Authors:
Vladimir Stanovov
1
;
Shakhnaz Akhmedova
2
;
Eugene Semenkin
3
and
Mariia Semenkina
4
Affiliations:
1
Reshetnev Siberian State University, Krasnoyarskii rabochii ave. 31, 660037, Krasnoyarsk, Russian Federation, Siberian Federal University, Institute of Mathematics and Computer Science, 79 Svobodny pr., 660041, Krasnoyarsk and Russian Federation
;
2
Reshetnev Siberian State University, Krasnoyarskii rabochii ave. 31, 660037, Krasnoyarsk and Russian Federation
;
3
Siberian Federal University, Institute of Mathematics and Computer Science, 79 Svobodny pr., 660041, Krasnoyarsk and Russian Federation
;
4
Heuristic and Evolutionary Algorithms Laboratory (HEAL), University of Applied Sciences Upper Austria and Softwarepark
Keyword(s):
Differential Evolution, Optimization, Parameter Control, Metaheuristic.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Memetic Algorithms
;
Soft Computing
Abstract:
The Differential Evolution (DE) is a highly competitive numerical optimization algorithm, with a small number of control parameters. However, it is highly sensitive to the setting of these parameters, which inspired many researchers to develop adaptation strategies. One of them is the popular Success-History based Adaptation (SHA) mechanism, which significantly improves the DE performance. In this study, the focus is on the choice of the metaparameters of the SHA, namely the settings of the Lehmer mean coefficients for scaling factor and crossover rate memory cells update. The experiments are performed on the LSHADE algorithm and the Congress on Evolutionary Computation competition on numerical optimization functions set. The results demonstrate that for larger dimensions the SHA mechanism with modified Lehmer mean allows a significant improvement of the algorithm efficiency. The theoretical considerations of the generalized Lehmer mean could be also applied to other adaptive mechani
sms.
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