Clustering Analysis of the Population in Db_SHADE Algorithm

Abstract

This paper provides an analysis of the population clustering in a novel Success-History based Adaptive Differential Evolution algorithm with Distance based adaptation (Db_SHADE) in order to analyze the exploration and exploitation abilities of the algorithm. The comparison with the original SHADE algorithm is performed on the CEC2015 benchmark set in two dimensional settings (10D and 30D). The clustering analysis helps to answer the question about prolonged exploration phase of the Db_SHADE algorithm. Possible future research directions are drawn in the discussion and conclusion.

Minimum-Volume Covering Ellipsoids: Improving the Efficiency of the Wolfe-Atwood Algorithm for Large-Scale Instances by Pooling and Batching

“…It has a strong connection to the optimal design in statistics [5]. Other application in statistics include outlier identification (see [6], [7], and [8]), and clustering (see [9], [10], and [11]). Containing ellipsoids play an important role in robust optimization [12] and control theory [13].…”