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Cited by 4
On Benchmarking Stochastic Global Optimization Algorithms

Discrete Competitive Facility Location by Ranking Candidate Locations
Algirdas Lančinskas, Pascual Fernández, Blas Pelegrín, Julius Žilinskas
https://doi.org/10.1007/978-3-030-39250-5_8
Book  Studies in Computational Intelligence (Data Science: New Issues, Challenges and Applications) Volume 869 (2020), p. 145
OPTIMISING THE SMOOTHNESS AND ACCURACY OF MOVING AVERAGE FOR STOCK PRICE DATA
Aistis RAUDYS, Židrina PABARŠKAITĖ
https://doi.org/10.3846/20294913.2016.1216906
Journal  Technological and Economic Development of Economy Volume 24, Issue 3 (2018), p. 984
Solution of asymmetric discrete competitive facility location problems using ranking of candidate locations
Algirdas Lančinskas, Julius Žilinskas, Pascual Fernández, Blas Pelegrín
https://doi.org/10.1007/s00500-020-05106-0
Journal  Soft Computing Volume 24, Issue 23 (2020), p. 17705
The Fast and the Robust: Trade‐Offs Between Optimization Robustness and Cost in the Calibration of Environmental Models
Dmitri Kavetski, Youwei Qin, George Kuczera
https://doi.org/10.1029/2017WR022051
Journal  Water Resources Research Volume 54, Issue 11 (2018), p. 9432


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