A hybrid genetic algorithm for constrained multi-objective optimization under uncertainty and target matching problems

This work presents a new approach for interval-based uncertainty analysis. The proposed approach integrates a local search strategy as the worst-case-scenario technique of anti-optimization with a constrained multi-objective genetic algorithm. Anti-optimization is a term for an approach to safety fa...

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Main Authors: Wang, N. F., Zhang, X. M., Yang, Y. W.
Other Authors: School of Civil and Environmental Engineering
Format: Article
Language:English
Published: 2013
Subjects:
Online Access:https://hdl.handle.net/10356/102654
http://hdl.handle.net/10220/16868
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1026542020-03-07T11:45:54Z A hybrid genetic algorithm for constrained multi-objective optimization under uncertainty and target matching problems Wang, N. F. Zhang, X. M. Yang, Y. W. School of Civil and Environmental Engineering DRNTU::Engineering::Civil engineering This work presents a new approach for interval-based uncertainty analysis. The proposed approach integrates a local search strategy as the worst-case-scenario technique of anti-optimization with a constrained multi-objective genetic algorithm. Anti-optimization is a term for an approach to safety factors in engineering structures which is described as pessimistic and searching for least favorable responses, in combination with optimization techniques but in contrast to probabilistic approaches. The algorithm is applied and evaluated to be efficient and effective in producing good results via target matching problems: a simulated topology and shape optimization problem where a ‘target’ geometry set is predefined as the Pareto optimal solution and a constrained multiobjective optimization problem formulated such that the design solutions will evolve and converge towards the target geometry set. 2013-10-25T01:41:06Z 2019-12-06T20:58:21Z 2013-10-25T01:41:06Z 2019-12-06T20:58:21Z 2013 2013 Journal Article Wang, N. F., Zhang, X. M., & Yang, Y. W. (2013). A hybrid genetic algorithm for constrained multi-objective optimization under uncertainty and target matching problems. Applied soft computing, 13(8), 3636-3645. 1568-4946 https://hdl.handle.net/10356/102654 http://hdl.handle.net/10220/16868 10.1016/j.asoc.2013.03.013 en Applied soft computing
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Civil engineering
spellingShingle DRNTU::Engineering::Civil engineering
Wang, N. F.
Zhang, X. M.
Yang, Y. W.
A hybrid genetic algorithm for constrained multi-objective optimization under uncertainty and target matching problems
description This work presents a new approach for interval-based uncertainty analysis. The proposed approach integrates a local search strategy as the worst-case-scenario technique of anti-optimization with a constrained multi-objective genetic algorithm. Anti-optimization is a term for an approach to safety factors in engineering structures which is described as pessimistic and searching for least favorable responses, in combination with optimization techniques but in contrast to probabilistic approaches. The algorithm is applied and evaluated to be efficient and effective in producing good results via target matching problems: a simulated topology and shape optimization problem where a ‘target’ geometry set is predefined as the Pareto optimal solution and a constrained multiobjective optimization problem formulated such that the design solutions will evolve and converge towards the target geometry set.
author2 School of Civil and Environmental Engineering
author_facet School of Civil and Environmental Engineering
Wang, N. F.
Zhang, X. M.
Yang, Y. W.
format Article
author Wang, N. F.
Zhang, X. M.
Yang, Y. W.
author_sort Wang, N. F.
title A hybrid genetic algorithm for constrained multi-objective optimization under uncertainty and target matching problems
title_short A hybrid genetic algorithm for constrained multi-objective optimization under uncertainty and target matching problems
title_full A hybrid genetic algorithm for constrained multi-objective optimization under uncertainty and target matching problems
title_fullStr A hybrid genetic algorithm for constrained multi-objective optimization under uncertainty and target matching problems
title_full_unstemmed A hybrid genetic algorithm for constrained multi-objective optimization under uncertainty and target matching problems
title_sort hybrid genetic algorithm for constrained multi-objective optimization under uncertainty and target matching problems
publishDate 2013
url https://hdl.handle.net/10356/102654
http://hdl.handle.net/10220/16868
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