Comparing state-of-the-art evolutionary multi-objective algorithms for long-term groundwater monitoring design

This study compares the performances of four state-of-the-art evolutionary multi-objective optimization (EMO) algorithms: the Non-Dominated Sorted Genetic Algorithm II (NSGAII), the Epsilon-Dominance Non-Dominated Sorted Genetic Algorithm II ( ε-NSGAII), the Epsilon-Dominance Multi-Objective Evoluti...

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Veröffentlicht in:Advances in water resources Jg. 29; H. 6; S. 792 - 807
Hauptverfasser: Kollat, J.B., Reed, P.M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Elsevier Ltd 01.06.2006
Elsevier Science
Schlagworte:
algorithms
computer software
Earth sciences
Earth, ocean, space
environmental monitoring
epsilon-dominance multi-objective evolutionary algorithm
epsilon-dominance non-dominated sorted genetic algorithm II
Evolutionary algorithms
Exact sciences and technology
groundwater contamination
Hydrogeology
hydrologic models
Hydrology. Hydrogeology
Long-term groundwater monitoring
Multi-objective optimization
non-dominated sorted genetic algorithm II
Performance metrics
strength Pareto evolutionary algorithm 2
Performance metrics
Multi-objective optimization
Long-term groundwater monitoring
Evolutionary algorithms
algorithms
efficiency
testing
ground water
concentration
indicators
pollution
water resources
aquifers
optimization
water resource management
errors
ISSN:0309-1708, 1872-9657
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Zusammenfassung:This study compares the performances of four state-of-the-art evolutionary multi-objective optimization (EMO) algorithms: the Non-Dominated Sorted Genetic Algorithm II (NSGAII), the Epsilon-Dominance Non-Dominated Sorted Genetic Algorithm II ( ε-NSGAII), the Epsilon-Dominance Multi-Objective Evolutionary Algorithm ( εMOEA), and the Strength Pareto Evolutionary Algorithm 2 (SPEA2), on a four-objective long-term groundwater monitoring (LTM) design test case. The LTM test case objectives include: (i) minimize sampling cost, (ii) minimize contaminant concentration estimation error, (iii) minimize contaminant concentration estimation uncertainty, and (iv) minimize contaminant mass estimation error. The 25-well LTM design problem was enumerated to provide the true Pareto-optimal solution set to facilitate rigorous testing of the EMO algorithms. The performances of the four algorithms are assessed and compared using three runtime performance metrics (convergence, diversity, and ε-performance), two unary metrics (the hypervolume indicator and unary ε-indicator) and the first-order empirical attainment function. Results of the analyses indicate that the ε-NSGAII greatly exceeds the performance of the NSGAII and the εMOEA. The ε-NSGAII also achieves superior performance relative to the SPEA2 in terms of search effectiveness and efficiency. In addition, the ε-NSGAII’s simplified parameterization and its ability to adaptively size its population and automatically terminate results in an algorithm which is efficient, reliable, and easy-to-use for water resources applications.
Bibliographie:http://dx.doi.org/10.1016/j.advwatres.2005.07.010
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ISSN:0309-1708
1872-9657
DOI:10.1016/j.advwatres.2005.07.010