Euler-Lagrange coupling method is used to establish the fluid-structure interaction model for tires with different tread patterns by obtaining the grounding mark and normal contact force between tire and the road surface during tire rolling. The altering of load force, tire pressure, and water film thickness in relation to the effect on tire-road force during both constant speed and critical hydroplaning speed was analyzed. Results show that the critical hydroplaning speed and normal contact force between tire and the road surface are positively correlated with vehicle load and tire pressure and negatively correlated with water film thickness. Python language is used to develop the pre-processing plug-ins to achieve parametric modeling and rapid creation of Finite Element Analysis (FEA) model to reduce time costs, and the effectiveness of the plug-ins is verified through comparative tests.
{"title":"Model Construction and Numerical Simulation for Hydroplaning of Complex Tread Tires","authors":"Senwang Tao;Jinbiao Wang;Ruonan Dong","doi":"10.23919/CSMS.2022.0020","DOIUrl":"10.23919/CSMS.2022.0020","url":null,"abstract":"Euler-Lagrange coupling method is used to establish the fluid-structure interaction model for tires with different tread patterns by obtaining the grounding mark and normal contact force between tire and the road surface during tire rolling. The altering of load force, tire pressure, and water film thickness in relation to the effect on tire-road force during both constant speed and critical hydroplaning speed was analyzed. Results show that the critical hydroplaning speed and normal contact force between tire and the road surface are positively correlated with vehicle load and tire pressure and negatively correlated with water film thickness. Python language is used to develop the pre-processing plug-ins to achieve parametric modeling and rapid creation of Finite Element Analysis (FEA) model to reduce time costs, and the effectiveness of the plug-ins is verified through comparative tests.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10004846/10004913.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44743169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Particle swarm optimization (PSO) is a type of swarm intelligence algorithm that is frequently used to resolve specific global optimization problems due to its rapid convergence and ease of operation. However, PSO still has certain deficiencies, such as a poor trade-off between exploration and exploitation and premature convergence. Hence, this paper proposes a dual-stage hybrid learning particle swarm optimization (DHLPSO). In the algorithm, the iterative process is partitioned into two stages. The learning strategy used at each stage emphasizes exploration and exploitation, respectively. In the first stage, to increase population variety, a Manhattan distance based learning strategy is proposed. In this strategy, each particle chooses the furthest Manhattan distance particle and a better particle for learning. In the second stage, an excellent example learning strategy is adopted to perform local optimization operations on the population, in which each particle learns from the global optimal particle and a better particle. Utilizing the Gaussian mutation strategy, the algorithm's searchability in particular multimodal functions is significantly enhanced. On benchmark functions from CEC 2013, DHLPSO is evaluated alongside other PSO variants already in existence. The comparison results clearly demonstrate that, compared to other cutting-edge PSO variations, DHLPSO implements highly competitive performance in handling global optimization problems.
{"title":"Dual-Stage Hybrid Learning Particle Swarm Optimization Algorithm for Global Optimization Problems","authors":"Wei Li;Yangtao Chen;Qian Cai;Cancan Wang;Ying Huang;Soroosh Mahmoodi","doi":"10.23919/CSMS.2022.0018","DOIUrl":"10.23919/CSMS.2022.0018","url":null,"abstract":"Particle swarm optimization (PSO) is a type of swarm intelligence algorithm that is frequently used to resolve specific global optimization problems due to its rapid convergence and ease of operation. However, PSO still has certain deficiencies, such as a poor trade-off between exploration and exploitation and premature convergence. Hence, this paper proposes a dual-stage hybrid learning particle swarm optimization (DHLPSO). In the algorithm, the iterative process is partitioned into two stages. The learning strategy used at each stage emphasizes exploration and exploitation, respectively. In the first stage, to increase population variety, a Manhattan distance based learning strategy is proposed. In this strategy, each particle chooses the furthest Manhattan distance particle and a better particle for learning. In the second stage, an excellent example learning strategy is adopted to perform local optimization operations on the population, in which each particle learns from the global optimal particle and a better particle. Utilizing the Gaussian mutation strategy, the algorithm's searchability in particular multimodal functions is significantly enhanced. On benchmark functions from CEC 2013, DHLPSO is evaluated alongside other PSO variants already in existence. The comparison results clearly demonstrate that, compared to other cutting-edge PSO variations, DHLPSO implements highly competitive performance in handling global optimization problems.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10004846/10004907.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41721413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Using a brain-computer interface (BCI) rather than limbs to control multiple robots (i.e., brain-controlled multi-robots) can better assist people with disabilities in daily life than a brain-controlled single robot. For example, one person with disabilities can move by a brain-controlled wheelchair (leader robot) and simultaneously transport objects by follower robots. In this paper, we explore how to control the direction, speed, and formation of a brain-controlled multi-robot system (consisting of leader and follower robots) for the first time and propose a novel multi-robot predictive control framework (MRPCF) that can track users' control intents and ensure the safety of multiple robots. The MRPCF consists of the leader controller, follower controller, and formation planner. We build a whole brain-controlled multi-robot physical system for the first time and test the proposed system through human-in-the-loop actual experiments. The experimental results indicate that the proposed system can track users' direction, speed, and formation control intents when guaranteeing multiple robots' safety. This paper can promote the study of brain-controlled robots and multi-robot systems and provide some novel views into human-machine collaboration and integration.
