QQLMPA : A quasi-opposition learning and Q-learning based marine predators algorithm

Journal article


Zhao, Shangrui, Wu, Yulu, Tan, Shuang, Wu, Jinran, Cui, Zhesen and Wang, You-Gan. (2023). QQLMPA : A quasi-opposition learning and Q-learning based marine predators algorithm. Expert Systems with Applications. 213(Part C), p. Article 119246. https://doi.org/10.1016/j.eswa.2022.119246
AuthorsZhao, Shangrui, Wu, Yulu, Tan, Shuang, Wu, Jinran, Cui, Zhesen and Wang, You-Gan
Abstract

Many engineering and scientific problems in the real-world boil down to optimization problems, which are difficult to solve by using traditional methods. Meta-heuristics are appealing algorithms for solving optimization problems while keeping computational costs reasonable. The marine predators algorithm (MPA) is a modern optimization meta-heuristic, inspired by widespread Lévy and Brownian foraging strategies in ocean predators as well as optimal encounter rate strategies in biological interactions between predator and prey. However, MPA is not without its shortcomings. In this paper, a quasi-opposition based learning and Q-learning based marine predators algorithm (QQLMPA) is proposed. This offers multiple improvements over standard MPA. Primely, Q-learning allows MPA to fully use the information generated by previous iterations. And also, quasi-opposition based learning serves to increase population diversity, reducing the risk of convergence to inferior local optima. Numerical experiments demonstrate better performance by QQLMPA on 32 benchmark optimization functions and three engineering problems: designs of pressure vessel, hydro-static thrust bearing, and speed reducer.

KeywordsQ-learning algorithm; marine predators algorithm; meta-heuristics; quasi-opposition based learning
Year2023
JournalExpert Systems with Applications
Journal citation213 (Part C), p. Article 119246
PublisherElsevier Ltd
ISSN0957-4174
Digital Object Identifier (DOI)https://doi.org/10.1016/j.eswa.2022.119246
Scopus EID2-s2.0-85142200037
Page range1-19
FunderAustralian Research Council (ARC)
Chinese Fundamental Research Funds for the Central Universities
Publisher's version
License
All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online12 Nov 2022
Publication process dates
Accepted06 Nov 2022
Deposited19 Jul 2023
Grant IDDP160104292
WUT: 213114009
Permalink -

https://acuresearchbank.acu.edu.au/item/8z553/qqlmpa-a-quasi-opposition-learning-and-q-learning-based-marine-predators-algorithm

Restricted files

Publisher's version

  • 25
    total views
  • 0
    total downloads
  • 0
    views this month
  • 0
    downloads this month
These values are for the period from 19th October 2020, when this repository was created.

Export as

Related outputs

Improved prediction of local significant wave height by considering the memory of past winds
Zhang, Shaotong, Yang, Zhen, Zhang, Yaqi, Zhao, Shangrui, Wu, Jinran, Wang, Chenghao, Wang, You-Gan, Jeng, Dong-Sheng, Nielsen, Peter, Li, Guangxue and Li, Sanzhong. (2023). Improved prediction of local significant wave height by considering the memory of past winds. Water Resources Research. 59(8), p. Article e2023WR034974. https://doi.org/10.1029/2023WR034974
Inferring circadian gene regulatory relationships from gene expression data with a hybrid framework
Hu, Shuwen, Jing, Yi, Li, Tao, Wang, You-Gan, Liu, Zhenyu, Gao, Jing and Tian, Yu-Chu. (2023). Inferring circadian gene regulatory relationships from gene expression data with a hybrid framework. BMC Bioinformatics. 24(1), p. Article 362. https://doi.org/10.1186/s12859-023-05458-y
QL-ADIFA : Hybrid optimization using Q-learning and an adaptive logarithmic spiral-levy firefly algorithm
Tan, Shuang, Zhao, Shangrui and Wu, Jinran. (2023). QL-ADIFA : Hybrid optimization using Q-learning and an adaptive logarithmic spiral-levy firefly algorithm. Mathematical Biosciences and Engineering. 20(8), pp. 13542-13561. https://doi.org/10.3934/mbe.2023604
Mixture extreme learning machine algorithm for robust regression
Zhao, Shangrui, Chen, Xuan-Ang, Wu, Jinran and Wang, You-Gan. (2023). Mixture extreme learning machine algorithm for robust regression. Knowledge-Based Systems. 280, p. Article 111033. https://doi.org/10.1016/j.knosys.2023.111033
Event-triggered output feedback control for a class of nonlinear systems via disturbance observer and adaptive dynamic programming
Yang, Yang, Fan, Xin, Gao, Weinan, Yue, Wenbin, Liu, Aaron, Geng, Shuocong and Wu, Jinran. (2023). Event-triggered output feedback control for a class of nonlinear systems via disturbance observer and adaptive dynamic programming. IEEE Transactions on Fuzzy Systems. 31(9), pp. 3148-3160. https://doi.org/10.1109/TFUZZ.2023.3245294
Robust regression for electricity demand forecasting against cyberattacks
VandenHeuvel, Daniel, Wu, Jinran and Wang, You-Gan. (2023). Robust regression for electricity demand forecasting against cyberattacks. International Journal of Forecasting. 39(4), pp. 1573-1592. https://doi.org/10.1016/j.ijforecast.2022.10.004
An evaluation of the impact of COVID-19 lockdowns on electricity demand
Wu, Jinran, Levi, Noa, Araujo, Robyn and Wang, You-Gan. (2023). An evaluation of the impact of COVID-19 lockdowns on electricity demand. Electric Power Systems Research. 216, p. Article 109015. https://doi.org/10.1016/j.epsr.2022.109015
Robust adaptive rescaled lncosh neural network regression toward time-series forecasting
Yang, Yang, Zhou, Hu, Wu, Jinran, Ding, Zhe, Tian, Yu-Chu, Yue, Dong and Wang, You-Gan. (2023). Robust adaptive rescaled lncosh neural network regression toward time-series forecasting. IEEE Transactions on Systems, Man and Cybernetics: Systems. pp. 1-12. https://doi.org/10.1109/TSMC.2023.3272880
Does one subgenome become dominant in the formation and evolution of a polyploid?
