zc0315 Goto Github PK

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Name: Chong Zhou

Type: User

Bio: https://zc0315.github.io

I received my M.S. degree in the School of Information Engineering from China University of Geosciences and Ph.D in the School of Computer from China University of Geosciences, Wuhan in 2015 and 2018, respectively. I am currently lecturer in the School of Information Engineering, Hebei GEO University.

News

A paper entitled:"Multi-objective Harris Hawks Optimization with Associative Learning and Chaotic Local Search for Feature Selection" was accepted at IEEE access.

Research Interest

My research interests include artificial intelligence, evolutionary computation, multi/many-objective optimization, machine learning and its application.

evolutionary computation
- multi/many-objective optimization
- multi-objective feature selection
- image segmentation
- closed loop detection
deep learning
- water gauge recognition

Selected Publications

Zhang, Y., Zhang, Y., Zhang, C., & Zhou, C., Multi-objective Harris Hawks Optimization with Associative Learning and Chaotic Local Search for Feature Selection. IEEE Access, 2022
Zhou, C., Dai, G., Zhang, C., Li, X., & Ma, K. Entropy based evolutionary algorithm with adaptive reference points for many-objective optimization problems. Information Sciences, 2018, 465, 232-247.
Dai, G., Zhou, C.*, Wang, M., & Li, X. Indicator and reference points coguided evolutionary algorithm for many-objective optimization problems. Knowledge-Based Systems, 2018, 140, 50-63.
Zhou C., Li S, Zhang Y, Chen Z, Zhang C. Enhancing Backtracking Search Algorithm using Reflection Mutation Strategy Based on Sine Cosine. Algorithms. 2019; 12(11):225.
Chong Zhou, Guangming Dai, Enhanced θ dominance and density selection based evolutionary algorithm for many-objective optimization problems, Applied Intelligence, press online, 2017, DOI: 10.1007/s10489-017-0998-9（IF 1.90，CCF C）
Zhou, C., Chen, L., Chen, Z., Li, X., & Dai, G. A sine cosine mutation based differential evolution algorithm for solving node location problem. International Journal of Wireless and Mobile Computing, 2017, 13(3), 253-259.
Liang Chen, Chong Zhou, Guangming Dai, An improved differential evolution algorithm based on suboptimal solution mutation, International Journal of Computing Science and Mathematics, Vol. 8, No 1, 2017. DOI:10.1504/IJCSM.2017.083141
Liang Chen, Chong Zhou, Entropy determined hybrid two-stage multi-objective evolutionary algorithm combining locally linear embedding, IEEE Congress on Evolutionary Computation, CEC 2016, DOI: 10.1109/CEC.2016.7744109

Grants&Awards

Natural Science Youth Foundation of Hebei Province
PhD Research Startup Foundation of Hebei GEO University
Open Research Project of the Hubei Key Laboratory of Intelligent Geo-Information Processing
Fundamental Research Funds for the Universities in Hebei Province

Teaching

Javaweb development
Javeweb framework

Contact

email:[email protected]

Chong Zhou's Projects

2048-rl

2048 Reinforcement Learning

android-tech-frontier

一个定期翻译国外Android优质的技术、开源库、软件架构设计、测试等文章的开源项目

article-summarizer

Uses frequency analysis to summarize text.

articletranslateproject

AndroidWeekly**文章翻译项目

awesome

A curated list of awesome lists

awesome-android-ui

A curated list of awesome Android UI/UX libraries

awesome-open-gpt

Collection of Open Source Projects Related to GPT/GPT相关开源项目合集🚀、精选🛠

awesome-rl

Reinforcement learning resources curated

beginlearning

Practices for machine learning

bookcode

co-with-gnns-example

组合优化问题用图神经网络

code

Code for the book "Mastering OpenCV with Practical Computer Vision Projects" by Packt Publishing 2012.

computer-network-a-top-down-approach-answer

计算机网络-自顶向下方法习题/编程/实验答案

deap

Distributed Evolutionary Algorithms in Python

deep-symbolic-optimization

A deep learning framework for symbolic optimization.

deeplearning

Deep Learning (Python, C/C++, Java, Scala, Go)

deeplearningtutorials

Deep Learning Tutorial notes and code. See the wiki for more info.

dynamic_pointer_network

an implementation of Pointer Network using tensorflow

easings.net

Easing Functions Cheat Sheet 缓动函数速查表

easystyle

All kinds of neural style transformer

evolutiontool

演化计算平台，蜂群算法，差分进化算法等

free-programming-books

gluon-tutorials-zh

通过MXNet/Gluon来动手学习深度学习

$identifying-the-parameters-of-the-integer-and-fractional-order-dynamic-pv-models icon$ identifying-the-parameters-of-the-integer-and-fractional-order-dynamic-pv-models

In the case of static PV modeling (single, double, and three diode models), the load variation and switching operation of the inverter and DC/DC converter stages are not considered. Therefore, another type of PV model named integer order dynamic PV model (IOM) has been introduced, which is the most efficient and accurate model to handle the static models' aforementioned drawbacks. That is why the dynamic model is the preferable one for the design of the grid-connected PV systems. Recently, the theory of fractional calculus has been employed to reinforce the efficiency and flexibility of IOM. As a result, the fractional-order dynamic PV model (FOM) has been introduced as the latest trend in tackling the PV models' dynamic behavior. The accuracy of the dynamic PV models is mainly influenced by obtaining their parameters under different operating conditions. The manufacturers usually undefine the models’ parameters. Therefore, it is crucial to identify these parameters accurately with minimum execution time using the experimental load current–time (I-T) curve [1]-[2]-[3]. [1]AbdelAty AM, Radwan AG, Elwakil AS, Psychalinos C. Transient and steady-state response of a fractional-order dynamic PV model under different loads. J Circ Syst Comput 2018;27(02):1850023. https://doi.org/10.1142/s0218126618500238 [2] Yousri, D., Allam, D., Eteiba, M.B. and Suganthan, P.N., 2019. Static and dynamic photovoltaic models’ parameters identification using Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer variants. Energy conversion and management, 182, pp.546-563. [3] Enhanced Marine Predators Algorithm for identifying static and dynamic Photovoltaic models parameters March 2021 Energy Conversion and Management ( In proofing). Note: To implement the code for optimizing the fractional order model. The user should click on fomcon-1.21b right click and select add to the path ( then select folders and subfolders) to let all the inside files are readable. Then use main to implement the optimization process