{"title":"Brain-Controlled Multi-Robot at Servo-Control Level Based on Nonlinear Model Predictive Control","authors":"Zhenge Yang;Luzheng Bi;Weiming Chi;Haonan Shi;Cuntai Guan","doi":"10.23919/CSMS.2022.0019","DOIUrl":"10.23919/CSMS.2022.0019","url":null,"abstract":"Using a brain-computer interface (BCI) rather than limbs to control multiple robots (i.e., brain-controlled multi-robots) can better assist people with disabilities in daily life than a brain-controlled single robot. For example, one person with disabilities can move by a brain-controlled wheelchair (leader robot) and simultaneously transport objects by follower robots. In this paper, we explore how to control the direction, speed, and formation of a brain-controlled multi-robot system (consisting of leader and follower robots) for the first time and propose a novel multi-robot predictive control framework (MRPCF) that can track users' control intents and ensure the safety of multiple robots. The MRPCF consists of the leader controller, follower controller, and formation planner. We build a whole brain-controlled multi-robot physical system for the first time and test the proposed system through human-in-the-loop actual experiments. The experimental results indicate that the proposed system can track users' direction, speed, and formation control intents when guaranteeing multiple robots' safety. This paper can promote the study of brain-controlled robots and multi-robot systems and provide some novel views into human-machine collaboration and integration.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10004846/10004911.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46063212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Existing motion planning algorithms for multi-robot systems must be improved to address poor coordination and increase low real-time performance. This paper proposes a new distributed real-time motion planning method for a multi-robot system using Model Predictive Contouring Control (MPCC). MPCC allows separating the tracking accuracy and productivity, to improve productivity better than the traditional Model Predictive Control (MPC) which follows a time-dependent reference. In the proposed distributed MPCC, each robot exchanges the predicted paths of the other robots and generates the collision-free motion in a parallel manner. The proposed distributed MPCC method is tested in industrial operation scenarios in the robot simulation platform Gazebo. The simulation results show that the proposed distributed MPCC method realizes real-time multi-robot motion planning and performs better than three commonly-used planning methods (dynamic window approach, MPC, and prioritized planning).
{"title":"Distributed Model Predictive Contouring Control for Real-Time Multi-Robot Motion Planning","authors":"Jianbin Xin;Yaoguang Qu;Fangfang Zhang;Rudy Negenborn","doi":"10.23919/CSMS.2022.0017","DOIUrl":"10.23919/CSMS.2022.0017","url":null,"abstract":"Existing motion planning algorithms for multi-robot systems must be improved to address poor coordination and increase low real-time performance. This paper proposes a new distributed real-time motion planning method for a multi-robot system using Model Predictive Contouring Control (MPCC). MPCC allows separating the tracking accuracy and productivity, to improve productivity better than the traditional Model Predictive Control (MPC) which follows a time-dependent reference. In the proposed distributed MPCC, each robot exchanges the predicted paths of the other robots and generates the collision-free motion in a parallel manner. The proposed distributed MPCC method is tested in industrial operation scenarios in the robot simulation platform Gazebo. The simulation results show that the proposed distributed MPCC method realizes real-time multi-robot motion planning and performs better than three commonly-used planning methods (dynamic window approach, MPC, and prioritized planning).","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10004846/10004912.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46708314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An integrated observer framework based mechanical parameters identification approach for adaptive control of permanent magnet synchronous motors is proposed in this paper. Firstly, an integrated observer framework is established for mechanical parameters' estimation, which consists of an extended sliding mode observer (ESMO) and a Luenberger observer. Aiming at minimizing the influence of parameters coupling, the viscous friction and the moment of inertia are obtained by ESMO and the load torque is identified by Luenberger observer separately. After obtaining estimates of the mechanical parameters, the optimal proportional integral (PI) parameters of the speed-loop are determined according to third-order best design method. As a result, the controller can adjust the PI parameters in real time according to the parameter changes to realize the adaptive control of the system. Meanwhile, the disturbance is compensated according to the estimates. Finally, the experiments were carried out on simulation platform, and the experimental results validated the reliability of parameter identification and the efficiency of the adaptive control strategy presented in this paper.
