Efficient large scale global optimization through clustering-based population methods
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Publication:2027033
DOI10.1016/j.cor.2020.105165OpenAlexW3110979014MaRDI QIDQ2027033
Publication date: 21 May 2021
Published in: Computers \& Operations Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.cor.2020.105165
global optimizationdifferential evolutionclustering methodsrandom projectionsmemetic algorithmsmulti-level single-linkage
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- Differential evolution methods based on local searches
- Computational investigation of simple memetic approaches for continuous global optimization
- Extensions of Lipschitz maps into Banach spaces
- Differential evolution -- a simple and efficient heuristic for global optimization over continuous spaces
- Database-friendly random projections: Johnson-Lindenstrauss with binary coins.
- Stochastic global optimization methods part I: Clustering methods
- Stochastic global optimization methods part II: Multi level methods
- Cluster Analysis Using Seed Points and Density-Determined Hyperspheres as an Aid to Global Optimization
- Benchmarking Derivative-Free Optimization Algorithms
- Clustering methods for large scale geometrical global optimization
- SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization
- Global optimization
- Benchmarking optimization software with performance profiles.