Divergent exploration in design with a dynamic multiobjective optimization formulation

Curtis, Shane Keawe; Mattson, Christopher A.; Hancock, Braden J.; Lewis, Patrick K.

doi:10.1007/s00158-012-0855-8

Cited by 11 publications

(4 citation statements)

References 30 publications

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“…'Ω is the diagonal of the hypercube containing the entire formulation objective space, Ω 0 ð Þ is the diagonal of the hypercube containing the original design objective space, and in general Ω i ð Þ is the diagonal of the hypercube containing the i-th space' (Curtis et al 2013) A12, A13…”

Section: Results From the Investigation Target Related To Q3mentioning

confidence: 99%

Uses of the novelty metrics proposed by Shah et al.: what emerges from the literature?

Fiorineschi

Rotini

2023

Des. Sci.

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Several concepts and types of procedures for assessing novelty and related concepts exist in the literature. Among them, the two approaches originally proposed by Shah and colleagues are often considered by scholars. These metrics rely on well-defined novelty types and a specific concept of novelty; however, more than 20 years after the first publication, it is still not clear whether and to what extent these metrics are actually used, why they are used and how. Through a comprehensive review of the papers citing the main work of Shah, Vargas-Hernandez & Smith (2003a, 2003b) (the main study where the metrics are comprehensively described and applied), the present work aims to bridge this gap. The results highlight that only a few of the citing papers actually use the assessment approach proposed by Shah et al. and that a nonnegligible number uses a modified or adapted version of the original metrics. Furthermore, several criticalities in the application of the metrics have been uncovered, which are expected to provide relevant information for scholars involved in reliable and repeatable novelty assessments.

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Section: Results From the Investigation Target Related To Q3mentioning

confidence: 99%

Uses of the novelty metrics proposed by Shah et al.: what emerges from the literature?

Fiorineschi

Rotini

2023

Des. Sci.

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“…They include at least two kinds: algorithms that solve dynamic MOO problems without adapting the problems, and algorithms that convert a dynamic MOO problem into multiple static MOO problems [57]. Trabelsi et al's example [58] was solved using the former algorithm; while Curtis et al's two examples [59] were solved using the latter algorithm. Curtis et al's examples also showed that the conversion or re-formulation of a dynamic MOO problem can be done by iteratively adding and removing objectives and design variables, and that this re-formulation can extend the exploration divergently into a larger space in order to avoid missing potentially superior solutions.…”

Section: Type 1 Methods With Non-dynamic and Non-interactive Re-opfmentioning

confidence: 99%

Dynamic and interactive re-formulation of multi-objective optimization problems for conceptual architectural design exploration

Ding

Stefano²,

Turrin

et al. 2020

Automation in Construction

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Simulation-Based Multi-Objective Optimization (SBMOO) methods are being increasingly used in conceptual architectural design. They mostly focus on the solving, rather than the re-formulation, of a Multi-Objective Optimization (MOO) problem. However, Optimization Problem Re-Formulation (Re-OPF) is necessary for treating ill-defined conceptual architectural design as an iterative exploration process. The paper proposes an innovative SBMOO method which builds in a dynamic and interactive Re-OPF phase. This Re-OPF phase, as the main novelty of the proposed method, aims at achieving a realistic MOO model (i.e., a parametric geometrysimulation model which includes important objectives, constraints, and design variables). The proposed method is applied to the conceptual design of a top-daylighting system, focusing on divergent concept generation. The integration of software tools Grasshopper and modeFRONTIER is adopted to support this application. The main finding from this application is that the proposed method can help to achieve quantitatively better and qualitatively more diverse Pareto solutions.

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“…In recent years, there are much literature on DMOPs. These literature show that it is widely used in engineering and research on DMOPs in various application areas [1][2][3][4][5][6][7][8][9][10]. Meanwhile, some other scientific problems, such as intelligent learning [11], bilevel optimization [12], can be solved by means of DMOPs.…”

Section: Introductionmentioning

confidence: 99%

An environmental selection and transfer learning-based dynamic multiobjective optimization evolutionary algorithm

Xiang

Ren

2022

Nonlinear Dyn

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In recent years, the dynamic multiobjective optimization problems (DMOPs), whose major strategy is to track the varying PS (Pareto Optimal Solution, PS) and/or PF (Pareto Optimal Frontier), caused a great deal of attention worldwide. As a promising solution, reusing of "experiences" to establish a prediction model is proved to be very useful and widely used in practice. However, most existing methods overlook the importance of environmental selection in the evolutionary processes. In this paper, we propose a dynamic multiobjective optimal evolutionary algorithm which is based on environmental selection and transfer learning (DMOEA-ESTL). This approach makes full use of the environmental selection and transfer learning technique to generate individuals for a new environment by reusing experience to maintain the diversity of the population and speed up the evolutionary process. As experimental validation, we embed this new scheme in the NSGA-II (non-dominated sorting genetic algorithm). We test the proposed algorithm with the help of six benchmark functions as well as compare it with the other two prediction based strategies FPS (Forward-looking Prediction Strategy, FPS) and PPS (Population Prediction Strategy, PPS). The experimental results testify that the proposed strategy can deal with the DMOPs effectively.

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Divergent exploration in design with a dynamic multiobjective optimization formulation

Cited by 11 publications

References 30 publications

Uses of the novelty metrics proposed by Shah et al.: what emerges from the literature?

Uses of the novelty metrics proposed by Shah et al.: what emerges from the literature?

Dynamic and interactive re-formulation of multi-objective optimization problems for conceptual architectural design exploration

An environmental selection and transfer learning-based dynamic multiobjective optimization evolutionary algorithm

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