Liu, Chunji and Wang, You-Gan. (2023). Does one subgenome become dominant in the formation and evolution of a polyploid? Annals of Botany. 131(1), pp. 11-16. https://doi.org/10.1093/aob/mcac024
Event-trigger-based fault-tolerant control of uncertain non-affine systems with predefined performance
Yang, Yang, Zhang, Yuwei, Wang, Zijin, Wu, Jinran and Si, Xuefeng. (2023). Event-trigger-based fault-tolerant control of uncertain non-affine systems with predefined performance. International Journal of Control, Automation and Systems. 21(2), pp. 519-535. https://doi.org/10.1007/s12555-021-1007-y
A novel deep learning framework with a COVID-19 adjustment for electricity demand forecasting
Cui, Zhesen, Wu, Jinran, Lian, Wei and Wang, You-Gan. (2023). A novel deep learning framework with a COVID-19 adjustment for electricity demand forecasting. Energy Reports. 9, pp. 1887-1895. https://doi.org/10.1016/j.egyr.2023.01.019
Accelerated computation of the genetic algorithm for energy-efficient virtual machine placement in data centers
Ding, Zhe, Tian, Yu-Chu, Wang, You-Gan, Zhang, Wei-Zhe and Yu, Zu-Guo. (2023). Accelerated computation of the genetic algorithm for energy-efficient virtual machine placement in data centers. Neural Computing and Applications. 35(7), pp. 5421-5436. https://doi.org/10.1007/s00521-022-07941-8
Prediction of shear stress induced by shoaling internal solitary waves based on machine learning method
Tian, Zhuangcai, Liu, Hanlu, Zhang, Shaotong, Wu, Jinran and Tian, Jiahao. (2023). Prediction of shear stress induced by shoaling internal solitary waves based on machine learning method. Marine Georesources and Geotechnology. 41(2), pp. 221-232. https://doi.org/10.1080/1064119X.2022.2136045
Predictions of machine learning with mixed-effects in analyzing longitudinal data under model misspecification
Hu, Shuwen, Wang, You-Gan, Drovandi, Christopher and Cao, Taoyun. (2023). Predictions of machine learning with mixed-effects in analyzing longitudinal data under model misspecification. Statistical Methods and Applications. 32(2), pp. 681-711. https://doi.org/10.1007/s10260-022-00658-x
A working likelihood approach to support vector regression with a data-driven insensitivity parameter
Wu, Jinran and Wang, You-Gan. (2023). A working likelihood approach to support vector regression with a data-driven insensitivity parameter. International Journal of Machine Learning and Cybernetics. 14(3), pp. 929-945. https://doi.org/10.1007/s13042-022-01672-x
An integrated federated learning algorithm for short-term load forecasting
Yang, Yang, Wang, Zijin, Zhao, Shangrui and Wu, Jinran. (2023). An integrated federated learning algorithm for short-term load forecasting. Electric Power Systems Research. 214, p. Article 108830. https://doi.org/10.1016/j.epsr.2022.108830
A new algorithm for support vector regression with automatic selection of hyperparameters
Wang, You-Gan, Wu, Jinran, Hu, Zhi-Hua and McLachlan, Geoffrey J.. (2023). A new algorithm for support vector regression with automatic selection of hyperparameters. Pattern Recognition. 133, p. Article 108989. https://doi.org/10.1016/j.patcog.2022.108989
A hybrid deep learning framework for air quality prediction with spatial autocorrelation during the COVID‑19 pandemic
Zhao, Zixi, Wu, Jinran, Cai, Fengjing, Zhang, Shaotong and Wang, You-Gan. (2023). A hybrid deep learning framework for air quality prediction with spatial autocorrelation during the COVID‑19 pandemic. Scientific Reports. 13(1), pp. 1-17. https://doi.org/10.1038/s41598-023-28287-8
Iterative learning in support vector regression with heterogeneous variances
Wu, Jinran and Wang, You-Gan. (2023). Iterative learning in support vector regression with heterogeneous variances. IEEE Transactions on Emerging Topics in Computational Intelligence. 7(2), pp. 513-522. https://doi.org/10.1109/TETCI.2022.3182725
Parameter estimation for univariate skew-normal distribution based on the modified empirical characteristic function
Hou, Gege, Xu, Ancha, Cai, Fengjing and Wang, You-Gan. (2022). Parameter estimation for univariate skew-normal distribution based on the modified empirical characteristic function. Communications in Statistics - Theory and Methods. 51(22), pp. 7897-7910. https://doi.org/10.1080/03610926.2021.1883655
A novel decompose-cluster-feedback algorithm for load forecasting with hierarchical structure
Yang, Yang, Zhou, Hu, Wu, Jinran, Liu, Chan-Juan and Wang, You-Gan. (2022). A novel decompose-cluster-feedback algorithm for load forecasting with hierarchical structure. International Journal of Electrical Power and Energy Systems. 142(Part A), p. Article 108249. https://doi.org/10.1016/j.ijepes.2022.108249
