Adaptive Differential Evolution with the Stagnation Termination Mechanism
Year: 2024
Type: article
Abstract: Differential evolution is capable of searching for the optimum for different types of optimization problems with a few inputs, which has gained worldwide popularity. In this paper, we propose a parameters adaptation scheme based on the stagnation ratio (PASR), which regards the stagnation ratio (STR) as the indicator for adjusting the control parameters and greediness parameter. To be specific, when the STR is larger than the predefined threshold, exploration is advocated. In this circumstance, larger control parameters and a greediness parameter are adopted. However, when the STR is smaller than the predefined threshold, exploitation is preferred. In this case, smaller control parameters and a greediness parameter are utilized. Further, when the stagnation lasts for a long period, a generation-based selection (GBS) scheme is developed to help it escape from the local optimum and stagnation. Comparative experiments have been implemented on the CEC2017 to testify the effectiveness of adaptive differential evolution with the stagnation termination mechanism (STMDE) and its components. The competitiveness of the STMDE is also verified via comparing it to top-performing DE variants in the practical optimization problem selected from the CEC2011.
Source: Mathematics
DOI: 10.3390/math12203168
Authors: Yuhong Liu, Liming Zheng, Bohan Cai
Institution: Jinan University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,955
OpenAlex ID: W4403296966