QL-ADIFA : Hybrid optimization using Q-learning and an adaptive logarithmic spiral-levy firefly algorithm

Journal article

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
AuthorsTan, Shuang, Zhao, Shangrui and Wu, Jinran
Abstract

Optimization problems are ubiquitous in engineering and scientific research, with a large number of such problems requiring resolution. Meta-heuristics offer a promising approach to solving optimization problems. The firefly algorithm (FA) is a swarm intelligence meta-heuristic that emulates the flickering patterns and behaviour of fireflies. Although FA has been significantly enhanced to improve its performance, it still exhibits certain deficiencies. To overcome these limitations, this study presents the Q-learning based on the adaptive logarithmic spiral-Levy flight firefly algorithm (QL-ADIFA). The Q-learning technique empowers the improved firefly algorithm to leverage the firefly's environmental awareness and memory while in flight, allowing further refinement of the enhanced firefly. Numerical experiments demonstrate that QL-ADIFA outperforms existing methods on 15 benchmark optimization functions and twelve engineering problems: cantilever arm design, pressure vessel design, three-bar truss design problem, and 9 constrained optimization problems in CEC2020.

KeywordsQ-learning algorithm; firefly algorithm; meta-heuristics; optimization problems
Year2023
JournalMathematical Biosciences and Engineering
Journal citation20 (8), pp. 13542-13561
PublisherAmerican Institute of Mathematical Sciences (AIMS)
ISSN1547-1063
Digital Object Identifier (DOI)https://doi.org/10.3934/mbe.2023604
PubMed ID37679101
Scopus EID2-s2.0-85163545008
Open accessPublished as ‘gold’ (paid) open access
Page range13542-13561
Publisher's version
License
File Access Level
Open
Output statusPublished
Publication dates
Online14 Jun 2023
Publication process dates
Accepted01 Jun 2023
Deposited29 Nov 2023
Permalink -

https://acuresearchbank.acu.edu.au/item/8zzx3/ql-adifa-hybrid-optimization-using-q-learning-and-an-adaptive-logarithmic-spiral-levy-firefly-algorithm

Log in to edit

Download files

Publisher's version
OA_Tan_2023_QL_ADIFA_Hybrid_optimization_using_Q.pdf
License: CC BY 4.0
File access level: Open

  • 26
    total views
  • 12
    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
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. 53(9), pp. 5658-5669. https://doi.org/10.1109/TSMC.2023.3272880
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
QQLMPA : A quasi-opposition learning and Q-learning based marine predators algorithm
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
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
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
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
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
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
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
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
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
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