Solving a class of high-order elliptic pdes using deep neural networks based on its coupled scheme
Li, Xi'An, Wu, Jinran, Zhang, Lei and Tai, Xin. (2022). Solving a class of high-order elliptic pdes using deep neural networks based on its coupled scheme. Mathematics. 10(22), p. Article 4186. https://doi.org/10.3390/math10224186
Optimal battery capacity in electrical load scheduling
Duan, Qibin, Wu, Jinran and Wang, You-Gan. (2022). Optimal battery capacity in electrical load scheduling. Journal of Energy Storage. 50, p. Article 104190. https://doi.org/10.1016/j.est.2022.104190
An effective distance-based centrality approach for exploring the centrality of maritime shipping network
Kuang, Zengjie, Liu, Chanjuan, Wu, Jinran and Wang, You-Gan. (2022). An effective distance-based centrality approach for exploring the centrality of maritime shipping network. Heliyon. 8(11), p. Article e11474. https://doi.org/10.1016/j.heliyon.2022.e11474
In situ observations of hydro-sediment dynamics on the abandoned Diaokou lobe of the Yellow River Delta : Erosion mechanism and rate
Zhang, Shaotong, Zhang, Yaqi, Xu, Jishang, Guo, Lei, Li, Guangxue, Jia, Yonggang, Qiao, Lulu, Wu, Jinran, Wen, Mingzheng and Zhu, Chaoqi. (2022). In situ observations of hydro-sediment dynamics on the abandoned Diaokou lobe of the Yellow River Delta : Erosion mechanism and rate. Estuarine, Coastal and Shelf Science. 277, p. Article 108065. https://doi.org/10.1016/j.ecss.2022.108065
A statistical learning framework for spatial-temporal feature selection and application to air quality index forecasting
Zhao, Zixi, Wu, Jinran, Cai, Fengjing, Zhang, Shaotong and Wang, You-Gan. (2022). A statistical learning framework for spatial-temporal feature selection and application to air quality index forecasting. Ecological Indicators. 144, p. Article 109416. https://doi.org/10.1016/j.ecolind.2022.109416
Overseas warehouse deployment for cross-border e-commerce in the context of the Belt and Road Initiative
Liu, Chanjuan, Wu, Jinran and Lakshika Jayetileke, Harshanie. (2022). Overseas warehouse deployment for cross-border e-commerce in the context of the Belt and Road Initiative. Sustainability. 14(15), p. Article 9642. https://doi.org/10.3390/su14159642
An asymmetric bisquare regression for mixed cyberattack-resilient load forecasting
Zhao, Shangrui, Wu, Qingyue, Zhang, Yueyi, Wu, Jinran and Li, Xi-An. (2022). An asymmetric bisquare regression for mixed cyberattack-resilient load forecasting. Expert Systems with Applications. 210, p. Article 118467. https://doi.org/10.1016/j.eswa.2022.118467
An optimal statistical regression model for predicting wave-induced equilibrium scour depth in sandy and silty seabeds beneath pipelines
Zhang, Yaqi, Wu, Jinran, Zhang, Shaotong, Li, Guangxue, Jeng, Dong-Sheng, Xu, Jishang, Tian, Zhuangcai and Xu, Xingyu. (2022). An optimal statistical regression model for predicting wave-induced equilibrium scour depth in sandy and silty seabeds beneath pipelines. Ocean Engineering. 258, p. Article 111709. https://doi.org/10.1016/j.oceaneng.2022.111709
An effective dimensionality reduction approach for short-term load forecasting
Yang, Yang, Wang, Zijin, Gao, Yuchao, Wu, Jinran, Zhao, Shangrui and Ding, Zhe. (2022). An effective dimensionality reduction approach for short-term load forecasting. Electric Power Systems Research. 210, p. Article 108150. https://doi.org/10.1016/j.epsr.2022.108150
A hybrid robust system considering outliers for electric load series forecasting
Yang, Yang, Tao, Zhenghang, Qian, Chen, Gao, Yuchao, Zhou, Hu, Ding, Zhe and Wu, Jinran. (2022). A hybrid robust system considering outliers for electric load series forecasting. Applied Intelligence. 52(2), pp. 1630-1652. https://doi.org/10.1007/s10489-021-02473-5
Event-triggered output feedback containment control for a class of stochastic nonlinear multi-agent systems
Yang, Yang, Xi, Xiaorui, Miao, Songtao and Wu, Jinran. (2022). Event-triggered output feedback containment control for a class of stochastic nonlinear multi-agent systems. Applied Mathematics and Computation. 418, p. Article 126817. https://doi.org/10.1016/j.amc.2021.126817
An efficient DBSCAN optimized by arithmetic optimization algorithm with opposition-based learning
Yang, Yang, Qian, Chen, Li, Haomiao, Gao, Yuchao, Wu, Jinran, Liu, Chan-Juan and Zhao, Shangrui. (2022). An efficient DBSCAN optimized by arithmetic optimization algorithm with opposition-based learning. Journal of Supercomputing. 78(18), pp. 19566-19604. https://doi.org/10.1007/s11227-022-04634-w
Spatio-temporal quantile regression analysis revealing more nuanced patterns of climate change : A study of long-term daily temperature in Australia
