Improving the efficiency of multi-objective evolutionary algorithms through decomposition: An application to water distribution network design |
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Affiliation: | 1. Civil, Environment and Mining Engineering, University of Adelaide, Adelaide, Australia;2. Research Institute for Knowledge Systems, Maastricht, the Netherlands;3. Bushfire and Natural Hazards Cooperative Research Centre, Melbourne, Australia;1. University of California-Riverside, Center for Conservation Biology, 900 University Ave, Riverside, CA 92521, United States;2. University of California-Riverside, Department of Botany and Plant Sciences, 900 University Ave, Riverside, CA 92521, United States;3. Department of Civil and Environmental Engineering, Cullen College of Engineering, University of Houston, Houston, TX 77004, United States;4. Department of Planning and Development, Aalborg University Copenhagen, Copenhagen, Denmark;5. Department of GIS and Remote Sensing, Tarbiat Modares University, Tehran, Iran;6. Urban Development and Mobility Department, LISER, Luxembourg |
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Abstract: | Evolutionary algorithms (EAs) have been widely used in handling various water resource optimization problems in recent years. However, it is still challenging for EAs to identify near-optimal solutions for realistic problems within the available computational budgets. This paper introduces a novel multi-objective optimization method to improve the efficiency of a typically difficult water resource problem: water distribution network (WDN) design. In the proposed approach, a WDN is decomposed into different sub-networks using decomposition techniques. EAs optimize these sub-networks individually, generating Pareto fronts for each sub-network with great efficiency. A propagation method is proposed to evolve Pareto fronts of the sub-networks towards the Pareto front for the full network while eliminating the need to hydraulically simulate the intact network itself. Results from two complex realistic WDNs show that the proposed approach is able to find better fronts than conventional full-search algorithms (optimize the entire network without decomposition) with dramatically improved efficiency. |
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Keywords: | Graph decomposition Water distribution network Differential evolution algorithm Multi-objective optimization |
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