A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems
•Integrated building design is inherently a multi-objective optimization problem.•Recently, many multi-objective optimization algorithms have been developed.•The performance of seven multi-objective evolutionary are tested in solving a nZEB design case.•The performance of studied algorithms is ranke...
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| Vydáno v: | Energy and buildings Ročník 121; s. 57 - 71 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.06.2016
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| Témata: | |
| ISSN: | 0378-7788 |
| On-line přístup: | Získat plný text |
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| Abstract | •Integrated building design is inherently a multi-objective optimization problem.•Recently, many multi-objective optimization algorithms have been developed.•The performance of seven multi-objective evolutionary are tested in solving a nZEB design case.•The performance of studied algorithms is ranked using six comparison criteria (two are novel).•1400–1800 were minimum required number of evaluations to stabilize optimization results.
Integrated building design is inherently a multi-objective optimization problem where two or more conflicting objectives must be minimized and/or maximized concurrently. Many multi-objective 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 1.610 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 1400–1800 were minimum required number of evaluations to stabilize optimization results of the building energy model. |
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| AbstractList | Integrated building design is inherently a multi-objective optimization problem where two or more conflicting objectives must be minimized and/or maximized concurrently. Many multi-objective 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 super(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 1400-1800 were minimum required number of evaluations to stabilize optimization results of the building energy model. •Integrated building design is inherently a multi-objective optimization problem.•Recently, many multi-objective optimization algorithms have been developed.•The performance of seven multi-objective evolutionary are tested in solving a nZEB design case.•The performance of studied algorithms is ranked using six comparison criteria (two are novel).•1400–1800 were minimum required number of evaluations to stabilize optimization results. Integrated building design is inherently a multi-objective optimization problem where two or more conflicting objectives must be minimized and/or maximized concurrently. Many multi-objective 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 1.610 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 1400–1800 were minimum required number of evaluations to stabilize optimization results of the building energy model. |
| Author | Hensen, Jan L.M. Nguyen, Anh-Tuan Hamdy, Mohamed |
| Author_xml | – sequence: 1 givenname: Mohamed surname: Hamdy fullname: Hamdy, Mohamed email: m.h.hassan.mohamed@tue.nl, DSc.M.Hamdy@gmail.com organization: Department of the Built Environment, Building Physics and Services, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands – sequence: 2 givenname: Anh-Tuan surname: Nguyen fullname: Nguyen, Anh-Tuan organization: Faculty of Architecture, The University of Danang—University of Science and Technology, 54 Nguyen Luong Bang, Danang, Viet Nam – sequence: 3 givenname: Jan L.M. surname: Hensen fullname: Hensen, Jan L.M. organization: Department of the Built Environment, Building Physics and Services, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands |
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| PublicationDate | 2016-06-01 2016-06-00 20160601 |
| PublicationDateYYYYMMDD | 2016-06-01 |
| PublicationDate_xml | – month: 06 year: 2016 text: 2016-06-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationTitle | Energy and buildings |
| PublicationYear | 2016 |
| Publisher | Elsevier B.V |
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| SubjectTerms | Algorithms Building simulation Comparison Design of buildings Evolutionary algorithms Experimentation Genetic algorithms Mathematical models Multi-objective optimization Odonata Optimization Running Sorting |
| Title | A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems |
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