Search

Your search keyword '"Cai, Yiqiao"' showing total 214 results
Start Over Author "Cai, Yiqiao"
214 results on '"Cai, Yiqiao"'

10. Multi-Task Multi-Objective Evolutionary Search Based on Deep Reinforcement Learning for Multi-Objective Vehicle Routing Problems with Time Windows.

Author

Deng, Jianjun, Wang, Junjie, Wang, Xiaojun, Cai, Yiqiao, and Liu, Peizhong

Subjects
DEEP reinforcement learning, REINFORCEMENT learning, VEHICLE routing problem, TRAVEL time (Traffic engineering), COMBINATORIAL optimization
Abstract

The vehicle routing problem with time windows (VRPTW) is a widely studied combinatorial optimization problem in supply chains and logistics within the last decade. Recent research has explored the potential of deep reinforcement learning (DRL) as a promising solution for the VRPTW. However, the challenge of addressing the VRPTW with many conflicting objectives (MOVRPTW) still remains for DRL. The MOVRPTW considers five conflicting objectives simultaneously: minimizing the number of vehicles required, the total travel distance, the travel time of the longest route, the total waiting time for early arrivals, and the total delay time for late arrivals. To tackle the MOVRPTW, this study introduces the MTMO/DRP-AT, a multi-task multi-objective evolutionary search algorithm, by making full use of both DRL and the multitasking mechanism. In the MTMO/DRL-AT, a two-objective MOVRPTW is constructed as an assisted task, with the objectives being to minimize the total travel distance and the travel time of the longest route. Both the main task and the assisted task are simultaneously solved in a multitasking scenario. Each task is decomposed into scalar optimization subproblems, which are then solved by an attention model trained using DRL. The outputs of these trained models serve as the initial solutions for the MTMO/DRL-AT. Subsequently, the proposed algorithm incorporates knowledge transfer and multiple local search operators to further enhance the quality of these promising solutions. The simulation results on real-world benchmarks highlight the superior performance of the MTMO/DRL-AT compared to several other algorithms in solving the MOVRPTW. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

11. Operational-Behavior Auditing in Cloud Storage

Author

Chen, Zhaoyi, Tian, Hui, Lu, Jing, Cai, Yiqiao, Wang, Tian, Chen, Yonghong, Akan, Ozgur, Series editor, Bellavista, Paolo, Series editor, Cao, Jiannong, Series editor, Coulson, Geoffrey, Series editor, Dressler, Falko, Series editor, Ferrari, Domenico, Series editor, Gerla, Mario, Series editor, Kobayashi, Hisashi, Series editor, Palazzo, Sergio, Series editor, Sahni, Sartaj, Series editor, Shen, Xuemin Sherman, Series editor, Stan, Mircea, Series editor, Xiaohua, Jia, Series editor, Zomaya, Albert Y., Series editor, Wan, Jiafu, editor, Lin, Kai, editor, Zeng, Delu, editor, Li, Jin, editor, Xiang, Yang, editor, Liao, Xiaofeng, editor, Huang, Jiwu, editor, and Liu, Zheli, editor

Published
2018
Full Text
View/download PDF

12. Improving the Security of a Public Auditing Scheme for Multiple-Replica Dynamic Data

Author

Lu, Jing, Chen, Yuxiang, Tian, Hui, Chen, Yonghong, Wang, Tian, Cai, Yiqiao, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Hu, Ting, editor, Wang, Feng, editor, Li, Hongwei, editor, and Wang, Qian, editor

Published
2018
Full Text
View/download PDF

13. Differential Evolution with Proximity-Based Replacement Strategy and Elite Archive Mechanism for Global Optimization

Author

Shao, Chi, Cai, Yiqiao, Luo, Wei, Li, Jing, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Vaidya, Jaideep, editor, and Li, Jin, editor

Published
2018
Full Text
View/download PDF

14. Enhanced Differential Evolution with Self-organizing Map for Numerical Optimization

Author

Wu, Duanwei, Cai, Yiqiao, Li, Jing, Luo, Wei, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Vaidya, Jaideep, editor, and Li, Jin, editor

Published
2018
Full Text
View/download PDF

24. Improving the Localization Probability and Decreasing Communication Cost for Mobile Users

Author

Wang, Wenhua, Wang, Tian, Bhuiyan, Md. Zakirul Alam, Cai, Yiqiao, Tian, Hui, Chen, Yonghong, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Wang, Guojun, editor, Ray, Indrakshi, editor, Alcaraz Calero, Jose M., editor, and Thampi, Sabu M., editor

Published
2016
Full Text
View/download PDF

27. Detecting Targets Based on a Realistic Detection and Decision Model in Wireless Sensor Networks

Author

Wang, Tian, Peng, Zhen, Liang, Junbin, Cai, Yiqiao, Chen, Yonghong, Tian, Hui, Zhong, Bineng, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Xu, Kuai, editor, and Zhu, Haojin, editor

Published
2015
Full Text
View/download PDF

34. Multi-start Stochastic Competitive Hopfield Neural Network for p-Median Problem

Author

Cai, Yiqiao, Wang, Jiahai, Yin, Jian, Li, Caiwei, Zhang, Yunong, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Yu, Wen, editor, He, Haibo, editor, and Zhang, Nian, editor

Published
2009
Full Text
View/download PDF

40. Improving differential evolution with a new selection method of parents for mutation

Author

Cai, Yiqiao, Chen, Yonghong, Wang, Tian, and Tian, Hui

Abstract

In differential evolution (DE), the salient feature lies in its mutationmechanismthat distinguishes it from other evolutionary algorithms. Generally, for most of the DE algorithms, the parents for mutation are randomly chosen from the current population. Hence, all vectors of population have the equal chance to be selected as parents without selective pressure at all. In this way, the information of population cannot be fully exploited to guide the search. To alleviate this drawback and improve the performance of DE, we present a new selection method of parents that attempts to choose individuals for mutation by utilizing the population information effectively. The proposed method is referred as fitnessand- position based selection (FPS), which combines the fitness and position information of population simultaneously for selecting parents in mutation of DE. In order to evaluate the effectiveness of FPS, FPS is applied to the original DE algorithms, as well as several DE variants, for numerical optimization. Experimental results on a suite of benchmark functions indicate that FPS is able to enhance the performance of most DE algorithms studied. Compared with other selection methods, FPS is also shown to be more effective to utilize information of population for guiding the search of DE.

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results