Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163499
Title: An efficient differential evolution with fitness-based dynamic mutation strategy and control parameters
Authors: Gupta, Shubham
Su, Rong
Keywords: Science::Mathematics::Applied mathematics::Optimization
Issue Date: 2022
Source: Gupta, S. & Su, R. (2022). An efficient differential evolution with fitness-based dynamic mutation strategy and control parameters. Knowledge-Based Systems, 251, 109280-. https://dx.doi.org/10.1016/j.knosys.2022.109280
Project: A19D6a0053 
Journal: Knowledge-Based Systems 
Abstract: It is known that the performance of the differential evolution (DE) algorithm highly depends on the mutation strategy and its control parameters. However, it is arduous to choose an appropriate mutation strategy and control parameters for a given optimization problem. Therefore, in this paper, an efficient framework of the DE named EFDE is proposed with a novel fitness-based dynamic mutation strategy and control parameters. This algorithm avoids the burden of selecting appropriate mutation strategy and control parameters and tries to maintain an appropriate balance between diversity and convergence. In the EFDE, the proposed mutation strategy adopts a dynamic number of fitness-based leading individuals to utilize the evolutionary state of the EFDE population for the evolution procedure. Furthermore, a new way of defining the control parameters is introduced based on the evolutionary state of each individual involved during the trial vector generation process. A comprehensive comparison of the proposed EFDE over challenging sets of problems from a well-known benchmark set of 23 problems, CEC2014, and CEC2017 real parameter single objective competition against several state-of-the-art algorithms is performed. The proposed EFDE is also used to solve four engineering design problems. Comparison and analysis of results confirm that the EFDE provides very competitive and better solution accuracy as compared to the other state-of-the-art algorithms.
URI: https://hdl.handle.net/10356/163499
ISSN: 0950-7051
DOI: 10.1016/j.knosys.2022.109280
Schools: School of Electrical and Electronic Engineering 
Rights: © 2022 Elsevier B.V. All rights reserved. This paper was published in Knowledge-Based Systems and is made available with permission of Elsevier B.V.
Fulltext Permission: embargo_20240912
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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