Assessing the Uses of NLP-based Surrogate Models for Solving Expensive Multi-Objective Optimization Problems: Application to Potable Water Chains

DOI

10.2991/ict4s-env-15.2015.2How to use a DOI?

Keywords

drinking water production plant, environmental impact, expensive multi-objective optimization, life cycle assessment, meta-heuristic algorithms, surrogate model

Abstract

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.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Florin Capitanescu
AU  - Antonino Marvuglia
AU  - Enrico Benetto
AU  - Aras Ahmadi
AU  - Ligia Tiruta-Barna
PY  - 2015/09
DA  - 2015/09
TI  - Assessing the Uses of NLP-based Surrogate Models for Solving Expensive Multi-Objective Optimization Problems: Application to Potable Water Chains
BT  - Proceedings of EnviroInfo and ICT for Sustainability 2015
PB  - Atlantis Press
SP  - 10
EP  - 18
SN  - 2352-538X
UR  - https://doi.org/10.2991/ict4s-env-15.2015.2
DO  - 10.2991/ict4s-env-15.2015.2
ID  - Capitanescu2015/09
ER  -