Complexity science is an interdisciplinary scientific field that analyzes systems as holistic entities consisting of characteristics beyond the sum of a system's individual elements. This paper presents current research across the literature promoting cyber security as a complex adaptive system. We introduce complex systems concepts and fields of study, and deliver historical context, main themes, and current research relevant to cyber operations. Examples of cyber operations research leveraging agent-based modeling demonstrate the power of computational modeling grounded in complex systems principles. We discuss cyber operations as a scientific field, define current shortfalls for scientific rigor, and provide examples of how a complexity science foundation can further research and practice across a variety of cyber-based efforts. We propose standard definitions applicable to complex systems for cyber professionals and conclude with recommendations for future cyber operations research.
{"title":"Complexity Science and Cyber Operations: A Literature Survey","authors":"Briant Becote;Bhaskar Prasad Rimal","doi":"10.23919/CSMS.2023.0018","DOIUrl":"https://doi.org/10.23919/CSMS.2023.0018","url":null,"abstract":"Complexity science is an interdisciplinary scientific field that analyzes systems as holistic entities consisting of characteristics beyond the sum of a system's individual elements. This paper presents current research across the literature promoting cyber security as a complex adaptive system. We introduce complex systems concepts and fields of study, and deliver historical context, main themes, and current research relevant to cyber operations. Examples of cyber operations research leveraging agent-based modeling demonstrate the power of computational modeling grounded in complex systems principles. We discuss cyber operations as a scientific field, define current shortfalls for scientific rigor, and provide examples of how a complexity science foundation can further research and practice across a variety of cyber-based efforts. We propose standard definitions applicable to complex systems for cyber professionals and conclude with recommendations for future cyber operations research.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 4","pages":"327-342"},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10347383","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138550268","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}
The distributed hybrid flow shop scheduling problem (DHFSP), which integrates distributed manufacturing models with parallel machines, has gained significant attention. However, in actual scheduling, some adjacent machines do not have buffers between them, resulting in blocking. This paper focuses on addressing the DHFSP with blocking constraints (DBHFSP) based on the actual production conditions. To solve DBHFSP, we construct a mixed integer linear programming (MILP) model for DBHFSP and validate its correctness using the Gurobi solver. Then, an advanced iterated greedy (AIG) algorithm is designed to minimize the makespan, in which we modify the Nawaz, Enscore, and Ham (NEH) heuristic to solve blocking constraints. To balance the global and local search capabilities of AIG, two effective inter-factory neighborhood search strategies and a swap-based local search strategy are designed. Additionally, each factory is mutually independent, and the movement within one factory does not affect the others. In view of this, we specifically designed a memory-based decoding method for insertion operations to reduce the computation time of the objective. Finally, two shaking strategies are incorporated into the algorithm to mitigate premature convergence. Five advanced algorithms are used to conduct comparative experiments with AIG on 80 test instances, and experimental results illustrate that the makespan and the relative percentage increase (RPI) obtained by AIG are 1.0% and 86.1% respectively, better than the comparative algorithms.
