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• W4387580086 startingPage "122025" @default.
• W4387580086 abstract "Locating multiple roots of nonlinear equation systems (NESs) remains a challenging and meaningful task in the numerical optimization community. Although a large number of NES-solving approaches have been put forward, they can only find the roots of one NES at a time. In this paper, we develop a novel NES-solving algorithm based on evolutionary multitasking referred to as EMNES, the goal of which is to effectively find the multiple roots of multiple different NESs simultaneously in a single run through knowledge sharing and transfer. Specifically, firstly a NES-solving framework based on evolutionary multitasking is proposed. Then an efficient multi-task evolutionary algorithm based on neighborhood-based speciation differential evolution for NESs is designed. Finally, combining Gaussian distribution and uniform distribution, a novel resource release strategy is proposed to release the found roots to improve resource utilization and increase population diversity. Numerous experimental results reveal that the proposed EMNES algorithm can achieve a higher root rate and success rate when compared with several well-established algorithms on thirty NESs. Furthermore, simulation results on a more complex test set show that the proposed EMNES is able to locate more roots than most comparison algorithms." @default.
• W4387580086 created "2023-10-13" @default.
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• W4387580086 date "2023-10-01" @default.
• W4387580086 modified "2023-10-14" @default.
• W4387580086 title "Solving nonlinear equation systems based on evolutionary multitasking with neighborhood-based speciation differential evolution" @default.
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• W4387580086 doi "https://doi.org/10.1016/j.eswa.2023.122025" @default.
• W4387580086 hasPublicationYear "2023" @default.
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