Duan, Qibin, McGrory, Clare A., Brown, Glenn, Mengersen, Kerrie and Wang, You-Gan. (2022). Spatio-temporal quantile regression analysis revealing more nuanced patterns of climate change : A study of long-term daily temperature in Australia. PLoS ONE. 17, p. Article e0271457. https://doi.org/10.1371/journal.pone.0271457
Performance of variance estimators in the analysis of longitudinal data with a large cluster size
Hu, Shuwen, Wang, You-Gan and Fu, Liya. (2022). Performance of variance estimators in the analysis of longitudinal data with a large cluster size. Journal of Statistical Computation and Simulation. 92(1), pp. 1-18. https://doi.org/10.1080/00949655.2021.1929983
Ridgelized Hotelling's T2 test on mean vectors of large dimension
Ha, Gao-Fan, Zhang, Qiuyan, Bai, Zhidong and Wang, You-Gan. (2022). Ridgelized Hotelling's T2 test on mean vectors of large dimension. Random Matrices: Theory and Application. 11(1), p. Article 2250011. https://doi.org/10.1142/S2010326322500113
Distribution, transfer process and influence factors of phosphorus at sediment-water interface in the Huaihe River
Xu, Jing, Mo, Yuming, Tang, Hongwu, Wang, Kun, Ji, Qingfeng, Zhang, Pei, Wang, You-Gan, Jin, Guangqiu and Li, Ling. (2022). Distribution, transfer process and influence factors of phosphorus at sediment-water interface in the Huaihe River. Journal of Hydrology. 612, p. Article 128079. https://doi.org/10.1016/j.jhydrol.2022.128079
A new mixture copula model for spatially correlated multiple variables with an environmental application
Abraj, Mohomed, Wang, You-Gan and Thompson, M. Helen. (2022). A new mixture copula model for spatially correlated multiple variables with an environmental application. Scientific Reports. 12(1), p. Article 13867. https://doi.org/10.1038/s41598-022-18007-z
Energy-efficient virtual machine placement in data centres via an accelerated Genetic Algorithm with improved fitness computation
Hormozi, Elham, Hu, Shuwen, Ding, Zhe, Tian, Yu-Chu, Wang, You-Gan, Yu, Zu-Guo and Zhang, Weizhe. (2022). Energy-efficient virtual machine placement in data centres via an accelerated Genetic Algorithm with improved fitness computation. Energy. 252, pp. 1-15. https://doi.org/10.1016/j.energy.2022.123884
A physics-informed statistical learning framework for forecasting local suspended sediment concentrations in marine environment
Zhang, Shaotong, Wu, Ryan, Wang, You-Gan, Jeng, Dong-Sheng and Li, Guangxue. (2022). A physics-informed statistical learning framework for forecasting local suspended sediment concentrations in marine environment. Water Research. 218, pp. 1-16. https://doi.org/10.1016/j.watres.2022.118518
Robustified extreme learning machine regression with applications in outlier-blended wind-speed forecasting
Yang, Yang, Zhou, Hu, Wu, Ryan, Ding, Zhe and Wang, You-Gan. (2022). Robustified extreme learning machine regression with applications in outlier-blended wind-speed forecasting. Applied Soft Computing. 122, pp. 1-14. https://doi.org/10.1016/j.asoc.2022.108814
An opposition learning and spiral modelling based arithmetic optimization algorithm for global continuous optimization problems
Yang, Yang, Gao, Yuchao, Tan, Shuang, Zhao, Shangrui, Wu, Jinran, Gao, Shangce, Zhang, Tengfei, Tian, Yu-Chu and Wang, You-Gan. (2022). An opposition learning and spiral modelling based arithmetic optimization algorithm for global continuous optimization problems. Engineering Applications of Artificial Intelligence. 113, p. Article 104981. https://doi.org/10.1016/j.engappai.2022.104981
A modified memetic algorithm with an application to gene selection in a sheep body weight study
Miao, Maoxuan, Wu, Jinran, Cai, Fengjing and Wang, You-Gan. (2022). A modified memetic algorithm with an application to gene selection in a sheep body weight study. Animals. 12(2), p. Article 201. https://doi.org/10.3390/ani12020201
Robust penalized extreme learning machine regression with applications in wind speed forecasting
Yang, Yang, Zhou, Hu, Gao, Yuchao, Wu, Jinran, Wang, You-Gan and Fu, Liya. (2022). Robust penalized extreme learning machine regression with applications in wind speed forecasting. Neural Computing and Applications. 34(1), pp. 391-407. https://doi.org/10.1007/s00521-021-06370-3
Packing computing servers into the vessel of an underwater data center considering cooling efficiency
Hu, Zhi-Hua, Zheng, Yu-Xin and Wang, You-Gan. (2022). Packing computing servers into the vessel of an underwater data center considering cooling efficiency. Applied Energy. 314, p. Article 118986. https://doi.org/10.1016/j.apenergy.2022.118986
Multi-horizon accommodation demand forecasting : A New Zealand case study
Zhu, Min, Wu, Jinran and Wang, You-Gan. (2021). Multi-horizon accommodation demand forecasting : A New Zealand case study. International Journal of Tourism Research. 23(3), pp. 442-453. https://doi.org/10.1002/jtr.2416