{"title":"Intelligent Optimization Under Multiple Factories: Hybrid Flow Shop Scheduling Problem with Blocking Constraints Using an Advanced Iterated Greedy Algorithm","authors":"Yong Wang;Yuting Wang;Yuyan Han;Junqing Li;Kaizhou Gao;Yusuke Nojima","doi":"10.23919/CSMS.2023.0016","DOIUrl":"https://doi.org/10.23919/CSMS.2023.0016","url":null,"abstract":"The distributed hybrid flow shop scheduling problem (DHFSP), which integrates distributed manufacturing models with parallel machines, has gained significant attention. However, in actual scheduling, some adjacent machines do not have buffers between them, resulting in blocking. This paper focuses on addressing the DHFSP with blocking constraints (DBHFSP) based on the actual production conditions. To solve DBHFSP, we construct a mixed integer linear programming (MILP) model for DBHFSP and validate its correctness using the Gurobi solver. Then, an advanced iterated greedy (AIG) algorithm is designed to minimize the makespan, in which we modify the Nawaz, Enscore, and Ham (NEH) heuristic to solve blocking constraints. To balance the global and local search capabilities of AIG, two effective inter-factory neighborhood search strategies and a swap-based local search strategy are designed. Additionally, each factory is mutually independent, and the movement within one factory does not affect the others. In view of this, we specifically designed a memory-based decoding method for insertion operations to reduce the computation time of the objective. Finally, two shaking strategies are incorporated into the algorithm to mitigate premature convergence. Five advanced algorithms are used to conduct comparative experiments with AIG on 80 test instances, and experimental results illustrate that the makespan and the relative percentage increase (RPI) obtained by AIG are 1.0% and 86.1% respectively, better than the comparative algorithms.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 4","pages":"282-306"},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10347379","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138550352","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}
Cooperative spatial exploration in initially unknown surroundings is a common embodied task in various applications and requires satisfactory coordination among the agents. Unlike many other research questions, there is a lack of simulation platforms for the cooperative exploration problem to perform and statistically evaluate different methods before they are deployed in practical scenarios. To this end, this paper designs a simulation framework to run different models, which features efficient event scheduling and data sharing. On top of such a framework, we propose and implement two different cooperative exploration strategies, i.e., the synchronous and asynchronous ones. While the coordination in the former approach is conducted after gathering the perceptive information from all agents in each round, the latter enables an ad-hoc coordination. Accordingly, they exploit different principles for assigning target points for the agents. Extensive experiments on different types of environments and settings not only validate the scheduling efficiency of our simulation engine, but also demonstrate the respective advantages of the two strategies on different metrics.
{"title":"Modeling and Simulation of Cooperative Exploration Strategies in Unknown Environments","authors":"Yong Peng;Yue Hu;Chuan Ai","doi":"10.23919/CSMS.2023.0014","DOIUrl":"https://doi.org/10.23919/CSMS.2023.0014","url":null,"abstract":"Cooperative spatial exploration in initially unknown surroundings is a common embodied task in various applications and requires satisfactory coordination among the agents. Unlike many other research questions, there is a lack of simulation platforms for the cooperative exploration problem to perform and statistically evaluate different methods before they are deployed in practical scenarios. To this end, this paper designs a simulation framework to run different models, which features efficient event scheduling and data sharing. On top of such a framework, we propose and implement two different cooperative exploration strategies, i.e., the synchronous and asynchronous ones. While the coordination in the former approach is conducted after gathering the perceptive information from all agents in each round, the latter enables an ad-hoc coordination. Accordingly, they exploit different principles for assigning target points for the agents. Extensive experiments on different types of environments and settings not only validate the scheduling efficiency of our simulation engine, but also demonstrate the respective advantages of the two strategies on different metrics.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 4","pages":"343-356"},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10347385","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138550270","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}
Harmony Search (HS) algorithm is highly effective in solving a wide range of real-world engineering optimization problems. However, it still has the problems such as being prone to local optima, low optimization accuracy, and low search efficiency. To address the limitations of the HS algorithm, a novel approach called the Dual-Memory Dynamic Search Harmony Search (DMDS-HS) algorithm is introduced. The main innovations of this algorithm are as follows: Firstly, a dual-memory structure is introduced to rank and hierarchically organize the harmonies in the harmony memory, creating an effective and selectable trust region to reduce approach blind searching. Furthermore, the trust region is dynamically adjusted to improve the convergence of the algorithm while maintaining its global search capability. Secondly, to boost the algorithm's convergence speed, a phased dynamic convergence domain concept is introduced to strategically devise a global random search strategy. Lastly, the algorithm constructs an adaptive parameter adjustment strategy to adjust the usage probability of the algorithm's search strategies, which aim to rationalize the abilities of exploration and exploitation of the algorithm. The results tested on the Computational Experiment Competition on 2017 (CEC2017) test function set show that DMDS-HS outperforms the other nine HS algorithms and the other four state-of-the-art algorithms in terms of diversity, freedom from local optima, and solution accuracy. In addition, applying DMDS-HS to data clustering problems, the results show that it exhibits clustering performance that exceeds the other seven classical clustering algorithms, which verifies the effectiveness and reliability of DMDS-HS in solving complex data clustering problems.
