Differential Evolution: A Survey and Analysis
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Authors
Eltaeib, Tarik
Mahmood, Ausif
Issue Date
2018-10-16
Type
Article
Language
en_US
Keywords
Differential evolution , Optimization , Stochastic
Alternative Title
Abstract
Differential evolution (DE) has been extensively used in optimization studies since its development in 1995 because of its reputation as an effective global optimizer. DE is a population-based metaheuristic technique that develops numerical vectors to solve optimization problems. DE strategies have a significant impact on DE performance and play a vital role in achieving stochastic global optimization. However, DE is highly dependent on the control parameters involved. In practice, the fine-tuning of these parameters is not always easy. Here, we discuss the improvements and developments that have been made to DE algorithms. In particular, we present a state-of-the-art survey of the literature on DE and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques.
Description
Citation
Eltaeib, T.; Mahmood, A. Differential Evolution: A Survey and Analysis. Applied Sciences. 2018, 8, 1945.
Publisher
MDPI