{"title":"An Integrated Observer Framework Based Mechanical Parameters Identification for Adaptive Control of Permanent Magnet Synchronous Motor","authors":"Zhong Liao;Zhaohua Liu;Lei Chen;Mingyang Lyu;Zhengheng Wang;Dian Wang;Faming Wu;Hualiang Wei","doi":"10.23919/CSMS.2022.0022","DOIUrl":"10.23919/CSMS.2022.0022","url":null,"abstract":"An integrated observer framework based mechanical parameters identification approach for adaptive control of permanent magnet synchronous motors is proposed in this paper. Firstly, an integrated observer framework is established for mechanical parameters' estimation, which consists of an extended sliding mode observer (ESMO) and a Luenberger observer. Aiming at minimizing the influence of parameters coupling, the viscous friction and the moment of inertia are obtained by ESMO and the load torque is identified by Luenberger observer separately. After obtaining estimates of the mechanical parameters, the optimal proportional integral (PI) parameters of the speed-loop are determined according to third-order best design method. As a result, the controller can adjust the PI parameters in real time according to the parameter changes to realize the adaptive control of the system. Meanwhile, the disturbance is compensated according to the estimates. Finally, the experiments were carried out on simulation platform, and the experimental results validated the reliability of parameter identification and the efficiency of the adaptive control strategy presented in this paper.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10004846/10004914.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45037016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xuyue Wang;Wangyang Yu;Zeyuan Ding;Xiaojun Zhai;Sangeet Saha
It is important to understand the process of cancer cell metastasis and some cancer characteristics that increase disease risk. Because the occurrence of the disease is caused by many factors, and the pathogenesis process is also complicated. It is necessary to use interpretable and visual modeling methods to characterize this complex process. Machine learning techniques have demonstrated extraordinary capabilities in identifying models and extracting patterns from data to improve medical prognostic decisions. However, in most cases, it is unexplainable. Using formal methods to model can ensure the correctness and understandability of prediction decisions in a certain extent, and can well visualize the analysis process. Coloured Petri Nets (CPN) is a powerful formal model. This paper presents a modeling approach with CPN and machine learning in breast cancer, which can visualize the process of cancer cell metastasis and the impact of cell characteristics on the risk of disease. By evaluating the performance of several common machine learning algorithms, we finally choose the logistic regression algorithm to analyze the data, and integrate the obtained prediction model into the CPN model. Our method allows us to understand the relations among the cancer cell metastasis and clearly see the quantitative prediction results.
