Selective Multistart Optimization Based on Adaptive Latin Hypercube Sampling and Interval Enclosures
Gorde:
| Argitaratua izan da: | Mathematics vol. 13, no. 11 (2025), p. 1733 |
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MDPI AG
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| Sarrera elektronikoa: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 024 | 7 | |a 10.3390/math13111733 |2 doi | |
| 035 | |a 3217737877 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231533 |2 nlm | ||
| 100 | 1 | |a Nikas, Ioannis A |u Department of Tourism Management, University of Patras, GR 26334 Patras, Greece | |
| 245 | 1 | |a Selective Multistart Optimization Based on Adaptive Latin Hypercube Sampling and Interval Enclosures | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Solving global optimization problems is a significant challenge, particularly in high-dimensional spaces. This paper proposes a selective multistart optimization framework that employs a modified Latin Hypercube Sampling (LHS) technique to maintain a constant search space coverage rate, alongside Interval Arithmetic (IA) to prioritize sampling points. The proposed methodology addresses key limitations of conventional multistart methods, such as the exponential decline in space coverage with increasing dimensionality. It prioritizes sampling points by leveraging the hypercubes generated through LHS and their corresponding interval enclosures, guiding the optimization process toward regions more likely to contain the global minimum. Unlike conventional multistart methods, which assume uniform sampling without quantifying spatial coverage, the proposed approach constructs interval enclosures around each sample point, enabling explicit estimation and control of the explored search space. Numerical experiments on well-known benchmark functions demonstrate improvements in space coverage efficiency and enhanced local/global minimum identification. The proposed framework offers a promising approach for large-scale optimization problems frequently encountered in machine learning, artificial intelligence, and data-intensive domains. | |
| 653 | |a Big Data | ||
| 653 | |a Machine learning | ||
| 653 | |a Deep learning | ||
| 653 | |a Interval arithmetic | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Adaptive sampling | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Global optimization | ||
| 653 | |a Hypercubes | ||
| 653 | |a Methods | ||
| 653 | |a Algorithms | ||
| 653 | |a Enclosures | ||
| 653 | |a Efficiency | ||
| 653 | |a Latin hypercube sampling | ||
| 700 | 1 | |a Georgopoulos, Vasileios P |u Department of Physics, University of Patras, GR 26504 Rion, Greece; vasileios.georgopoulos@upnet.gr (V.P.G.); vxloukop@upatras.gr (V.C.L.) | |
| 700 | 1 | |a Loukopoulos, Vasileios C |u Department of Physics, University of Patras, GR 26504 Rion, Greece; vasileios.georgopoulos@upnet.gr (V.P.G.); vxloukop@upatras.gr (V.C.L.) | |
| 773 | 0 | |t Mathematics |g vol. 13, no. 11 (2025), p. 1733 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3217737877/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3217737877/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3217737877/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |