Multiobjective evolutionary optimization of water distribution systems: Exploiting diversity with infeasible solutions.
This article investigates the computational efficiency of constraint handling in multi-objective evolutionary optimization algorithms for water distribution systems. The methodology investigated here encourages the co-existence and simultaneous development including crossbreeding of subpopulations o...
| Published in: | Journal of Environmental Management Vol. 183; pp. 133 - 142 |
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| Main Authors: | , |
| Format: | Article |
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Academic Press Inc.
Dec2016 Part 1
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=118342686&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 118342686 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 03014797 EMJ jtl: Journal of Environmental Management issn: 03014797 maglogo: N pubinfo: dt: Dec2016 Part 1 vid: 183 pid: 735 pub: Academic Press Inc. artinfo: ui: 118342686 10.1016/j.jenvman.2016.08.048 ppf: 133 ppct: 9 formats: tig: atl: Multiobjective evolutionary optimization of water distribution systems: Exploiting diversity with infeasible solutions. aug: au: Tanyimboh, Tiku T. Seyoum, Alemtsehay G. affil: Department of Civil and Environmental Engineering, University of Strathclyde, James Weir Building, 75 Montrose Street, Glasgow G1 1XJ, UK su: Decision making Mathematical optimization Water distribution Crossbreeding Plant gene banks sug: subj: Decision making Water Supply and Irrigation Systems Water and Sewer Line and Related Structures Construction Mathematical optimization Water distribution Crossbreeding Plant gene banks keyword: Constraint handling Dynamic simulation Infrastructure planning Maximum solution vector Minimum solution vector Water supply Constraint handling Dynamic simulation Infrastructure planning Maximum solution vector Minimum solution vector Water supply ab: This article investigates the computational efficiency of constraint handling in multi-objective evolutionary optimization algorithms for water distribution systems. The methodology investigated here encourages the co-existence and simultaneous development including crossbreeding of subpopulations of cost-effective feasible and infeasible solutions based on Pareto dominance. This yields a boundary search approach that also promotes diversity in the gene pool throughout the progress of the optimization by exploiting the full spectrum of non-dominated infeasible solutions. The relative effectiveness of small and moderate population sizes with respect to the number of decision variables is investigated also. The results reveal the optimization algorithm to be efficient, stable and robust. It found optimal and near-optimal solutions reliably and efficiently. The real-world system based optimization problem involved multiple variable head supply nodes, 29 fire-fighting flows, extended period simulation and multiple demand categories including water loss. The least cost solutions found satisfied the flow and pressure requirements consistently. The best solutions achieved indicative savings of 48.1% and 48.2% based on the cost of the pipes in the existing network, for populations of 200 and 1000, respectively. The population of 1000 achieved slightly better results overall. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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