Differences between diploid donors are the main contributing factor for subgenome asymmetry measured in either gene ratio or relative diversity in allopolyploids
Ye, Xueling, Hu, Haiyan, Zhou, Hong, Jiang, Yunfeng, Gao, Shang, Yuan, Zhongwei, Stiller, Jiri, Li, Chengwei, Chen, Guoyue, Liu, Yaxi, Wei, Yuming, Zheng, You-Liang, Wang, You-Gan and Liu, Chunji. (2021). Differences between diploid donors are the main contributing factor for subgenome asymmetry measured in either gene ratio or relative diversity in allopolyploids. Genome. 64(9), pp. 847-856. https://doi.org/10.1139/gen-2020-0024
A cloud endpoint coordinating CAPTCHA based on multi-view stacking ensemble
Ouyang, Zhiyou, Zhai, Xu, Wu, Jinran, Yang, Jian, Yue, Dong, Dou, Chunxia and Zhang, Tengfei. (2021). A cloud endpoint coordinating CAPTCHA based on multi-view stacking ensemble. Computers & Security. 103, p. Article 102178. https://doi.org/10.1016/j.cose.2021.102178
A hybrid rolling grey framework for short time series modelling
Cui, Zhesen, Wu, Jinran, Ding, Zhe, Duan, Qibin, Lian, Wei, Yang, Yang and Cao, Taoyun. (2021). A hybrid rolling grey framework for short time series modelling. Neural Computing and Applications. 33(17), pp. 11339-11353. https://doi.org/10.1007/s00521-020-05658-0
State consensus cooperative control for a class of nonlinear multi-agent systems with output constraints via ADP approach
Yang, Yang, Fan, Xin, Xu, Chuang, Wu, Jinran and Sun, Baohua. (2021). State consensus cooperative control for a class of nonlinear multi-agent systems with output constraints via ADP approach. Neurocomputing. 458, pp. 284-296. https://doi.org/10.1016/j.neucom.2021.05.046
Small sample bias correction or bias reduction?
Zhang, Xuemao, Paul, Sudhir and Wang, You-Gan. (2021). Small sample bias correction or bias reduction? Communications in Statistics: Simulation and Computation. 50(4), pp. 1165-1177. https://doi.org/10.1080/03610918.2019.1577976
A robust and efficient variable selection method for linear regression
Yang, Zhuoran, Fu, Liya, Wang, You-Gan, Dong, Zhixiong and Jiang, Yunlu. (2021). A robust and efficient variable selection method for linear regression. Journal of Applied Statistics. 49(14), pp. 3677-3692. https://doi.org/10.1080/02664763.2021.1962259
Robust regression with asymmetric loss functions
Fu, Liya and Wang, You-Gan. (2021). Robust regression with asymmetric loss functions. Statistical Methods in Medical Research. 30(8), pp. 1800-1815. https://doi.org/10.1177/09622802211012012
A temporal LASSO regression model for the emergency forecasting of the suspended sediment concentrations in coastal oceans: Accuracy and interpretability
Zhang, Shaotong, Wu, Ryan, Jia, Yonggang, Wang, You-Gan, Zhang, Yaqi and Duan, Qibin. (2021). A temporal LASSO regression model for the emergency forecasting of the suspended sediment concentrations in coastal oceans: Accuracy and interpretability. Engineering Applications of Artificial Intelligence. 100, pp. 1-13. https://doi.org/10.1016/j.engappai.2021.104206
Robust approach for variable selection with high dimensional longitudinal data analysis
Fu, Liya, Li, Jiaqi and Wang, You-Gan. (2021). Robust approach for variable selection with high dimensional longitudinal data analysis. Statistics in Medicine. 40(30), pp. 6835-6854. https://doi.org/10.1002/sim.9213
Efficient and doubly-robust methods for variable selection and parameter estimation in longitudinal data analysis
Fu, Liya, Yang, Zhuoran, Cai, Fengjing and Wang, You-Gan. (2021). Efficient and doubly-robust methods for variable selection and parameter estimation in longitudinal data analysis. Computational Statistics. 36(2), pp. 781-804. https://doi.org/10.1007/s00180-020-01038-3
Predictive regression with p-lags and order-q autoregressive predictors
Jayetileke, Harshanie L., Wang, You-Gan and Zhu, Min. (2021). Predictive regression with p-lags and order-q autoregressive predictors. Journal of Empirical Finance. 62, pp. 282-293. https://doi.org/10.1016/j.jempfin.2021.04.006
An efficient Gehan-type estimation for the accelerated failure time model with clustered and censored data
Fu, Liya, Yang, Zhuoran, Zhou, Yan and Wang, You-Gan. (2021). An efficient Gehan-type estimation for the accelerated failure time model with clustered and censored data. Lifetime Data Analysis. 27(4), pp. 679-709. https://doi.org/10.1007/s10985-021-09526-4
Robust estimation procedure for autoregressive models with heterogeneity
Callens, A., Wang, Y.-G., Fu, L. and Liquet, B.. (2021). Robust estimation procedure for autoregressive models with heterogeneity. Environmental Modeling and Assessment. 26(3), pp. 313-323. https://doi.org/10.1007/s10666-020-09730-w
