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Wu Deng
Researcher at Civil Aviation University of China
Publications - 81
Citations - 5555
Wu Deng is an academic researcher from Civil Aviation University of China. The author has contributed to research in topics: Computer science & Optimization problem. The author has an hindex of 26, co-authored 62 publications receiving 3364 citations. Previous affiliations of Wu Deng include Southwest Jiaotong University & Shandong Institute of Business and Technology.
Papers
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Journal ArticleDOI
An Improved Ant Colony Optimization Algorithm Based on Hybrid Strategies for Scheduling Problem
Wu Deng,Junjie Xu,Huimin Zhao +2 more
TL;DR: The experiment results show that the proposed ICMPACO algorithm can effectively obtain the best optimization value in solving TSP and effectively solve the gate assignment problem, obtain better assignment result, and it takes on better optimization ability and stability.
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A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm
TL;DR: The fuzzy information entropy can accurately and more completely extract the characteristics of the vibration signal, the improved PSO algorithm can effectively improve the classification accuracy of LS-SVM, and the proposed fault diagnosis method outperforms the other mentioned methods.
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A novel collaborative optimization algorithm in solving complex optimization problems
Wu Deng,Huimin Zhao,Huimin Zhao,Li Zou,Li Zou,Li Zou,Guangyu Li,Guangyu Li,Xinhua Yang,Daqing Wu,Daqing Wu +10 more
TL;DR: The experiment results show that the proposed MGACACO algorithm can avoid falling into the local extremum, and takes on better search precision and faster convergence speed.
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Study on an improved adaptive PSO algorithm for solving multi-objective gate assignment
TL;DR: The experiment results show that the DOADAPO algorithm can improve the convergence speed and enhance the local search ability and global search ability, and the multi-objective optimization model of gate assignment can improved the comprehensive service of gate assignments.
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An Improved Quantum-Inspired Differential Evolution Algorithm for Deep Belief Network
TL;DR: An improved quantum-inspired differential evolution (MSIQDE), namely MSIQDE algorithm based on making use of the merits of the Mexh wavelet function, standard normal distribution, adaptive quantum state update, and quantum nongate mutation, is proposed to avoid premature convergence and improve the global search ability.