Metaheuristic vs. deterministic global optimization algorithms: The univariate case
作者：
Highlights：
• Numerical methods for solving multiextremal optimization problems are considered.
• A limited computational budget is taken as one of the main comparison criterion.
• A quality certificate is requested for the solutions provided by the methods.
• Univariate constrained problems from literature and applied fields are used to test.
• Well-known nature-inspired metaheuristic and deterministic methods are compared on 134 constrained test problems by performing more than 125,000 launches of the methods.
摘要
•Numerical methods for solving multiextremal optimization problems are considered.•A limited computational budget is taken as one of the main comparison criterion.•A quality certificate is requested for the solutions provided by the methods.•Univariate constrained problems from literature and applied fields are used to test.•Well-known nature-inspired metaheuristic and deterministic methods are compared on 134 constrained test problems by performing more than 125,000 launches of the methods.
论文关键词：Constrained global optimization,Numerical comparison,Lipschitz-based deterministic approaches,Nature-inspired metaheuristics
论文评审过程：Received 5 January 2017, Revised 20 April 2017, Accepted 1 May 2017, Available online 22 May 2017, Version of Record 18 October 2017.
论文官网地址：https://doi.org/10.1016/j.amc.2017.05.014
