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ICT-ENSURE Information System on Literature in the Field of ICT for Environmental Sustainability ICT-ENSURE Information System on Literature in the Field of ICT for Environmental Sustainability European Commission European Commission CORDIS Seventh Framework Programme KIT - Karlsruhe Institute of Technology Graz University of Technology, Knowledge Management Institute International Society for Environmental Protection
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Assessing the Uses of NLP-based Surrogate Models for Solving Expensive Multi-Objective Optimization Problems: Application to Potable Water Chains

Authors
Capitanescu, Florin (Luxembourg Institute of Science and Technology)
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Marvuglia, Antonino (Luxembourg Institute of Science and Technology)
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Benetto, Enrico (Luxembourg Institute of Science and Technology)
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Ahmadi, Aras (University of Toulouse)
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Tiruta-Barna, Ligia (University of Toulouse)
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Chapter ConverStation I
Volume EnviroInfo & ICT4S, Conference Proceedings (Part 1)
Conference Building the knowledge base for environmental action and sustainability
Copenhagen, 2015
Year 2015
Abstract of the Article
In practice many multi-objective optimization problems relying on computationally expensive black-box model simulators of industrial processes have to be solved with limited computing time budget. In this context, this paper proposes and explores the uses of an iterative heuristic approach aiming at quickly providing a satisfactory accurate approximation of the Pareto front. The approach builds, in each iteration, a multi-objective nonlinear programming (MO-NLP) surrogate problem model using curve fitting of objectives and constraints. The approximated solutions of the Pareto front are generated by applying the "-constraint method to the multi-objective surrogate problem, converting it into a desired number of single objective (SO) NLP problems, for which mature and computationally efficient solvers exist. The proposed approach is applied to the cost versus life cycle assessment (LCA)-based environmental optimization of drinking water treatment chains. The paper thoroughly investigates various settings choices of the approach such as: the type of the polynomial function to be fit, the input points, choice of weights in curve fitting, and analytical fit. The numerical simulations results with the approach show that a good quality approximation of Pareto front can be obtained with a significantly smaller computational time than with the popular SPEA2 state-of-the-art metaheuristic algorithm.
Pages 10 - 18
Additional Information 10.2991/ict4s-env-15.2015.2
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