new epsilon-dominance hierarchical Bayesian optimization algorithm for large multiobjective monitoring network design problems

This study focuses on the development of a next generation multiobjective evolutionary algorithm (MOEA) that can learn and exploit complex interdependencies and/or correlations between decision variables in monitoring design applications to provide more robust performance for large problems (defined...

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Published inAdvances in water resources Vol. 31; no. 5; pp. 828 - 845
Main Authors Kollat, J.B, Reed, P.M, Kasprzyk, J.R
Format Journal Article
LanguageEnglish
Published Oxford Elsevier Science 01.05.2008
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Online AccessGet full text
ISSN0309-1708
DOI10.1016/j.advwatres.2008.01.017

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Abstract This study focuses on the development of a next generation multiobjective evolutionary algorithm (MOEA) that can learn and exploit complex interdependencies and/or correlations between decision variables in monitoring design applications to provide more robust performance for large problems (defined in terms of both the number of objectives and decision variables). The proposed MOEA is termed the epsilon-dominance hierarchical Bayesian optimization algorithm ( epsilon -hBOA), which is representative of a new class of probabilistic model building evolutionary algorithms. The epsilon -hBOA has been tested relative to a top-performing traditional MOEA, the epsilon-dominance nondominated sorted genetic algorithm II ( epsilon -NSGAII) for solving a four-objective LTM design problem. A comprehensive performance assessment of the epsilon -NSGAII and various configurations of the epsilon -hBOA have been performed for both a 25 well LTM design test case (representing a relatively small problem with over 33million possible designs), and a 58 point LTM design test case (with over 2.88x10 super(1) super(7) possible designs). The results from this comparison indicate that the model building capability of the epsilon -hBOA greatly enhances its performance relative to the epsilon -NSGAII, especially for large monitoring design problems. This work also indicates that decision variable interdependencies appear to have a significant impact on the overall mathematical difficulty of the monitoring network design problem.
AbstractList This study focuses on the development of a next generation multiobjective evolutionary algorithm (MOEA) that can learn and exploit complex interdependencies and/or correlations between decision variables in monitoring design applications to provide more robust performance for large problems (defined in terms of both the number of objectives and decision variables). The proposed MOEA is termed the epsilon-dominance hierarchical Bayesian optimization algorithm ( epsilon -hBOA), which is representative of a new class of probabilistic model building evolutionary algorithms. The epsilon -hBOA has been tested relative to a top-performing traditional MOEA, the epsilon-dominance nondominated sorted genetic algorithm II ( epsilon -NSGAII) for solving a four-objective LTM design problem. A comprehensive performance assessment of the epsilon -NSGAII and various configurations of the epsilon -hBOA have been performed for both a 25 well LTM design test case (representing a relatively small problem with over 33million possible designs), and a 58 point LTM design test case (with over 2.88x10 super(1) super(7) possible designs). The results from this comparison indicate that the model building capability of the epsilon -hBOA greatly enhances its performance relative to the epsilon -NSGAII, especially for large monitoring design problems. This work also indicates that decision variable interdependencies appear to have a significant impact on the overall mathematical difficulty of the monitoring network design problem.
Author Kasprzyk, J.R
Reed, P.M
Kollat, J.B
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models
plumes
ground water
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Evolutionary algorithms
pollution
networks
tetrachloroethylene
underground storage
water resources
Long-term groundwater monitoring
aquifers
correlation
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Snippet This study focuses on the development of a next generation multiobjective evolutionary algorithm (MOEA) that can learn and exploit complex interdependencies...
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SubjectTerms algorithms
Bayesian networks
Bayesian theory
Earth sciences
Earth, ocean, space
Engineering and environment geology. Geothermics
environmental monitoring
epsilon-dominance hierarchical Bayesian optimization algorithm
evolutionary algorithms
Exact sciences and technology
groundwater
groundwater contamination
Hydrogeology
Hydrology. Hydrogeology
mathematics and statistics
multiobjective monitoring
Pollution, environment geology
probabilistic models
wells
Title new epsilon-dominance hierarchical Bayesian optimization algorithm for large multiobjective monitoring network design problems
URI https://www.proquest.com/docview/20693826
https://www.proquest.com/docview/46542938
Volume 31
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