复杂系统建模与仿真(英文)最新文献_第4页

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复杂系统建模与仿真(英文) Pub Date : 2023-12-07 DOI: 10.23919/CSMS.2023.10347378

引用次数: 0

Evolutionary Experience-Driven Particle Swarm Optimization with Dynamic Searching 经验驱动的进化粒子群优化与动态搜索

复杂系统建模与仿真(英文) Pub Date : 2023-12-07 DOI: 10.23919/CSMS.2023.0015

Wei Li;Jianghui Jing;Yangtao Chen;Xunjun Chen;Ata Jahangir Moshayedi

{"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}

引用次数: 0

Digital Twin Implementation of Autonomous Planning Arc Welding Robot System 自主规划弧焊机器人系统的数字孪生实现

复杂系统建模与仿真(英文) Pub Date : 2023-08-03 DOI: 10.23919/CSMS.2023.0013

Xuewu Wang;Yi Hua;Jin Gao;Zongjie Lin;Rui Yu

{"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}

引用次数: 0

Low-Carbon Routing Based on Improved Artificial Bee Colony Algorithm for Electric Trackless Rubber-Tyred Vehicles 基于改进人工蜂群算法的电动无轨橡胶轮胎车辆低碳路径

复杂系统建模与仿真(英文) Pub Date : 2023-08-02 DOI: 10.23919/CSMS.2023.0011

Yinan Guo;Yao Huang;Shirong Ge;Yizhe Zhang;Ersong Jiang;Bin Cheng;Shengxiang Yang

{"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}

引用次数: 1

Gaussian Process Based Modeling and Control of Affine Systems with Control Saturation Constraints 具有控制饱和约束的仿射系统的高斯过程建模与控制

复杂系统建模与仿真(英文) Pub Date : 2023-08-02 DOI: 10.23919/CSMS.2023.0009

Shulong Zhao;Qipeng Wang;Jiayi Zheng;Xiangke Wang

引用次数: 0

Iε+LGEA A Learning-Guided Evolutionary Algorithm Based on Iε+ Indicator for Portfolio Optimization 基于ε+指标的投资组合优化学习引导进化算法

复杂系统建模与仿真(英文) Pub Date : 2023-08-02 DOI: 10.23919/CSMS.2023.0012

Feng Wang;Zilu Huang;Shuwen Wang

{"title":"Iε+LGEA A Learning-Guided Evolutionary Algorithm Based on Iε+ Indicator for Portfolio Optimization","authors":"Feng Wang;Zilu Huang;Shuwen Wang","doi":"10.23919/CSMS.2023.0012","DOIUrl":"10.23919/CSMS.2023.0012","url":null,"abstract":"Portfolio optimization is a classical and important problem in the field of asset management, which aims to achieve a trade-off between profit and risk. Previous portfolio optimization models use traditional risk measurements such as variance, which symmetrically delineate both positive and negative sides and are not practical and stable. In this paper, a new model with cardinality constraints is first proposed, in which the idiosyncratic volatility factor is used to replace traditional risk measurements and can capture the risks of the portfolio in a more accurate way. The new model has practical constraints which involve the sparsity and irregularity of variables and make it challenging to be solved by traditional Multi-Objective Evolutionary Algorithms (MOEAs). To solve the model, a Learning-Guided Evolutionary Algorithm based on I\u0000<inf>ε+</inf>\u0000 indicator (I\u0000<inf>ε+</inf>\u0000LGEA) is developed. In I\u0000<inf>ε+</inf>\u0000LGEA the I\u0000<inf>ε+</inf>\u0000 indicator is incorporated into the initialization and genetic operators to guarantee the sparsity of solutions and can help improve the convergence of the algorithm. And a new constraint-handling method based on I\u0000<inf>ε+</inf>\u0000 indicator is also adopted to ensure the feasibility of solutions. The experimental results on five portfolio trading datasets including up to 1226 assets show that I\u0000<inf>ε+</inf>\u0000LGEA outperforms some state-of-the-art MOEAs in most cases.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 3","pages":"191-201"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10206014/10206019.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43576620","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}

引用次数: 0

Comprehensive Model Construction and Simulation for Superconducting Electrodynamic Suspension Train 超导电动悬架列车综合模型构建与仿真

复杂系统建模与仿真(英文) Pub Date : 2023-08-02 DOI: 10.23919/CSMS.2023.0010

Linfeng Liu;Hao Ye;Wei Dong;Junfeng Cui

引用次数: 0

Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm Optimization 基于贡献自适应粒子群优化的城市生活垃圾高效多行程收集路径

