Solving Nonlinear Equation Systems by a Two-Phase Evolutionary Algorithm

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)5652-5663
Journal / PublicationIEEE Transactions on Systems, Man, and Cybernetics: Systems
Issue number9
Online published20 Dec 2019
Publication statusPublished - Sep 2021


A two-phase evolutionary algorithm is developed to find multiple solutions of a nonlinear equations system. It transforms a nonlinear equations system into a multimodal optimization problem. In phase one of the proposed algorithm, a strategy combines a multiobjective optimization technique and a niching technique to maintain the population diversity. Phase two consists of a detection method and a local search method for encouraging the convergence. The detection method finds several promising subregions and the local search method locates the corresponding optimal solutions in each promising subregion. The experiments on a set of 30 nonlinear equation systems demonstrate that the proposed algorithm is better than other state-of-the-art algorithms.

Research Area(s)

  • Multiobjective optimization technique, niching technique, nonlinear equation systems (NESs), population diversity