How close simple EAs’ optimal solutions can approach global optima: experience from water distribution system design problems
An issue regarding near-optimal solutions identified by evolutionary algorithms (EAs) is that their absolute deviations from the global optima are often unknown, and hence an EA's performance in handling real-world problems remains unclear. To this end, this paper investigates how close optimal...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2021-03-01
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| Series: | Aqua |
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| Online Access: | http://aqua.iwaponline.com/content/70/2/171 |
| _version_ | 1830207734473031680 |
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| author | Hang Yin Chengna Xu Fengyi Yao Shipeng Chu Yuan Huang |
| author_facet | Hang Yin Chengna Xu Fengyi Yao Shipeng Chu Yuan Huang |
| author_sort | Hang Yin |
| collection | DOAJ |
| description | An issue regarding near-optimal solutions identified by evolutionary algorithms (EAs) is that their absolute deviations from the global optima are often unknown, and hence an EA's performance in handling real-world problems remains unclear. To this end, this paper investigates how close optimal solutions from simple EAs can approach the global optimal for water distribution system (WDS) design problems through an experiment with the number of decision variables ranging from 21 to 3,400. Three simple EAs are considered: the standard differential evolution, the standard genetic algorithm and the creeping genetic algorithm (CGA). The CGA consistently identifies optimal solutions with deviations lower than 50% to the global optimal, even for the WDS with 3,400 decision variables, but the performance of the other two EAs is heavily case study dependent. Results obtained build knowledge regarding these simple EAs’ ability in handling WDS design problems with different sizes. We must acknowledge that these results are conditioned on the WDSs and the parameterization strategies used, and future studies should focus on generalizing the findings obtained in this paper. HIGHLIGHTS
This paper explores EA's ability in finding global optima.;
This paper gives guidelines for the selection of EAs based on the number of decision variables.; |
| first_indexed | 2024-12-18T04:37:19Z |
| format | Article |
| id | doaj.art-2121ec62d0af4776ba48e644b5895902 |
| institution | Directory Open Access Journal |
| issn | 2709-8028 2709-8036 |
| language | English |
| last_indexed | 2024-12-18T04:37:19Z |
| publishDate | 2021-03-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Aqua |
| spelling | doaj.art-2121ec62d0af4776ba48e644b58959022022-12-21T21:20:50ZengIWA PublishingAqua2709-80282709-80362021-03-0170217118310.2166/aqua.2020.117117How close simple EAs’ optimal solutions can approach global optima: experience from water distribution system design problemsHang Yin0Chengna Xu1Fengyi Yao2Shipeng Chu3Yuan Huang4 College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China An issue regarding near-optimal solutions identified by evolutionary algorithms (EAs) is that their absolute deviations from the global optima are often unknown, and hence an EA's performance in handling real-world problems remains unclear. To this end, this paper investigates how close optimal solutions from simple EAs can approach the global optimal for water distribution system (WDS) design problems through an experiment with the number of decision variables ranging from 21 to 3,400. Three simple EAs are considered: the standard differential evolution, the standard genetic algorithm and the creeping genetic algorithm (CGA). The CGA consistently identifies optimal solutions with deviations lower than 50% to the global optimal, even for the WDS with 3,400 decision variables, but the performance of the other two EAs is heavily case study dependent. Results obtained build knowledge regarding these simple EAs’ ability in handling WDS design problems with different sizes. We must acknowledge that these results are conditioned on the WDSs and the parameterization strategies used, and future studies should focus on generalizing the findings obtained in this paper. HIGHLIGHTS This paper explores EA's ability in finding global optima.; This paper gives guidelines for the selection of EAs based on the number of decision variables.;http://aqua.iwaponline.com/content/70/2/171evolutionary algorithmsglobal optimaparameterization strategieswater distribution systems |
| spellingShingle | Hang Yin Chengna Xu Fengyi Yao Shipeng Chu Yuan Huang How close simple EAs’ optimal solutions can approach global optima: experience from water distribution system design problems Aqua evolutionary algorithms global optima parameterization strategies water distribution systems |
| title | How close simple EAs’ optimal solutions can approach global optima: experience from water distribution system design problems |
| title_full | How close simple EAs’ optimal solutions can approach global optima: experience from water distribution system design problems |
| title_fullStr | How close simple EAs’ optimal solutions can approach global optima: experience from water distribution system design problems |
| title_full_unstemmed | How close simple EAs’ optimal solutions can approach global optima: experience from water distribution system design problems |
| title_short | How close simple EAs’ optimal solutions can approach global optima: experience from water distribution system design problems |
| title_sort | how close simple eas optimal solutions can approach global optima experience from water distribution system design problems |
| topic | evolutionary algorithms global optima parameterization strategies water distribution systems |
| url | http://aqua.iwaponline.com/content/70/2/171 |
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