{"title":"Harmony Search Algorithm Based on Dual-Memory Dynamic Search and Its Application on Data Clustering","authors":"Jinglin Wang;Haibin Ouyang;Zhiyu Zhou;Steven Li","doi":"10.23919/CSMS.2023.0019","DOIUrl":"https://doi.org/10.23919/CSMS.2023.0019","url":null,"abstract":"Harmony Search (HS) algorithm is highly effective in solving a wide range of real-world engineering optimization problems. However, it still has the problems such as being prone to local optima, low optimization accuracy, and low search efficiency. To address the limitations of the HS algorithm, a novel approach called the Dual-Memory Dynamic Search Harmony Search (DMDS-HS) algorithm is introduced. The main innovations of this algorithm are as follows: Firstly, a dual-memory structure is introduced to rank and hierarchically organize the harmonies in the harmony memory, creating an effective and selectable trust region to reduce approach blind searching. Furthermore, the trust region is dynamically adjusted to improve the convergence of the algorithm while maintaining its global search capability. Secondly, to boost the algorithm's convergence speed, a phased dynamic convergence domain concept is introduced to strategically devise a global random search strategy. Lastly, the algorithm constructs an adaptive parameter adjustment strategy to adjust the usage probability of the algorithm's search strategies, which aim to rationalize the abilities of exploration and exploitation of the algorithm. The results tested on the Computational Experiment Competition on 2017 (CEC2017) test function set show that DMDS-HS outperforms the other nine HS algorithms and the other four state-of-the-art algorithms in terms of diversity, freedom from local optima, and solution accuracy. In addition, applying DMDS-HS to data clustering problems, the results show that it exhibits clustering performance that exceeds the other seven classical clustering algorithms, which verifies the effectiveness and reliability of DMDS-HS in solving complex data clustering problems.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 4","pages":"261-281"},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10347380","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138550223","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}
Nowadays, autonomous robots are expected to accomplish more complex tasks and operate in an open-world environment with uncertainties. Developing software for such robots involves the design of task planning paradigms and the implementation of robotic software architectures, making software development rather tricky and time-consuming. In recent decades, component-based software development approaches have been increasingly adopted in robotics to improve software development efficiency by reusing data and controlling flows between components. However, few works have tackled the more critical issue of reusing complex high-level task planning paradigms and robotic software architectures. To make up for the limitation, this paper first identifies the mainstream task planning paradigms and proposes a set of novel patterns for interaction pipelines between the robotic functions of sensing, planning, and acting. Then this paper presents a novel Behavior Tree (BT) based development framework Structural-BT, which provides a set of reusable BT structures that implement abstract interaction pipelines while maintaining interfaces for task-specific customization. The Structural-BT framework supports the modular design of structure functionalities and allows easy extensibility of the inner planning flows between BT components. With the Structural-BT framework, software engineers can develop robotic software by flexibly composing BT structures to formulate the skeleton software architecture and implement task-specific algorithms when necessary. In the experiment, this paper develops robotic software for diverse task scenarios and selects the baseline approaches of Robot Operating System (ROS) and classical BT development frameworks for comparison. By quantitatively measuring the reuse frequencies and ratios of BT structures, the Structural-BT framework has been shown to be more efficient than the baseline approaches for robotic software development.
