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Solving Nonlinear Equation Systems by a Two-Phase Evolutionary Algorithm
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DOI：10.1109/TSMC.2019.2957324.png)
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Abstract
En 中文
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.
Keywords:
Optimization
Sociology
Statistics
Convergence
Nonlinear equations
Transforms
Maintenance engineering
Multiobjective optimization technique
niching technique
nonlinear equation systems (NESs)
population diversity
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