Convergence conditions and numerical comparison of global optimization methods based on dimensionality reduction schemes
作者：
Highlights：
• Multiextremal multidimensional optimization problems are considered.
• Numerical methods of global optimization are the subject of study.
• Schemes of dimensionality reduction based on nested optimization and Peano-type space-filling curves are applied in the research.
• Global convergence of numerical methods combining the dimensionality reduction schemes and characteristical algorithms is theoretically substantiated.
• Numerical comparison of the methods investigated is carried out on complicated problem sets belonging to the well-known test class GKLS.
摘要
•Multiextremal multidimensional optimization problems are considered.•Numerical methods of global optimization are the subject of study.•Schemes of dimensionality reduction based on nested optimization and Peano-type space-filling curves are applied in the research.•Global convergence of numerical methods combining the dimensionality reduction schemes and characteristical algorithms is theoretically substantiated.•Numerical comparison of the methods investigated is carried out on complicated problem sets belonging to the well-known test class GKLS.
论文关键词：Multiextremal functions,Global optimization,Numerical methods,Dimensionality reduction,Convergence,Comparison of efficiency
论文评审过程：Received 1 February 2017, Revised 21 May 2017, Accepted 27 June 2017, Available online 19 July 2017, Version of Record 18 October 2017.
论文官网地址：https://doi.org/10.1016/j.amc.2017.06.036
