Differential evolution using multi-strategy for the improvement of optimization performance

Differential evolution (DE) is an effective population-based optimization approach that has been widely used to deal with scientific and engineering problems. However, the performance of DE method is largely dependent on its trial vector produce strategy, namely, mutation strategy, crossover operati...

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Veröffentlicht in:	Neural computing & applications Jg. 37; H. 27; S. 22593 - 22620
Hauptverfasser:	Liu, Nengxian, Luo, Jianbin, Chang, Jie, Pan, Jeng-Shyang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.09.2025 Springer Nature B.V
Schlagworte:	Adaptation Archives & records Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Evolutionary computation Exploitation Image Processing and Computer Vision Methods Mutation Normal distribution Optimization Optimization algorithms Parameters Probability and Statistics in Computer Science S.I.: Hybrid Approaches to Nature-inspired Optimization Algorithms and Their Applications Special Issue on Hybrid Approaches to Nature-inspired Optimization Algorithms and Their Applications Stagnation Parameter adaptation schemes Local search Multi-strategy Stagnation problem
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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