{"title":"Modeling and Analyzing of Breast Tumor Deterioration Process with Petri Nets and Logistic Regression","authors":"Xuyue Wang;Wangyang Yu;Zeyuan Ding;Xiaojun Zhai;Sangeet Saha","doi":"10.23919/CSMS.2022.0016","DOIUrl":"10.23919/CSMS.2022.0016","url":null,"abstract":"It is important to understand the process of cancer cell metastasis and some cancer characteristics that increase disease risk. Because the occurrence of the disease is caused by many factors, and the pathogenesis process is also complicated. It is necessary to use interpretable and visual modeling methods to characterize this complex process. Machine learning techniques have demonstrated extraordinary capabilities in identifying models and extracting patterns from data to improve medical prognostic decisions. However, in most cases, it is unexplainable. Using formal methods to model can ensure the correctness and understandability of prediction decisions in a certain extent, and can well visualize the analysis process. Coloured Petri Nets (CPN) is a powerful formal model. This paper presents a modeling approach with CPN and machine learning in breast cancer, which can visualize the process of cancer cell metastasis and the impact of cell characteristics on the risk of disease. By evaluating the performance of several common machine learning algorithms, we finally choose the logistic regression algorithm to analyze the data, and integrate the obtained prediction model into the CPN model. Our method allows us to understand the relations among the cancer cell metastasis and clearly see the quantitative prediction results.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/9906545/09906550.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49561829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jiali Lin;Qiaomei Li;Guangsheng Lin;Zhihui He;Dazhi Jiang;Hao Liu
Decision making is one of the common human activities. But in complex, interactive, and dynamic systems, it is extremely important to make decisions scientifically because the influence of the behavior after decision making is generally irreversible. The predictability of behavior influence is an effective way to improve the scientific decision making. As a new branch of computing, computational experiment is an emerging management method for research on complex systems. In this paper, based on particle swarm intelligence, an evolutionary adaptive system model of brand innovation in the toy industry cluster is constructed. By imitating the evolution process of the complex adaptive system, this method is helpful to analyze the impact of the management behavior brought to simulation system, predict the management behavior in real world, and finally choose the best management strategy. This simulation tried to figure out the affection of government regulation strategies and provide corresponding assessments and recommendations, which gives a new solution to assist the government to effectively judge the influence of the macro policy, as well as provides a new way of thinking of the related intelligent decision making.
{"title":"An Evolutionary Adaptive System for Prediction of Strategy Influence: A Case Study of Government Regulation Guided Brand Innovation","authors":"Jiali Lin;Qiaomei Li;Guangsheng Lin;Zhihui He;Dazhi Jiang;Hao Liu","doi":"10.23919/CSMS.2022.0011","DOIUrl":"10.23919/CSMS.2022.0011","url":null,"abstract":"Decision making is one of the common human activities. But in complex, interactive, and dynamic systems, it is extremely important to make decisions scientifically because the influence of the behavior after decision making is generally irreversible. The predictability of behavior influence is an effective way to improve the scientific decision making. As a new branch of computing, computational experiment is an emerging management method for research on complex systems. In this paper, based on particle swarm intelligence, an evolutionary adaptive system model of brand innovation in the toy industry cluster is constructed. By imitating the evolution process of the complex adaptive system, this method is helpful to analyze the impact of the management behavior brought to simulation system, predict the management behavior in real world, and finally choose the best management strategy. This simulation tried to figure out the affection of government regulation strategies and provide corresponding assessments and recommendations, which gives a new solution to assist the government to effectively judge the influence of the macro policy, as well as provides a new way of thinking of the related intelligent decision making.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/9906545/09906546.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43246331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zunjun Wang;Zhiyang Jia;Xiuxuan Tian;Jingchuan Chen;Bei Pan
Worldwide competition and diverse demand of customers pose great challenges to manufacturing enterprises. How to organize production to achieve high productivity and low cost becomes their primary task. In the mean time, the rapid pace of technology innovation has contributed to the development of new types of flexible automation. Hence, increasing manufacturing enterprises convert to multi-product and small-batch production, a manufacturing strategy that brings increased output, reduced costs, and quick response to the market. A distinctive feature of small-batch production is that the system operates mainly in the transient states. Transient states may have a significant impact on manufacturing systems. It is therefore necessary to estimate the dynamic performance of systems. As the assembly system is a typical class of production systems, in this paper, we focus on the problem of dynamic performance prediction of the assembly systems that produce small batches of different types of products. And the system is assumed to be characterized with Bernoulli reliability machines, finite buffers, and changeovers. A mathematical model based on Markovian analysis is first derived and then, the analytical formulas for performance evaluation of three-machine assembly systems are given. Moreover, a novel approach based on decomposition and aggregation is proposed to predict dynamic performance of large-scale assembly systems that consist of multiple component lines and additional processing machines located downstream of the assemble machine. The proposed approach is validated to be highly accurate and computationally efficient when compared to Monte Carlo simulation.
