Differential Evolution: A Survey and Analysis
Source abstract
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.
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Research context
OpenAlex topics: Metaheuristic Optimization Algorithms Research · Evolutionary Algorithms and Applications · Advanced Multi-Objective Optimization Algorithms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Computer Science and Engineering Department, University of Bridgeport, Bridgeport, CT 06614, USA
References (100+)
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