{"title":"Towards Efficient Robotic Software Development by Reusing Behavior Tree Structures for Task Planning Paradigms","authors":"Shuo Yang;Qi Zhang","doi":"10.23919/CSMS.2023.0017","DOIUrl":"https://doi.org/10.23919/CSMS.2023.0017","url":null,"abstract":"Nowadays, autonomous robots are expected to accomplish more complex tasks and operate in an open-world environment with uncertainties. Developing software for such robots involves the design of task planning paradigms and the implementation of robotic software architectures, making software development rather tricky and time-consuming. In recent decades, component-based software development approaches have been increasingly adopted in robotics to improve software development efficiency by reusing data and controlling flows between components. However, few works have tackled the more critical issue of reusing complex high-level task planning paradigms and robotic software architectures. To make up for the limitation, this paper first identifies the mainstream task planning paradigms and proposes a set of novel patterns for interaction pipelines between the robotic functions of sensing, planning, and acting. Then this paper presents a novel Behavior Tree (BT) based development framework Structural-BT, which provides a set of reusable BT structures that implement abstract interaction pipelines while maintaining interfaces for task-specific customization. The Structural-BT framework supports the modular design of structure functionalities and allows easy extensibility of the inner planning flows between BT components. With the Structural-BT framework, software engineers can develop robotic software by flexibly composing BT structures to formulate the skeleton software architecture and implement task-specific algorithms when necessary. In the experiment, this paper develops robotic software for diverse task scenarios and selects the baseline approaches of Robot Operating System (ROS) and classical BT development frameworks for comparison. By quantitatively measuring the reuse frequencies and ratios of BT structures, the Structural-BT framework has been shown to be more efficient than the baseline approaches for robotic software development.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 4","pages":"357-380"},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10347382","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138550269","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) algorithms have been successfully used for various complex optimization problems. However, balancing the diversity and convergence is still a problem that requires continuous research. Therefore, an evolutionary experience-driven particle swarm optimization with dynamic searching (EEDSPSO) is proposed in this paper. For purpose of extracting the effective information during population evolution, an adaptive framework of evolutionary experience is presented. And based on this framework, an experience-based neighborhood topology adjustment (ENT) is used to control the size of the neighborhood range, thereby effectively keeping the diversity of population. Meanwhile, experience-based elite archive mechanism (EEA) adjusts the weights of elite particles in the late evolutionary stage, thus enhancing the convergence of the algorithm. In addition, a Gaussian crisscross learning strategy (GCL) adopts crosslearning method to further balance the diversity and convergence. Finally, extensive experiments use the CEC2013 and CEC2017. The experiment results show that EEDSPSO outperforms current excellent PSO variants.
{"title":"Evolutionary Experience-Driven Particle Swarm Optimization with Dynamic Searching","authors":"Wei Li;Jianghui Jing;Yangtao Chen;Xunjun Chen;Ata Jahangir Moshayedi","doi":"10.23919/CSMS.2023.0015","DOIUrl":"https://doi.org/10.23919/CSMS.2023.0015","url":null,"abstract":"Particle swarm optimization (PSO) algorithms have been successfully used for various complex optimization problems. However, balancing the diversity and convergence is still a problem that requires continuous research. Therefore, an evolutionary experience-driven particle swarm optimization with dynamic searching (EEDSPSO) is proposed in this paper. For purpose of extracting the effective information during population evolution, an adaptive framework of evolutionary experience is presented. And based on this framework, an experience-based neighborhood topology adjustment (ENT) is used to control the size of the neighborhood range, thereby effectively keeping the diversity of population. Meanwhile, experience-based elite archive mechanism (EEA) adjusts the weights of elite particles in the late evolutionary stage, thus enhancing the convergence of the algorithm. In addition, a Gaussian crisscross learning strategy (GCL) adopts crosslearning method to further balance the diversity and convergence. Finally, extensive experiments use the CEC2013 and CEC2017. The experiment results show that EEDSPSO outperforms current excellent PSO variants.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 4","pages":"307-326"},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10347384","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138550357","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}
Industrial robots are currently applied for ship sub-assembly welding to replace welding workers because of the intelligent production and cost savings. In order to improve the efficiency of the robot system, a digital twin system of welding path planning for the arc welding robot in ship sub-assembly welding is proposed in this manuscript to achieve autonomous planning and generation of the welding path. First, a five-dimensional digital twin model of the dual arc welding robot system is constructed. Then, the system kinematics analysis and calibration are studied for communication realization between the virtual and the actual system. Besides, a topology consisting of three bounding volume hierarchies (BVH) trees is proposed to construct digital twin virtual entities in this system. Based on this topology, algorithms for welding seam extraction and collision detection are presented. Finally, the genetic algorithm and the RRT-Connect algorithm combined with region partitioning (RRT-Connect-RP) are applied for the welding sequence global planning and local jump path planning, respectively. The digital twin system and its path planning application are tested in the actual application scenario. The results show that the system can not only simulate the actual welding operation of the arc welding robot but also realize path planning and real-time control of the robot.
