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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Bibliographic Details
Published in:	Advances in water resources Vol. 29; no. 6; pp. 792 - 807
Main Authors:	Kollat, J.B., Reed, P.M.
Format:	Journal Article
Language:	English
Published:	Oxford Elsevier Ltd 01.06.2006 Elsevier Science
Subjects:	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
Online Access:	Get full text
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