A study on algorithms for optimization of Latin hypercubes

By Mattias Liefvendahl, R. Stocki. 2006

246 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3cc8c2b548aa834bcfe4125043362218f21f4559

How researchers see this paper

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

In Computational Theory and Mathematics

1. Jianfei Cai
2. Haitao Liu
3. Yew-Soon Ong
4. Felipe Antonio Chegury Viana
5. Gerhard Venter
6. Vladimir Balabanov
7. Markus Kraft
8. Iftekhar A. Karimi

In Management Science and Operations Research

1. Yew-Soon Ong
2. Jianfei Cai
3. Haitao Liu
4. Gerhard Venter
5. Vladimir Balabanov
6. Markus Kraft
7. Iftekhar A. Karimi
8. Michael D. Shields

In Statistics and Probability

1. Yew-Soon Ong
2. Jianfei Cai
3. Haitao Liu
4. Gerhard Venter
5. Vladimir Balabanov
6. Markus Kraft
7. Iftekhar A. Karimi
8. Michael D. Shields

Citations in the corpus

• Design of computer experiments: A review (2017, background): “[83] ESE X X X Liefvendahl and Stocki [105] CP X X van Dam et al.”
• The generalization of Latin hypercube sampling (2015, background): “The widespread popularity of LHS has led to the invention of numerous variants meant to improve space-filling [14–20], optimize projective properties [21], minimize least square error and maximize entropy [22], and reduce spurious correlations [23– 26,16,18,27].”
• Probabilistic Load Flow Evaluation With Hybrid Latin Hypercube Sampling and Cholesky Decomposition (2009, methodology): “Different permutation methods have been proposed and adopted in LHS to ensure the correlations between the rows of matrix to be a minimum, for example, correlation reduction with genetic algorithm [21], columnwise-pairwise algorithm [22], single-switch optimization [23], Cholesky decomposition [24],”
• An algorithm for fast optimal Latin hypercube design of experiments (2010, methodology): “[25] 2005 Enhanced stochastic evolutionary algorithm p criterion, entropy and L2 discrepancy Liefvendahl and Stocki [26] 2006 Columnwise-pairwise and genetic algorithms Minimum distance and Audze–Eglājs functions van Dam et al.”
• A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017, methodology): “Hence, different time-consuming optimization strategies, e.g., columnwise-pairwise (Stocki 2005), genetic algorithm (Liefvendahl and Stocki 2006), simulated annealing (Pholdee and Bureerat 2015), enhanced stochastic evolutionary algo-rithm (Jin et al. 2005), and iterated local search heuristics…”