{"title":"Digital Twin Implementation of Autonomous Planning Arc Welding Robot System","authors":"Xuewu Wang;Yi Hua;Jin Gao;Zongjie Lin;Rui Yu","doi":"10.23919/CSMS.2023.0013","DOIUrl":"10.23919/CSMS.2023.0013","url":null,"abstract":"Industrial robots are currently applied for ship sub-assembly welding to replace welding workers because of the intelligent production and cost savings. In order to improve the efficiency of the robot system, a digital twin system of welding path planning for the arc welding robot in ship sub-assembly welding is proposed in this manuscript to achieve autonomous planning and generation of the welding path. First, a five-dimensional digital twin model of the dual arc welding robot system is constructed. Then, the system kinematics analysis and calibration are studied for communication realization between the virtual and the actual system. Besides, a topology consisting of three bounding volume hierarchies (BVH) trees is proposed to construct digital twin virtual entities in this system. Based on this topology, algorithms for welding seam extraction and collision detection are presented. Finally, the genetic algorithm and the RRT-Connect algorithm combined with region partitioning (RRT-Connect-RP) are applied for the welding sequence global planning and local jump path planning, respectively. The digital twin system and its path planning application are tested in the actual application scenario. The results show that the system can not only simulate the actual welding operation of the arc welding robot but also realize path planning and real-time control of the robot.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 3","pages":"236-251"},"PeriodicalIF":0.0,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10206014/10206020.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47228305","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}
Yinan Guo;Yao Huang;Shirong Ge;Yizhe Zhang;Ersong Jiang;Bin Cheng;Shengxiang Yang
Trackless rubber-tyerd vehicles are the core equipment for auxiliary transportation in inclined-shaft coal mines, and the rationality of their routes plays the direct impact on operation safety and energy consumption. Rich studies have been done on scheduling rubber-tyerd vehicles driven by diesel oil, however, less works are for electric trackless rubber-tyred vehicles. Furthermore, energy consumption of vehicles gives no consideration on the impact of complex roadway and traffic rules on driving, especially the limited cruising ability of electric trackless rubber-tyred vehichles (TRVs). To address this issue, an energy consumption model of an electric trackless rubber-tyred vehicle is formulated, in which the effects from total mass, speed profiles, slope of roadways, and energy management mode are all considered. Following that, a low-carbon routing model of electric trackless rubber-tyred vehicles is built to minimize the total energy consumption under the constraint of vehicle avoidance, allowable load, and endurance power. As a problem-solver, an improved artificial bee colony algorithm is put forward. More especially, an adaptive neighborhood search is designed to guide employed bees to select appropriate operator in a specific space. In order to assign onlookers to some promising food sources reasonably, their selection probability is adaptively adjusted. For a stagnant food source, a knowledge-driven initialization is developed to generate a feasible substitute. The experimental results on four real-world instances indicate that improved artificial bee colony algorithm (IABC) outperforms other comparative algorithms and the special designs in its three phases effectively avoid premature convergence and speed up convergence.