Influential factors on Chinese airlines’ profitability and forecasting methods
Xu, Xu, McGrory, Clare Anne, Wang, You-Gan and Wu, Jinran. (2021). Influential factors on Chinese airlines’ profitability and forecasting methods. Journal of Air Transport Management. 91, p. Article 101969. https://doi.org/10.1016/j.jairtraman.2020.101969
Support vector regression with asymmetric loss for optimal electric load forecasting
Wu, Ryan, Wang, You-Gan, Tian, Yu-Chu, Burrage, Kevin and Cao, Taoyun. (2021). Support vector regression with asymmetric loss for optimal electric load forecasting. Energy. 223, p. Article 119969. https://doi.org/10.1016/j.energy.2021.119969
Inclusion of features derived from a mixture of time window sizes improved classification accuracy of machine learning algorithms for sheep grazing behaviours
Hu, Shuwen, Ingham, Aaron, Schmoelzl, Sabine, McNally, Jody, Little, Bryce, Smith, Daniel, Bishop-Hurley, Greg, Wang, You-Gan and Li, Yutao. (2020). Inclusion of features derived from a mixture of time window sizes improved classification accuracy of machine learning algorithms for sheep grazing behaviours. Computers and Electronics in Agriculture. 179, p. Article 105857. https://doi.org/10.1016/j.compag.2020.105857
Response of sediments and phosphorus to catchment characteristics and human activities under different rainfall patterns with Bayesian Networks
Jin, Guangqiu, Xu, Jing, Mo, Yuming, Tang, Hongwu, Wei, Tong, Wang, You-Gan and Li, Ling. (2020). Response of sediments and phosphorus to catchment characteristics and human activities under different rainfall patterns with Bayesian Networks. Journal of Hydrology. 584, p. Article 124695. https://doi.org/10.1016/j.jhydrol.2020.124695
An improved firefly algorithm for global continuous optimization problems
Wu, Jinran, Wang, You-Gan, Burrage, Kevin, Tian, Yu-Chu, Lawson, Brodie and Ding, Zhe. (2020). An improved firefly algorithm for global continuous optimization problems. Expert Systems with Applications. 149, p. Article 113340. https://doi.org/10.1016/j.eswa.2020.113340
Bias reduction in the two-stage method for degradation data analysis
Xu, Ancha, Wang, You-Gan, Zheng, Shurong and Cai, Fengjing. (2020). Bias reduction in the two-stage method for degradation data analysis. Applied Mathematical Modelling. 77(Part 2), pp. 1413-1424. https://doi.org/10.1016/j.apm.2019.08.024
Adaptive resilient control of a class of nonlinear systems based on event-triggered mechanism
Yang, Yang, Ge, Jingzhi, Yue, Dong, Meng, Qing and Wu, Jinran. (2020). Adaptive resilient control of a class of nonlinear systems based on event-triggered mechanism. Neurocomputing. 403, pp. 304-313. https://doi.org/10.1016/j.neucom.2020.04.061
Exact algorithms for energy-efficient virtual machine placement in data centers
Wei, Chen, Hu, Zhi-Hua and Wang, You-Gan. (2020). Exact algorithms for energy-efficient virtual machine placement in data centers. Future Generation Computer Systems. 106, pp. 77-91. https://doi.org/10.1016/j.future.2019.12.043
A working likelihood approach for robust regression
Fu, Liya, Wang, You-Gan and Cai, Fengjing. (2020). A working likelihood approach for robust regression. Statistical Methods in Medical Research. 29(12), pp. 3641-3652. https://doi.org/10.1177/0962280220936310
Maritime convection and fluctuation between Vietnam and China : A data-driven study
Hu, Zhi-Hua, Liu, Chan-Juan, Chen, Wanting, Wang, You-Gan and Wei, Chen. (2020). Maritime convection and fluctuation between Vietnam and China : A data-driven study. Research in Transportation Business and Management. 34, pp. 1-15. https://doi.org/10.1016/j.rtbm.2019.100414
Identifying barley pan-genome sequence anchors using genetic mapping and machine learning
Gao, Shang, Wu, Ryan, Stiller, Jiri, Zheng, Zhi, Zhou, Meixue, Wang, You-Gan and Liu, Chunji. (2020). Identifying barley pan-genome sequence anchors using genetic mapping and machine learning. Theoretical and Applied Genetics. 133(9), pp. 2535-2544. https://doi.org/10.1007/s00122-020-03615-y
Natural mortality estimation using tree-based ensemble learning models
Liu, Chanjuan, Zhou, Shijie, Wang, You-Gan and Hu, Zhi-Hua. (2020). Natural mortality estimation using tree-based ensemble learning models. ICES Journal of Marine Science. 77(4), pp. 1414-1426. https://doi.org/10.1093/icesjms/fsaa058
Profile-guided three-phase virtual resource management for energy efficiency of data centers
Ding, Zhe, Tian, Yu-Chu, Tang, Maolin, Li, Yuefeng, Wang, You-Gan and Zhou, Chunjie. (2020). Profile-guided three-phase virtual resource management for energy efficiency of data centers. IEEE Transactions on Industrial Electronics. 67(3), pp. 2460-2468. https://doi.org/10.1109/TIE.2019.2902786
