A new unconstrained global optimization method based on clustering and parabolic approximation
作者：
Highlights：
• Proposed method uses clustering and parabolic approximation.
• Clustering period provides jumping to better points using with populations.
• The vertex of parabola is much close to the local minima than obtained cluster center.
• We assume that the best cluster center denotes the position of global optimum.
• The proposed method is simple, faster and very successful to gain the global optimum.
摘要
•Proposed method uses clustering and parabolic approximation.•Clustering period provides jumping to better points using with populations.•The vertex of parabola is much close to the local minima than obtained cluster center.•We assume that the best cluster center denotes the position of global optimum.•The proposed method is simple, faster and very successful to gain the global optimum.
论文关键词：Unconstrained global optimization,Fuzzy c-means,Clustering,Least squares estimation,Parabolic approximation
论文评审过程：Received 4 April 2015, Revised 16 February 2016, Accepted 17 February 2016, Available online 3 March 2016, Version of Record 14 March 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.02.036