{"title":"Low-Carbon Routing Based on Improved Artificial Bee Colony Algorithm for Electric Trackless Rubber-Tyred Vehicles","authors":"Yinan Guo;Yao Huang;Shirong Ge;Yizhe Zhang;Ersong Jiang;Bin Cheng;Shengxiang Yang","doi":"10.23919/CSMS.2023.0011","DOIUrl":"10.23919/CSMS.2023.0011","url":null,"abstract":"Trackless rubber-tyerd vehicles are the core equipment for auxiliary transportation in inclined-shaft coal mines, and the rationality of their routes plays the direct impact on operation safety and energy consumption. Rich studies have been done on scheduling rubber-tyerd vehicles driven by diesel oil, however, less works are for electric trackless rubber-tyred vehicles. Furthermore, energy consumption of vehicles gives no consideration on the impact of complex roadway and traffic rules on driving, especially the limited cruising ability of electric trackless rubber-tyred vehichles (TRVs). To address this issue, an energy consumption model of an electric trackless rubber-tyred vehicle is formulated, in which the effects from total mass, speed profiles, slope of roadways, and energy management mode are all considered. Following that, a low-carbon routing model of electric trackless rubber-tyred vehicles is built to minimize the total energy consumption under the constraint of vehicle avoidance, allowable load, and endurance power. As a problem-solver, an improved artificial bee colony algorithm is put forward. More especially, an adaptive neighborhood search is designed to guide employed bees to select appropriate operator in a specific space. In order to assign onlookers to some promising food sources reasonably, their selection probability is adaptively adjusted. For a stagnant food source, a knowledge-driven initialization is developed to generate a feasible substitute. The experimental results on four real-world instances indicate that improved artificial bee colony algorithm (IABC) outperforms other comparative algorithms and the special designs in its three phases effectively avoid premature convergence and speed up convergence.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 3","pages":"169-190"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10206014/10206015.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41492582","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}
Model-based methods require an accurate dynamic model to design the controller. However, the hydraulic parameters of nonlinear systems, complex friction, or actuator dynamics make it challenging to obtain accurate models. In this case, using the input-output data of the system to learn a dynamic model is an alternative approach. Therefore, we propose a dynamic model based on the Gaussian process (GP) to construct systems with control constraints. Since GP provides a measure of model confidence, it can deal with uncertainty. Unfortunately, most GP-based literature considers model uncertainty but does not consider the effect of constraints on inputs in closed-loop systems. An auxiliary system is developed to deal with the influence of the saturation constraints of input. Meanwhile, we relax the nonsingular assumption of the control coefficients to construct the controller. Some numerical results verify the rationality of the proposed approach and compare it with similar methods.
{"title":"Gaussian Process Based Modeling and Control of Affine Systems with Control Saturation Constraints","authors":"Shulong Zhao;Qipeng Wang;Jiayi Zheng;Xiangke Wang","doi":"10.23919/CSMS.2023.0009","DOIUrl":"10.23919/CSMS.2023.0009","url":null,"abstract":"Model-based methods require an accurate dynamic model to design the controller. However, the hydraulic parameters of nonlinear systems, complex friction, or actuator dynamics make it challenging to obtain accurate models. In this case, using the input-output data of the system to learn a dynamic model is an alternative approach. Therefore, we propose a dynamic model based on the Gaussian process (GP) to construct systems with control constraints. Since GP provides a measure of model confidence, it can deal with uncertainty. Unfortunately, most GP-based literature considers model uncertainty but does not consider the effect of constraints on inputs in closed-loop systems. An auxiliary system is developed to deal with the influence of the saturation constraints of input. Meanwhile, we relax the nonsingular assumption of the control coefficients to construct the controller. Some numerical results verify the rationality of the proposed approach and compare it with similar methods.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 3","pages":"252-260"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10206014/10206018.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42538354","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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