Evolutionary algorithm with multiobjective optimization technique for solving nonlinear equation systems

DOI10.1016/j.ins.2020.06.042zbMath1475.65028OpenAlexW3041013467MaRDI QIDQ2053832

Jingwei Xu, Shengqi Zhu, Yuting Luo, Wei-Feng Gao

Publication date: 30 November 2021

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2020.06.042

zbMATH Keywords

transformation techniquedifferential evolution (DE)K-means clusteringnonlinear equation systemsmultiobjective optimization technique

Mathematics Subject Classification ID

Evolutionary algorithms, genetic algorithms (computational aspects) (68W50) Multi-objective and goal programming (90C29) Numerical computation of solutions to systems of equations (65H10) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (3)

An improved dynamic multi-objective optimization approach for nonlinear equation systems ⋮ Decomposition-based multiobjective optimization for nonlinear equation systems with many and infinitely many roots ⋮ Evolutionary multitasking for solving nonlinear equation systems

Uses Software

• JADE

Cites Work

• A new approach based on the Newton's method to solve systems of nonlinear equations
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• Applying genetic algorithms for solving nonlinear algebraic equations
• Joint Range and Angle Estimation Using MIMO Radar With Frequency Diverse Array
• A Class of Methods for Solving Nonlinear Simultaneous Equations

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