The above article from IET Communications, published online on 4 November 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Interim Editor‐in‐Chief, Jian Ren, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.
Differential Evolution (DE) is a widely used algorithm for solving global optimization problems. The success of DE heavily relies on its mutation operation, which plays a crucial role in generating diverse and high-quality solutions. In this paper different mutation operations for enhancing the performance of DE in global optimization tasks has been considered. Here, a novel mutation strategy that aim to strike a balance between exploration and exploitation to improve the convergence speed and quality has been proposed. The proposed DE is basically focused on novel mutation-based strategy where a new coefficient factor $("\sigma")$ is involved with the base vector in basic mutation strategy $("DE/rand/1")$ to enhance the convergence of local variable during the exploitation and to improve the convergence rate as well as convergence quality. Additionally, we evaluate the proposed mutation operations on a set of 27 benchmark functions commonly used in global optimization. Experimental results demonstrate that the enhanced mutation strategies significantly outperform the state-of-the-art algorithms in terms of solution accuracy, convergence speed. The findings highlight the importance of mutation operations in DE and provide valuable insights into designing more effective mutation strategies for tackling complex global optimization problems.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.