorithm is similar to Particle Swarm Optimization (PSO) but it works differently. The algorithm is very powerful and has better results compared to other common metaheuristic algorithms. This paper aims at improving the performance of FDO, thus, the chaotic theory is used inside FDO to propose Chaotic FDO (CFDO). Ten chaotic maps are used in the CFDO to consider which of them are performing well to avoid local optima and finding global optima. New technic is used to conduct population in specific limitation since FDO technic has a problem to amend population. The proposed CFDO is evaluated by using 10 benchmark functions from CEC2019. Finally, the results show that the ability of CFDO is improved. Singer map has a great impact on improving CFDO while the Tent map is the worst. Results show that CFDO is superior to GA, FDO, and CSO. Both CEC2013 and CEC2005 are used to evaluate CFDO.Finally, the proposed CFDO is applied to classical engineering problems, such as pressure vessel design and the result shows that CFDO can handle the problem better than WOA, GWO, FDO, and CGWO. Besides, CFDO is applied to solve the task assignment problem and then compared to the original FDO. The results prove that CFDO has better capability to solve the problem.

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A general indirect representation for optimization of generative design systems by genetic algorithms: Application to a shape grammar-based design system

Cited by 30 publications

References 23 publications

Evolving a Psycho-Physical Distance Metric for Generative Design Exploration of Diverse Shapes

Evolving a Psycho-Physical Distance Metric for Generative Design Exploration of Diverse Shapes

Automated Design of Architectural Layouts Using a Multi-Objective Evolutionary Algorithm

Chaotic fitness-dependent optimizer for planning and engineering design

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