Тип публикации: статья из журнала
Год издания: 2021
Идентификатор DOI: 10.1016/j.ins.2021.03.016
Ключевые слова: differential evolution, Expected fitness improvement, Linear bias reduction, optimization, parameter adaptation, success-history adaptation
Аннотация: The paper describes the problem of bias in parameter adaptation in Differential Evolution and other Evolutionary Algorithms. Based on the newly proposed Expected Fitness Improvement metric, the shift towards exploitation is demonstrated. The generalized Lehmer mean and Linear Bias Reduction are for the first time proposed to controПоказать полностьюl the parameter adaptation bias for the fitness improvement based L-SHADE and distance based Db-L-SHADE algorithms. The experiments are performed on the benchmark functions of the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation (CEC) 2017 competition on real-parameter optimization. The influence of the modified scaling factor and crossover rate adaptation is evaluated using Friedman ranking procedure and Mann–Whitney statistical test. The usage of Lehmer mean and Linear Bias Reduction is shown to deliver statistically better results for high-dimensional functions and improve the exploration properties of the search algorithm in the long-term perspective. To support the statements made, the search process is additionally analyzed using diversity measures and cluster analysis techniques.
Журнал: Information Sciences
Выпуск журнала: Т. 566
Номера страниц: 215-238
ISSN журнала: 00200255
Издатель: Elsevier Science Publishing Company, Inc.