复杂系统建模与仿真(英文) Pub Date : 2023-08-02 DOI: 10.23919/CSMS.2023.0008

Xiaoning Shen;Hongli Pan;Zhongpei Ge;Wenyan Chen;Liyan Song;Shuo Wang

{"title":"Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm Optimization","authors":"Xiaoning Shen;Hongli Pan;Zhongpei Ge;Wenyan Chen;Liyan Song;Shuo Wang","doi":"10.23919/CSMS.2023.0008","DOIUrl":"10.23919/CSMS.2023.0008","url":null,"abstract":"Waste collection is an important part of waste management system. Transportation costs and carbon emissions can be greatly reduced by proper vehicle routing. Meanwhile, each vehicle can work again after achieving its capacity limit and unloading the waste. For this, an energy-efficient multi-trip vehicle routing model is established for municipal solid waste collection, which incorporates practical factors like the limited capacity, maximum working hours, and multiple trips of each vehicle. Considering both economy and environment, fixed costs, fuel costs, and carbon emission costs are minimized together. To solve the formulated model effectively, contribution-based adaptive particle swarm optimization is proposed. Four strategies named greedy learning, multi-operator learning, exploring learning, and exploiting learning are specifically designed with their own searching priorities. By assessing the contribution of each learning strategy during the process of evolution, an appropriate one is selected and assigned to each individual adaptively to improve the searching efficiency of the algorithm. Moreover, an improved local search operator is performed on the trips with the largest number of waste sites so that both the exploiting ability and the convergence accuracy of the algorithm are improved. Performance of the proposed algorithm is tested on ten waste collection instances, which include one real-world case derived from the Green Ring Company of Jiangbei New District, Nanjing, China, and nine synthetic instances with increasing scales generated from the commonly-used capacitated vehicle routing problem benchmark datasets. Comparisons with five state-of-the-art algorithms show that the proposed algorithm can obtain a solution with a higher accuracy for the constructed model.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 3","pages":"202-219"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10206014/10206017.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49326483","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}

引用次数: 0

Variable Reduction Strategy Integrated Variable Neighborhood Search and NSGA-II Hybrid Algorithm for Emergency Material Scheduling 应急物资调度的变约简策略——变邻域搜索和NSGA-II混合算法

复杂系统建模与仿真(英文) Pub Date : 2023-06-20 DOI: 10.23919/CSMS.2023.0006

Zhen Shu;Aijuan Song;Guohua Wu;Witold Pedrycz

{"title":"Variable Reduction Strategy Integrated Variable Neighborhood Search and NSGA-II Hybrid Algorithm for Emergency Material Scheduling","authors":"Zhen Shu;Aijuan Song;Guohua Wu;Witold Pedrycz","doi":"10.23919/CSMS.2023.0006","DOIUrl":"10.23919/CSMS.2023.0006","url":null,"abstract":"Developing a reasonable and efficient emergency material scheduling plan is of great significance to decreasing casualties and property losses. Real-world emergency material scheduling (EMS) problems are typically large-scale and possess complex constraints. An evolutionary algorithm (EA) is one of the effective methods for solving EMS problems. However, the existing EAs still face great challenges when dealing with large-scale EMS problems or EMS problems with equality constraints. To handle the above challenges, we apply the idea of a variable reduction strategy (VRS) to an EMS problem, which can accelerate the optimization process of the used EAs and obtain better solutions by simplifying the corresponding EMS problems. Firstly, we define an emergency material allocation and route scheduling model, and a variable neighborhood search and NSGA-II hybrid algorithm (VNS-NSGAII) is designed to solve the model. Secondly, we utilize VRS to simplify the proposed EMS model to enable a lower dimension and fewer equality constraints. Furthermore, we integrate VRS with VNS-NSGAII to solve the reduced EMS model. To prove the effectiveness of VRS on VNS-NSAGII, we construct two test cases, where one case is based on a multi-depot vehicle routing problem and the other case is combined with the initial 5·12 Wenchuan earthquake emergency material support situation. Experimental results show that VRS can improve the performance of the standard VNS-NSGAII, enabling better optimization efficiency and a higher-quality solution.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 2","pages":"83-101"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10158516/10158517.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48914409","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}

引用次数: 0

Multi-Agent System for Electric Vehicle Charging Scheduling in Parking Lots 停车场电动汽车充电调度的多Agent系统

复杂系统建模与仿真(英文) Pub Date : 2023-06-20 DOI: 10.23919/CSMS.2023.0005

Mao Tan;Zhonglin Zhang;Yuling Ren;Irampaye Richard;Yuzhou Zhang

{"title":"Multi-Agent System for Electric Vehicle Charging Scheduling in Parking Lots","authors":"Mao Tan;Zhonglin Zhang;Yuling Ren;Irampaye Richard;Yuzhou Zhang","doi":"10.23919/CSMS.2023.0005","DOIUrl":"10.23919/CSMS.2023.0005","url":null,"abstract":"As the number of electric vehicles (EVs) increases, massive numbers of EVs have started to gather in commercial parking lots to charge and discharge, which may significantly impact the operation of the grid. There may also be a deviation in the departure time of charged and discharged EVs in commercial parking lots. This deviation can lead to insufficient battery energy when the EVs leave the parking lot. This study uses the simulation software AnyLogic to build a commercial parking lot multi-agent simulation model, and the agent-based model can fully reflect the autonomy of individual EVs. Based on this simulation model, we propose an EV scheduling algorithm. The algorithm contains two main agents. The first is the power distribution center agent (PDCA), which is used to coordinate the energy output of photovoltaic (PV), energy storage system (ESS), and distribution station (DS) to solve the problem of grid overload. The second is the scheduling center agent (SCA), which is used to solve the insufficient battery energy problem due to EVs' random departures. The SCA includes two stages. In the first stage, a priority scheduling algorithm is proposed to emphasize the fairness of EV charging. In the second stage, a genetic algorithm is used to accurately determine the time interval between charging and discharging to ensure the maximum benefit of EV owner. Finally, simulation experiments are conducted in AnyLogic, and the results demonstrate the superiority of the algorithm over the classical algorithm.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 2","pages":"129-142"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10158516/10158520.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45872160","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}

引用次数: 0