Multi-objective optimization of cellular fenestration by an evolutionary algorithm

Wright, Jonathan A.; Brownlee, Alexander E. I.; Mourshed, Monjur; Wang, Mengchao

doi:10.1080/19401493.2012.762808

Cited by 50 publications

(37 citation statements)

References 25 publications

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“…Previous publications [9,30] have presented comparisons and analysis of results from several multi-objective evolutionary algorithms applied to this problem. The focus of the present paper is on mining a surrogate model of the problem, rather than on the optimisation process, so for convenience we replicate the best set of results from [30].…”

Section: Optimisation Resultsmentioning

confidence: 99%

“…This is the unweighted sum total of the energy used by heating, cooling and lighting systems over a specified period in a particular set of environmental circumstances. This is relatively complex as the energy consumption for the different systems varies with the glazing in different ways [30]. Electric lighting demand is reduced by incoming sunlight.…”

Section: Energymentioning

confidence: 99%

“…The problem forming the focus of our case study has previously been presented in [9,29,30]. We seek to optimise the size, shape and position of windows placed on a building façade; the goal is a design which minimises energy use and capital construction cost.…”

Section: Case Study: Cellular Windowsmentioning

confidence: 99%

“…They have gained popularity in recent years, primarily as a means of achieving speed up: a computationally cheap surrogate can take the place of a costly fitness function such as a longrunning simulation or a human-in-the-loop evaluation (e.g. [7,8,19,21,30]). …”

Section: Introductionmentioning

confidence: 99%

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Mining Markov Network Surrogates for Value-Added Optimisation

Brownlee

2016

Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion

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Surrogate fitness functions are a popular technique for speeding up metaheuristics, replacing calls to a costly fitness function with calls to a cheap model. However, surrogates also represent an explicit model of the fitness function, which can be exploited beyond approximating solution fitness. This paper proposes that mining surrogate fitness models can yield useful additional information on the problem to the decision maker, adding value to the optimisation process. An existing fitness model based on Markov networks is presented and applied to the optimisation of glazing on a building façade. Analysis of the model reveals how its parameters point towards the global optima of the problem after only part of the optimisation run, and reveals useful properties like the relative sensitivities of the problem variables.

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Section: Optimisation Resultsmentioning

confidence: 99%

Section: Energymentioning

confidence: 99%

Section: Case Study: Cellular Windowsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mining Markov Network Surrogates for Value-Added Optimisation

Brownlee

2016

Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion

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“…The PR_GA is intrinsically an optimization method in which a preparation phase is conducted for seeding the initial population with viable solutions before running the optimization using the GA. It is a well-established technique for improving optimization efficiency of evolutionary algorithms [47] and showed competitive performance in BOPs [48,19,49,50]. Thus the PR_GA can be considered the first choice for BOPs.…”

Section: Discussionmentioning

confidence: 99%

A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems

Hamdy

Nguyen

Hensen

2016

Energy and Buildings

256

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• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement: ABSTRACTIntegrated building design is inherently a multi-objective optimization problem where two or more conflicting objectives must be minimized and/or maximized concurrently. Many multiobjective optimization algorithms have been developed; however few of them are tested in solving building design problems. This paper compares performance of seven commonly-used multi-objective evolutionary optimization algorithms in solving the design problem of a nearly zero energy building (nZEB) where more than 1.6 10 solutions would be possible. The compared algorithms include a controlled non-dominated sorting genetic algorithm with a passive archive (pNSGA-II), a multi-objective particle swarm optimization (MOPSO), a two-phase optimization using the genetic algorithm (PR_GA), an elitist non-dominated sorting evolution strategy (ENSES), a multi-objective evolutionary algorithm based on the concept of epsilon dominance (evMOGA), a multi-objective differential evolution algorithm (spMODE-II), and a multi-objective dragonfly algorithm (MODA). Several criteria was used to compare performance of these algorithms.In most cases, the quality of the obtained solutions was improved when the number of generations was increased. The optimization results of running each algorithm 20 times with gradually increasing number of evaluations indicated that the PR_GA algorithm had a high repeatability to explore a large area of the solution-space and achieved close-to-optimal solutions with a good diversity, followed by the pNSGA-II, evMOGA and spMODE-II. Uncompetitive results were achieved by the ENSES, MOPSO and MODA in most running cases. The study also found that 1400 -180...

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