Improving evolutionary algorithm performance for integer type multi-objective building system design optimization

Xu, Wenda; Chong, Adrian; Karaguzel, Omer T.; Lam, Khee Poh

doi:10.1016/j.enbuild.2016.06.043

Cited by 38 publications

(12 citation statements)

References 14 publications

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“…error on global validation set [96] x NSGA-II -55% (27h to 12h) < 10% max. error on global validation set [107] IPOPT/NOMAD + 4% (49h to 52h) 0.7% deviation from true optimum [117] x NSGA-II n/a (7.4h to n/a) 1.59% deviation from true optimum Iterative surrogate-based [125] x NSGA-II -52.2% (14h to 7h) 3.17% difference in hypervolume of Pareto Front [72] x NSGA-II -82% (71h to 13h) 75%-80% samples of original Pareto Front found [128] x NSGA-II -60% (23h to 9h) optima are close to true Pareto Front but have low diversity (spread metric increases from 0.41 to 1.01) easily change the optimisation objective or optimizer settings without rerunning simulations. • Recently, researchers have been trying to find more general surrogates applicable to many different problems.…”

Section: Trends In the Application Of Surrogate Modelsmentioning

confidence: 99%

Surrogate modelling for sustainable building design – A review

Westermann

Evins

2019

Energy and Buildings

196

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Building design processes are dynamic and complex. The context of a building project is manifold and depends on the cultural context, climatic conditions and personal design preferences. Many stakeholders may be involved in deciding between a large space of possible designs defined by a set of influential design parameters. Building performance simulation is the state-of-the-art way to provide estimates of the energy and environmental performance of various design alternatives. However, setting up a simulation model can be labour intensive and evaluating it can be computationally costly. As a consequence, building simulations often occur towards the end of the design process instead of being an active component in design processes. This observation and the growing availability of machine learning algorithms as an aid to exploring analytical problems has lead to the development of surrogate models. The idea of surrogate models is to learn from a high-fidelity counterpart, here a building simulation model, by emulating the simulation outputs given the simulation inputs. The key advantage is their computational efficiency. They can produce performance estimates for hundreds of thousands of building designs within seconds. This has great potential to innovate the field. Instead of only being able to assess a few specific designs, entire regions of the design space can be explored, or instantaneous feedback on the sustainability of building can be given to architects during design sessions. I would like to express my thank to my supervisor, Dr. Ralph Evins, for giving me the opportunity to join him and the young Energy and Cities group in beautiful Victoria, for his guidance, and for his support to accommodate any of my plans. A special thanks goes to the rapidly growing team, which always had an open ear for my research ideas and brought in valuable input for my work. Especially I would like to thank Gaby Baasch, David Rulff, Matthias Welzel, David Fritzsche, Kevin Cant, Theo Christiaanse, and Gaëlle Faure. I also owe many thanks to Professor Arno Schlüter and the A/S research group at the Institute for Technology in Architecture, ETH Zurich, for hosting me during my visits in Zurich. Finally, I would like to express great gratitude to limitless support off-campus. Thanks to Chris Wood, Miguel Alvarez, Toby Cotton, Aurélien Liné, Claire Remington, the UVIC Field Hockey team and all the others. Thanks to my sisters and parents. Thank you, Fredi.

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Section: Trends In the Application Of Surrogate Modelsmentioning

confidence: 99%

Surrogate modelling for sustainable building design – A review

Westermann

Evins

2019

Energy and Buildings

196

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“…Moreover, authors in [26] use a Genetic Algorithm (GA) merged with a dynamic simulation tool to investigate the best retrofit opportunities and, hence, optimise the energy savings, the overall cost and the indoor thermal comfort. In addition, the work in [27] presents different optimization methods at building design level based on a multi-objective GA. The novelty of this work lies in modifying the behavior of the conventional GA by introducing adaptive operators and also changing the meta-model approach so as to enhance the convergence and speed of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Multi-Objective Meta-Heuristics for Environmental and Cost-Optimal Energy Refurbishment at District Level

et al. 2019

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Energy efficiency and environmental performance optimization at the district level are following an upward trend mostly triggered by minimizing the Global Warming Potential (GWP) to 20% by 2020 and 40% by 2030 settled by the European Union (EU) compared with 1990 levels. This paper advances over the state of the art by proposing two novel multi-objective algorithms, named Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Harmony Search (MOHS), aimed at achieving cost-effective energy refurbishment scenarios and allowing at district level the decision-making procedure. This challenge is not trivial since the optimisation process must provide feasible solutions for a simultaneous environmental and economic assessment at district scale taking into consideration highly demanding real-based constraints regarding district and buildings’ specific requirements. Consequently, in this paper, a two-stage optimization methodology is proposed in order to reduce the energy demand and fossil fuel consumption with an affordable investment cost at building level and minimize the total payback time while minimizing the GWP at district level. Aimed at demonstrating the effectiveness of the proposed two-stage multi-objective approaches, this work presents simulation results at two real district case studies in Donostia-San Sebastian (Spain) for which up to a 30% of reduction of GWP at district level is obtained for a Payback Time (PT) of 2–3 years.

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“…James and Azad [17] presented two case studies on the use of agent-based modelling in the design of complex systems. Xu et al [18] proposed two improved strategies for supporting system design optimization. Adaptive meta-model approaches were also proposed.…”

Section: Related Workmentioning

confidence: 99%

“…The result comparisons with the 6 surrogate models are detailed in the next section. We use the same structure (see Figure 7) to construct the 6 surrogate models, namely, BPN1, BPN2, BPN3, BPN4, BPN5, and BPN6, with the numbers of design parameters: 12,16,18,20,25, and 29, respectively. When a parameter in Table 2 is not chosen as a design variable, its value is fixed to the middle of its range values.…”

Section: Setting Up the System Surrogate Modelmentioning

confidence: 99%

A Systematic Optimization Design Method for Complex Mechatronic Products Design and Development

Jiang

Ding

Zhang

et al. 2018

Mathematical Problems in Engineering

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Designing a complex mechatronic product involves multiple design variables, objectives, constraints, and evaluation criteria as well as their nonlinearly coupled relationships. The design space can be very big consisting of many functional design parameters, structural design parameters, and behavioral design (or running performances) parameters. Given a big design space and inexplicit relations among them, how to design a product optimally in an optimization design process is a challenging research problem. In this paper, we propose a systematic optimization design method based on design space reduction and surrogate modelling techniques. This method firstly identifies key design parameters from a very big design space to reduce the design space, secondly uses the identified key design parameters to establish a system surrogate model based on data-driven modelling principles for optimization design, and thirdly utilizes the multiobjective optimization techniques to achieve an optimal design of a product in the reduced design space. This method has been tested with a high-speed train design. With comparison to others, the research results show that this method is practical and useful for optimally designing complex mechatronic products.

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Improving evolutionary algorithm performance for integer type multi-objective building system design optimization

Cited by 38 publications

References 14 publications

Surrogate modelling for sustainable building design – A review

Surrogate modelling for sustainable building design – A review

Two-Stage Multi-Objective Meta-Heuristics for Environmental and Cost-Optimal Energy Refurbishment at District Level

A Systematic Optimization Design Method for Complex Mechatronic Products Design and Development

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