Response of water quality to land use and sewage outfalls in different seasons
Xu, Jing, Jin, Guangqiu, Tang, Hongwu, Mo, Yuming, Wang, You-Gan and Li, Ling. (2019). Response of water quality to land use and sewage outfalls in different seasons. Science of the Total Environment. 696, p. Article 134014. https://doi.org/10.1016/j.scitotenv.2019.134014
A new hybrid model to predict the electrical load in five states of Australia
Wu, Jinran, Cui, Zhesen, Chen, Yanyan, Kong, Demeng and Wang, You-Gan. (2019). A new hybrid model to predict the electrical load in five states of Australia. Energy. 166, pp. 598-609. https://doi.org/10.1016/j.energy.2018.10.076
Sweepstakes reproductive success is absent in a New Zealand snapper (Chrysophrus auratus) population protected from fishing despite “tiny” Ne/N ratios elsewhere
Jones, Andy, Lavery, Shane D., Le Port, Agnès, Wang, You-Gan, Blower, Dean and Ovenden, Jennifer. (2019). Sweepstakes reproductive success is absent in a New Zealand snapper (Chrysophrus auratus) population protected from fishing despite “tiny” Ne/N ratios elsewhere. Molecular Ecology. 28(12), pp. 2986-2995. https://doi.org/10.1111/mec.15130
A review of the Behrens–Fisher problem and some of its analogs : Does the same size fit all?
Paul, Sudhir, Wang, You-Gan and Ullah, Insha. (2019). A review of the Behrens–Fisher problem and some of its analogs : Does the same size fit all? Revstat Statistical Journal. 17(4), p. 563–597.
Incorporating social objectives in evaluating sustainable fisheries harvest strategy
Wu, Jiafeng, Wang, Na, Hu, Zhi-Hua, Hong, Zhenjie and Wang, You-Gan. (2019). Incorporating social objectives in evaluating sustainable fisheries harvest strategy. Environmental Modeling and Assessment. 24(4), pp. 381-386. https://doi.org/10.1007/s10666-019-9651-9
Significance tests for analyzing gene expression data with small sample sizes
Ullah, Insha, Paul, Sudhir, Hong, Zhenjie and Wang, You-Gan. (2019). Significance tests for analyzing gene expression data with small sample sizes. Bioinformatics. 35(20), pp. 3996-4003. https://doi.org/10.1093/bioinformatics/btz189
Robust Estimation Using Modified Huber’s Functions With New Tails
Jiang, Yunlu, Wang, You-Gan, Fu, Liya and Wang, Xueqin. (2019). Robust Estimation Using Modified Huber’s Functions With New Tails. Technometrics. 61(1), pp. 111-122. https://doi.org/10.1080/00401706.2018.1470037
Variable selection in rank regression for analyzing longitudinal data
Fu, Liya and Wang, You-Gan. (2018). Variable selection in rank regression for analyzing longitudinal data. Statistical Methods in Medical Research. 27(8), pp. 2447-2458. https://doi.org/10.1177/0962280216681347
Assessing temporal variations of Ammonia Nitrogen concentrations and loads in the Huaihe River Basin in relation to policies on pollution source control
Xu, Jing, Jin, Guangqiu, Tang, Hongwu, Zhang, Pei, Wang, Shen, Wang, You-Gan and Li, Ling. (2018). Assessing temporal variations of Ammonia Nitrogen concentrations and loads in the Huaihe River Basin in relation to policies on pollution source control. Science of the Total Environment. 642, pp. 1386-1395. https://doi.org/10.1016/j.scitotenv.2018.05.395
Genomic prediction of breeding values using a subset of SNPs identified by three machine learning methods
Li, Bo, Zhang, Nanxi, Wang, You-Gan, George, Andrew W., Reverter, Antonio and Li, Yutao. (2018). Genomic prediction of breeding values using a subset of SNPs identified by three machine learning methods. Frontiers in Genetics. 9, p. Article 237. https://doi.org/10.3389/fgene.2018.00237
Dividend growth and equity premium predictability
Zhu, Min, Chen, Rui, Du, Ke and Wang, You-Gan. (2018). Dividend growth and equity premium predictability. International Review of Economics and Finance. 56, pp. 125-137. https://doi.org/10.1016/j.iref.2017.10.020
Robust Regression with Data-Dependent Regularization Parameters and Autoregressive Temporal Correlations
Wang, Na, Wang, You-Gan, Hu, Shuwen, Hu, Zhi-Hua, Xu, Jing, Tang, Hongwu and Jin, Guangqiu. (2018). Robust Regression with Data-Dependent Regularization Parameters and Autoregressive Temporal Correlations. Environmental Modeling and Assessment. 23(6), pp. 779-786. https://doi.org/10.1007/s10666-018-9605-7
Analysis of spatial data with a nested correlation structure
Adegboye, Oyelola, Leung, Denis and Wang, You-Gan. (2018). Analysis of spatial data with a nested correlation structure. Journal of the Royal Statistical Society Series C: Applied Statistics. 67(2), pp. 329-354. https://doi.org/10.1111/rssc.12230
Working correlation structure selection in generalized estimating equations