{"title":"Dynamic Performance Prediction in Batch-Based Assembly System with Bernoulli Machines and Changeovers","authors":"Zunjun Wang;Zhiyang Jia;Xiuxuan Tian;Jingchuan Chen;Bei Pan","doi":"10.23919/CSMS.2022.0015","DOIUrl":"10.23919/CSMS.2022.0015","url":null,"abstract":"Worldwide competition and diverse demand of customers pose great challenges to manufacturing enterprises. How to organize production to achieve high productivity and low cost becomes their primary task. In the mean time, the rapid pace of technology innovation has contributed to the development of new types of flexible automation. Hence, increasing manufacturing enterprises convert to multi-product and small-batch production, a manufacturing strategy that brings increased output, reduced costs, and quick response to the market. A distinctive feature of small-batch production is that the system operates mainly in the transient states. Transient states may have a significant impact on manufacturing systems. It is therefore necessary to estimate the dynamic performance of systems. As the assembly system is a typical class of production systems, in this paper, we focus on the problem of dynamic performance prediction of the assembly systems that produce small batches of different types of products. And the system is assumed to be characterized with Bernoulli reliability machines, finite buffers, and changeovers. A mathematical model based on Markovian analysis is first derived and then, the analytical formulas for performance evaluation of three-machine assembly systems are given. Moreover, a novel approach based on decomposition and aggregation is proposed to predict dynamic performance of large-scale assembly systems that consist of multiple component lines and additional processing machines located downstream of the assemble machine. The proposed approach is validated to be highly accurate and computationally efficient when compared to Monte Carlo simulation.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/9906545/09906886.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41913783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For sudden drinking water pollution event, reasonable opening or closing valves and hydrants in a water distribution network (WDN), which ensures the isolation and discharge of contaminant as soon as possible, is considered as an effective emergency measure. In this paper, we propose an emergency scheduling algorithm based on evolutionary reinforcement learning (ERL), which can train a good scheduling policy by the combination of the evolutionary computation (EC) and reinforcement learning (RL). Then, the optimal scheduling policy can guide the operation of valves and hydrants in real time based on sensor information, and protect people from the risk of contaminated water. Experiments verify our algorithm can achieve good results and effectively reduce the impact of pollution events.
{"title":"Dynamic Scheduling Algorithm Based on Evolutionary Reinforcement Learning for Sudden Contaminant Events Under Uncertain Environment","authors":"Chengyu Hu;Rui Qiao;Zhe Zhang;Xuesong Yan;Ming Li","doi":"10.23919/CSMS.2022.0014","DOIUrl":"10.23919/CSMS.2022.0014","url":null,"abstract":"For sudden drinking water pollution event, reasonable opening or closing valves and hydrants in a water distribution network (WDN), which ensures the isolation and discharge of contaminant as soon as possible, is considered as an effective emergency measure. In this paper, we propose an emergency scheduling algorithm based on evolutionary reinforcement learning (ERL), which can train a good scheduling policy by the combination of the evolutionary computation (EC) and reinforcement learning (RL). Then, the optimal scheduling policy can guide the operation of valves and hydrants in real time based on sensor information, and protect people from the risk of contaminated water. Experiments verify our algorithm can achieve good results and effectively reduce the impact of pollution events.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/9906545/09906547.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46573271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vehicle scheduling plays a profound role in public transportation. Especially, stochastic vehicle scheduling may lead to more robust schedules. To solve the stochastic vehicle scheduling problem (SVSP), a discrete artificial bee colony algorithm (DABC) is proposed. Due to the discreteness of SVSP, in DABC, a new encoding and decoding scheme with small dimensions is designed, whilst an initialization rule and three neighborhood search schemes (i.e., discrete scheme, heuristic scheme, and learnable scheme) are devised individually. A series of experiments demonstrate that the proposed DABC with any neighborhood search scheme is able to produce better schedules than the benchmark results and DABC with the heuristic scheme performs the best among the three proposed search schemes.
{"title":"A Discrete Artificial Bee Colony Algorithm for Stochastic Vehicle Scheduling","authors":"Yuanyuan Li;Yindong Shen;Jingpeng Li","doi":"10.23919/CSMS.2022.0012","DOIUrl":"10.23919/CSMS.2022.0012","url":null,"abstract":"Vehicle scheduling plays a profound role in public transportation. Especially, stochastic vehicle scheduling may lead to more robust schedules. To solve the stochastic vehicle scheduling problem (SVSP), a discrete artificial bee colony algorithm (DABC) is proposed. Due to the discreteness of SVSP, in DABC, a new encoding and decoding scheme with small dimensions is designed, whilst an initialization rule and three neighborhood search schemes (i.e., discrete scheme, heuristic scheme, and learnable scheme) are devised individually. A series of experiments demonstrate that the proposed DABC with any neighborhood search scheme is able to produce better schedules than the benchmark results and DABC with the heuristic scheme performs the best among the three proposed search schemes.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/9906545/09906549.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48402559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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