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

By Joshua B. Kollat, Patrick M. Reed. 2005

353 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Computational Theory and Mathematics, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/3d9dea7638b86f0820f56d410beef0a0c2c19891

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. David Hadka
2. Patrick M. Reed
3. Christian von Lücken
4. Benjamín Barán
5. Carlos A. Brizuela
6. K. V. Werkhoven
7. Yong Tang
8. Thorsten Wagener

In Computational Theory and Mathematics

1. David Hadka
2. Patrick M. Reed
3. Xin Yao
4. Joshua B. Kollat
5. Christian von Lücken
6. Benjamín Barán
7. Carlos A. Brizuela
8. Joseph R. Kasprzyk

In Ocean Engineering

1. Jasper A. Vrugt
2. Patrick M. Reed
3. David Hadka
4. Thorsten Wagener
5. Joshua B. Kollat
6. Christian von Lücken
7. Benjamín Barán
8. Carlos A. Brizuela

Citations in the corpus

• Sensitivity-guided reduction of parametric dimensionality for multi-objective calibration of watershed models (2009, methodology): “Epsilon nondominated sorted genetic algorithm II (e-NSGAII) The e-NSGAII is a multi-objective evolutionary algorithm (MOEA) that has been applied and tested for a variety of system optimization problems [20,21,31,32].”
• A survey on multi-objective evolutionary algorithms for many-objective problems (2014, methodology): “…instances of 18 unconstrained real valued test problems to compare the NSGA-II [24], the SPEA2 [110], the (cid:4) -MOEA [67], the (cid:4) -NSGA-II [59], a version of the MOEA/D [105], the IBEA [106], the differential evolution method introduced in [65] (GDE3), the Multi-Objective Particle Swarm…”
• Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions (2014, background): “…have demonstrated that for certain subsets of problems, a particular implementation of one algorithm can be found to outperform some others (Kollat and Reed, 2006; Reed et al., 2013) (motivating recent efforts to create autoadaptive multi-algorithms (Hadka and Reed, 2013; Vrugt and…”
• Borg: An Auto-Adaptive Many-Objective Evolutionary Computing Framework (2013, methodology): “(cid:2) - MOEA (Deb, Thiele, et al., 2002) and (cid:2) -NSGA-II (Kollat and Reed, 2006) are two popular algorithms using (cid:2) -dominance included in this study.”
• Many-Objective Evolutionary Algorithms (2015, background): “Laumanns et al. [2002] proposed the concept of -dominance.”