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...

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Autores principales:	Hang Yin, Chengna Xu, Fengyi Yao, Shipeng Chu, Yuan Huang
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	evolutionary algorithms global optima parameterization strategies water distribution systems Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/2121ec62d0af4776ba48e644b5895902
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spelling	oai:doaj.org-article:2121ec62d0af4776ba48e644b58959022021-11-05T13:46:12ZHow close simple EAs’ optimal solutions can approach global optima: experience from water distribution system design problems2709-80282709-803610.2166/aqua.2020.117https://doaj.org/article/2121ec62d0af4776ba48e644b58959022021-03-01T00:00:00Zhttp://aqua.iwaponline.com/content/70/2/171https://doaj.org/toc/2709-8028https://doaj.org/toc/2709-8036An 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.;Hang YinChengna XuFengyi YaoShipeng ChuYuan HuangIWA Publishingarticleevolutionary algorithmsglobal optimaparameterization strategieswater distribution systemsEnvironmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350ENAqua, Vol 70, Iss 2, Pp 171-183 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	evolutionary algorithms global optima parameterization strategies water distribution systems Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
spellingShingle	evolutionary algorithms global optima parameterization strategies water distribution systems Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 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
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.;
format	article
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
title	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_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_sort	how close simple eas’ optimal solutions can approach global optima: experience from water distribution system design problems
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/2121ec62d0af4776ba48e644b5895902
work_keys_str_mv	AT hangyin howclosesimpleeasoptimalsolutionscanapproachglobaloptimaexperiencefromwaterdistributionsystemdesignproblems AT chengnaxu howclosesimpleeasoptimalsolutionscanapproachglobaloptimaexperiencefromwaterdistributionsystemdesignproblems AT fengyiyao howclosesimpleeasoptimalsolutionscanapproachglobaloptimaexperiencefromwaterdistributionsystemdesignproblems AT shipengchu howclosesimpleeasoptimalsolutionscanapproachglobaloptimaexperiencefromwaterdistributionsystemdesignproblems AT yuanhuang howclosesimpleeasoptimalsolutionscanapproachglobaloptimaexperiencefromwaterdistributionsystemdesignproblems
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How close simple EAs’ optimal solutions can approach global optima: experience from water distribution system design problems

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