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Yueting Xu
Researcher at Wenzhou University
Publications - 13
Citations - 1733
Yueting Xu is an academic researcher from Wenzhou University. The author has contributed to research in topics: Local optimum & Global optimization. The author has an hindex of 11, co-authored 13 publications receiving 1137 citations. Previous affiliations of Yueting Xu include South China University of Technology.
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Enhanced Moth-flame optimizer with mutation strategy for global optimization
TL;DR: GM is introduced into the basic MFO to improve neighborhood-informed capability, CM with a large mutation step is adopted to enhance global exploration ability and LM is embedded to increase the randomness of search agents’ movement.
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A balanced whale optimization algorithm for constrained engineering design problems
TL;DR: Two novel effective strategies composed of Levy flight and chaotic local search are synchronously introduced into the whale optimization algorithm to guide the swarm and further promote the harmony between the inclusive exploratory and neighborhood-informed capacities of the conventional technique.
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An improved grasshopper optimization algorithm with application to financial stress prediction
TL;DR: The improved GOA which combines three strategies to achieve a more suitable balance between exploitation and exploration was established and the proposed learning scheme can guarantee a more stable kernel extreme learning machine model with higher predictive performance compared to others.
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An efficient chaotic mutative moth-flame-inspired optimizer for global optimization tasks
Yueting Xu,Huiling Chen,Ali Asghar Heidari,Ali Asghar Heidari,Jie Luo,Qian Zhang,Xuehua Zhao,Chengye Li +7 more
TL;DR: The proposed CLSGMFO can serve as an effective and efficient computer-aided tool for financial prediction and demonstrate that the proposed learning scheme can offer a superior kernel extreme learning machine model with excellent predictive performance.
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An enhanced Bacterial Foraging Optimization and its application for training kernel extreme learning machine
TL;DR: The experimental results show that the proposed CCGBFO significantly outperforms the original BFO in terms of both convergence speed and solution accuracy.