Paper Info
Title
Distance based parameter adaptation for Success-History based Differential Evolution
Abstract
This paper proposes a simple, yet effective, modification to scaling factor and crossover rate adaptation in Success-History based Adaptive Differential Evolution (SHADE), which can be used as a framework to all SHADE-based algorithms. The performance impact of the proposed method is shown on the real-parameter single objective optimization (CEC2015 and CEC2017) benchmark sets in 10, 30, 50, and 100 dimensions for all SHADE, L-SHADE (SHADE with linear decrease of population size), and jSO algorithms. The proposed distance based parameter adaptation is designed to address the premature convergence of SHADE–based algorithms in higher dimensional search spaces to maintain a longer exploration phase. This design effectiveness is supported by presenting a population clustering analysis, along with a population diversity measure. Also, the new distance based algorithm versions (Db_SHADE, DbL_SHADE, and DISH) have obtained significantly better optimization results than their canonical counterparts (SHADE, L_SHADE, and jSO) in 30, 50, and 100 dimensional functions.
Year
DOI
Venue
2019
10.1016/j.swevo.2018.10.013
Swarm and Evolutionary Computation
Keywords
Field
DocType
Differential Evolution,Distance based,Parameter adaptation,Success-History,Scaling factor,Crossover rate
Scale factor,Population,Premature convergence,Population diversity,Differential evolution,Population size,Statistics,Cluster analysis,Single objective,Mathematics
Journal
Volume
ISSN
Citations 
50
2210-6502
6
PageRank 
References 
Authors
0.42
26
6
Name
Order
Citations
PageRank
Adam Viktorin12916.76
Roman Senkerik237574.92
Roman Senkerik337574.92
Michal Pluhacek421747.34
Tomas Kadavy52020.97
Ales Zamuda640018.26