Research Area:  Metaheuristic Computing

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 adaptive, self-adaptive and hybrid techniques. 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.

Keywords:  
differential evolution
optimization
stochastic
adaptive
self-adaptive
hybrid techniques
self-adaptive
hybrid techniques

Author(s) Name:  Tarik Eltaeib, and Ausif Mahmood

Publisher name:  MDPI

DOI:  10.3390/app8101945

Volume Information:  8(10), 1945

Paper Link:   https://www.mdpi.com/2076-3417/8/10/1945