Search Results - "Deng, Changshou"

Robust post-processing time frequency technology and its application to mechanical fault diagnosis by Long, Junbo, Deng, Changshou, Wang, Haibin

“…Post-processing synchrosqueezing transform and synchroextracting transform methods can improve TFR resolution for fault diagnosis. The normal and fault signal…”
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Multi-strategy co-evolutionary differential evolution for mixed-variable optimization by Peng, Hu, Han, Yupeng, Deng, Changshou, Wang, Jing, Wu, Zhijian

“…In real world, engineering design problems have been regarded as common and complex problems which always include non-linear standard functions, many complex…”
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Robust Fractional Low-Order Multiple Window STFT for Infinite Variance Process Environment by Wang, Haibin, Deng, Changshou, Long, Junbo, Zhou, Youxue

“…Mechanical fault vibration signal is a typical non-Gaussian process, they can be characterized by the infinite variance process, and the noise within these…”
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Multi-strategy brain storm optimization algorithm with dynamic parameters adjustment by Liu, Jianan, Peng, Hu, Wu, Zhijian, Chen, Jianqiang, Deng, Changshou

“…As a novel swarm intelligence optimization algorithm, brain storm optimization (BSO) has its own unique capabilities in solving optimization problems. However,…”
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Enhancing Differential Evolution with Commensal Learning and Uniform Local Search by Peng, Hu, Wu, Zhijian, Deng, Changshou

“…Differential evolution (DE) is a popular and powerful evolutionary algorithm for global optimization problems. However, the combination of mutation strategies…”
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Semi-parametric training of autoencoders with Gaussian kernel smoothed topology learning neural networks by Xiang, Zhiyang, Deng, Changshou, Xiang, Xueting, Yu, Mali, Xiong, Jing

“…Autoencoders are essential for training multi-hidden layer neural networks. Parametric autoencoder trainings often require user selections of hidden neuron…”
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A Novel Hybrid Data Clustering Algorithm Based on Artificial Bee Colony Algorithm and K‐Means by Tran, Dang Cong, Wu, Zhijian, Wang, Zelin, Deng, Changshou

“…To improve the performance of K‐means clustering algorithm, this paper presents a new hybrid approach of Enhanced artificial bee colony algorithm and K‐means…”
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Firefly Algorithm With Luciferase Inhibition Mechanism by Peng, Hu, He, Yichen, Deng, Changshou, Wu, Zhijian

“…Firefly algorithm (FA) was proposed by Yang which inspired from communication between fireflies through flash. As an efficient swarm intelligence algorithm, FA…”
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An Enhanced Differential Evolution with Elite Chaotic Local Search by Yue, Xuezhi, Deng, Changshou, Huang, Haixia, Guo, Zhaolu, Wu, Zhijian

“…Differential evolution (DE) is a simple yet efficient evolutionary algorithm for real-world engineering problems. However, its search ability should be further…”
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Improved differential evolution with adaptive opposition strategy by Huichao Liu, Zhijian Wu, Hui Wang, Rahnamayan, Shahryar, Changshou Deng

“…Generalized opposition-based differential evolution (GODE) is an effective algorithm for global optimization over continuous search space. However, the…”
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Best neighbor-guided artificial bee colony algorithm for continuous optimization problems by Peng, Hu, Deng, Changshou, Wu, Zhijian

“…As a relatively recent invented swarm intelligence algorithm, artificial bee colony (ABC) becomes popular and is powerful for solving the tough continuous…”
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Enhancing firefly algorithm with courtship learning by Peng, Hu, Zhu, Wenhua, Deng, Changshou, Wu, Zhijian

“…The firefly algorithm (FA) is a nature-inspired heuristic optimization algorithm based on the luminescence and attraction behavior of fireflies. Although the…”
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Multi-strategy serial cuckoo search algorithm for global optimization by Peng, Hu, Zeng, Zhaogan, Deng, Changshou, Wu, Zhijian

“…Cuckoo search algorithm (CS) is a simple and effective nature-inspired optimization algorithm, which has been widely applied to solve the complex engineering…”
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Optimizing computation offloading under heterogeneous delay requirements for wireless powered mobile edge computing by Wan, Zheng, Dong, Xiaogang, Deng, Changshou

“…This paper studies the optimization design of a wireless powered mobile edge computing (WP-MEC) system with multiple edge servers, in which the heterogeneous…”
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Correction to: Optimizing computation offloading under heterogeneous delay requirements for wireless powered mobile edge computing by Wan, Zheng, Dong, Xiaogang, Deng, Changshou

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Multi-strategy multi-objective differential evolutionary algorithm with reinforcement learning by Han, Yupeng, Peng, Hu, Mei, Changrong, Cao, Lianglin, Deng, Changshou, Wang, Hui, Wu, Zhijian

“…Multiobjective evolutionary algorithms (MOEAs) have gained much attention due to their high effectiveness and efficiency in solving multiobjective optimization…”
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A multimodal multi-objective differential evolution with series-parallel combination and dynamic neighbor strategy by Peng, Hu, Xia, Wenwen, Luo, Zhongtian, Deng, Changshou, Wang, Hui, Wu, Zhijian

“…Multimodal multi-objective optimization problems (MMOPs), which aim to identify as many optimal solutions as possible and exhibit multiple equivalent Pareto…”
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Gene expression programming with dual strategies and neighborhood search for symbolic regression problems by Peng, Hu, Li, Lin, Mei, Changrong, Deng, Changshou, Yue, Xuezhi, Wu, Zhijian

“…As an evolutionary algorithm for automatic generation of computer programs, Gene expression programming (GEP) has received a lot of attention because it can…”
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A Spark-based differential evolution with grouping topology model for large-scale global optimization by He, Zhihui, Peng, Hu, Chen, Jianqiang, Deng, Changshou, Wu, Zhijian

“…Over the past few years, cloud computing model (e.g., Spark) has aroused huge attention. Differential evolution (DE) has been applied to cloud computing models…”
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Enhancing differential evolution with random neighbors based strategy by Peng, Hu, Guo, Zhaolu, Deng, Changshou, Wu, Zhijian

“…•We describe a mutation strategy DE/neighbor/1 to balance the global and local search.•For each individual, the neighbors are chosen from the population in a…”
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