A knowledge transfer-based adaptive differential evolution for solving nonlinear equation systems

doi:10.1016/j.knosys.2022.110214

A knowledge transfer-based adaptive differential evolution for solving nonlinear equation systems

2023-02-01

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DOI：10.1016/j.knosys.2022.110214

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Solving nonlinear equation systems (NESs) is an important yet challenging task in the field of numerical computation. It aims to locate multiple roots in a single run. However, the existing methods lack effective knowledge transfer. In this article, a knowledge transfer-based adaptive differential evolution is proposed to deal with NESs. Its main features are: (i) knowledge transfer between two niching techniques (crowding and speciation) is carried out to balance diversity and convergence; (ii) the variation characteristics of population diversity and convergence are used to judge knowledge transfer intensity; (iii) a knowledge transfer mechanism is designed to ensure that reasonable individuals are selected for the transfer to supplement the deficiencies of crowding and speciation; (iv) a parameter adaptation with niching level is introduced to improve search efficiency. Experiments on classical 30 NES problems have demonstrated that the proposed approach can outperform the state-of-the-art algorithms, in terms of root ratio and success rate.(c) 2022 Elsevier B.V. All rights reserved.

Keywords:

Nonlinear equations systems

Differential evolution

Knowledge transfer

A knowledge transfer-based adaptive differential evolution for solving nonlinear equation systems

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