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• W2966933481 abstract "Finding multiple roots of nonlinear equations systems (NESs) in a single run is an important yet difficult task. It requires to keep a balance between explorative and exploitative traits. In this paper, we present a random walk mutation-based differential evolution (DE) with estimation of distribution algorithm (EDA) to address this problem. The major characteristics are: i) the random walk mutation is capable of preserving the population diversity , which guides individuals to move toward different promising regions; ii) probability selection is employed to provide suitable parent individuals for evolution; iii) EDA is used to accelerate the convergence and obtains the roots. To evaluate the performance of our approach, 30 NESs with diverse features are selected as test suite. Experimental results indicate that the proposed approach is able to yield better performance compared with other state-of-the-art methods." @default.
• W2966933481 created "2019-08-22" @default.
• W2966933481 creator A5001217995 @default.
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• W2966933481 date "2019-06-01" @default.
• W2966933481 modified "2023-10-18" @default.
• W2966933481 title "Random Walk Mutation-based DE with EDA for Nonlinear Equations Systems" @default.
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• W2966933481 doi "https://doi.org/10.1109/cec.2019.8790111" @default.
• W2966933481 hasPublicationYear "2019" @default.
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