Fu, Liya, Hao, Yangyang and Wang, You-Gan. (2018). Working correlation structure selection in generalized estimating equations. Computational Statistics. 33(2), pp. 983-996. https://doi.org/10.1007/s00180-018-0800-4
A novel hybrid model based on extreme learning machine, k-nearest neighbor regression and wavelet denoising applied to short-term electric load forecasting
Li, Weide, Kong, Demeng and Wu, Jinran. (2017). A novel hybrid model based on extreme learning machine, k-nearest neighbor regression and wavelet denoising applied to short-term electric load forecasting. Energies. 10(5), p. Article 694. https://doi.org/10.3390/en10050694
A new hybrid model FPA-SVM considering cointegration for particular matter concentration forecasting : A case study of Kunming and Yuxi, China
Li, Weide, Kong, Demeng and Wu, Jinran. (2017). A new hybrid model FPA-SVM considering cointegration for particular matter concentration forecasting : A case study of Kunming and Yuxi, China. Computational Intelligence and Neuroscience. 2017, p. Article 2843651. https://doi.org/10.1155/2017/2843651
Selection of working correlation structure in generalized estimating equations
Wang, You-Gan and Fu, Liya. (2017). Selection of working correlation structure in generalized estimating equations. Statistics in Medicine. 36(14), pp. 2206-2219. https://doi.org/10.1002/sim.7262
Blockwise AICc for model selection in generalized linear models
Song, Guofeng, Dong, Xiaogang, Wu, Jiafeng and Wang, You-Gan. (2017). Blockwise AICc for model selection in generalized linear models. Environmental Modeling and Assessment. 22(6), pp. 523-533. https://doi.org/10.1007/s10666-017-9552-8
A comment on Koh’s “The optimal design of fallible organizations : Invariance of optimal decision threshold and uniqueness of hierarchy and polyarchy structures”
Zhu, Min, Liu, Chang and Wang, You-Gan. (2017). A comment on Koh’s “The optimal design of fallible organizations : Invariance of optimal decision threshold and uniqueness of hierarchy and polyarchy structures”. Social Choice and Welfare. 48(2), pp. 385-392. https://doi.org/10.1007/s00355-016-1009-5
The Buckley–James estimator and induced smoothing
Wang, You-Gan, Zhao, Yudong and Fu, Liya. (2016). The Buckley–James estimator and induced smoothing. Australian and New Zealand Journal of Statistics. 58(2), pp. 211-225. https://doi.org/10.1111/anzs.12155
Maximum likelihood estimation of natural mortality and quantification of temperature effects on catchability of brown tiger prawn (penaeus esculentus) in Moreton Bay (Australia) using logbook data
Kienzle, Marco, Sterling, David, Zhou, Shijie and Wang, You-Gan. (2016). Maximum likelihood estimation of natural mortality and quantification of temperature effects on catchability of brown tiger prawn (penaeus esculentus) in Moreton Bay (Australia) using logbook data. Ecological Modelling. 322, pp. 1-9. https://doi.org/10.1016/j.ecolmodel.2015.11.008
Otolith morphology of four mackerel species (Scomberomorus spp.) in Australia : Species differentiation and prediction for fisheries monitoring and assessment
Zischke, Mitchell T., Litherland, Lenore, Tilyard, Benjamin R., Stratford, Nicholas J., Jones, Ebony L. and Wang, You-Gan. (2016). Otolith morphology of four mackerel species (Scomberomorus spp.) in Australia : Species differentiation and prediction for fisheries monitoring and assessment. Fisheries Research. 176, pp. 39-47. https://doi.org/10.1016/j.fishres.2015.12.003
Efficient parameter estimation via Gaussian copulas for quantile regression with longitudinal data
Fu, Liya and Wang, You-Gan. (2016). Efficient parameter estimation via Gaussian copulas for quantile regression with longitudinal data. Journal of Multivariate Analysis. 143, pp. 492-502. https://doi.org/10.1016/j.jmva.2015.07.004
Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data
Lloyd-Jones, Luke R., Nguyen, Hien D., McLachlan, Geoffrey J., Sumpton, Wayne and Wang, You-Gan. (2016). Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data. Biometrics. 72(4), pp. 1255-1265. https://doi.org/10.1111/biom.12531
Movement and growth of the coral reef holothuroids Bohadschia argus and Thelenota ananas
Purcell, Steven W., Piddocke, Toby P., Dalton, Steven J. and Wang, You-Gan. (2016). Movement and growth of the coral reef holothuroids Bohadschia argus and Thelenota ananas. Marine Ecology Progress Series. 551, pp. 201-214. https://doi.org/10.3354/meps11720
Improved confidence intervals for the linkage disequilibrium method for estimating effective population size
Jones, A. T., Ovenden, J. R. and Wang, Y.-G.. (2016). Improved confidence intervals for the linkage disequilibrium method for estimating effective population size. Heredity. 117(4), pp. 217-223. https://doi.org/10.1038/hdy.2016.19