复杂系统建模与仿真(英文)最新文献_第6页

A Coevolutionary Algorithm for Many-Objective Optimization Problems with Independent and Harmonious Objectives 具有独立协调目标的多目标优化问题的协同进化算法

复杂系统建模与仿真(英文) Pub Date : 2023-03-01 DOI: 10.23919/csms.2022.0024

Fangqing Gu, Haosen Liu, Hailin Liu

引用次数: 0

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复杂系统建模与仿真(英文) Pub Date : 2022-12-01 DOI: 10.23919/CSMS.2022.10004915

引用次数: 0

Quantum-Inspired Distributed Memetic Algorithm 量子启发的分布式模因算法

复杂系统建模与仿真(英文) Pub Date : 2022-12-01 DOI: 10.23919/CSMS.2022.0021

Guanghui Zhang;Wenjing Ma;Keyi Xing;Lining Xing;Kesheng Wang

{"title":"Quantum-Inspired Distributed Memetic Algorithm","authors":"Guanghui Zhang;Wenjing Ma;Keyi Xing;Lining Xing;Kesheng Wang","doi":"10.23919/CSMS.2022.0021","DOIUrl":"10.23919/CSMS.2022.0021","url":null,"abstract":"This paper proposed a novel distributed memetic evolutionary model, where four modules distributed exploration, intensified exploitation, knowledge transfer, and evolutionary restart are coevolved to maximize their strengths and achieve superior global optimality. Distributed exploration evolves three independent populations by heterogenous operators. Intensified exploitation evolves an external elite archive in parallel with exploration to balance global and local searches. Knowledge transfer is based on a point-ring communication topology to share successful experiences among distinct search agents. Evolutionary restart adopts an adaptive perturbation strategy to control search diversity reasonably. Quantum computation is a newly emerging technique, which has powerful computing power and parallelized ability. Therefore, this paper further fuses quantum mechanisms into the proposed evolutionary model to build a new evolutionary algorithm, referred to as quantum-inspired distributed memetic algorithm (QDMA). In QDMA, individuals are represented by the quantum characteristics and evolved by the quantum-inspired evolutionary optimizers in the quantum hyperspace. The QDMA integrates the superiorities of distributed, memetic, and quantum evolution. Computational experiments are carried out to evaluate the superior performance of QDMA. The results demonstrate the effectiveness of special designs and show that QDMA has greater superiority compared to the compared state-of-the-art algorithms based on Wilcoxon's rank-sum test. The superiority is attributed not only to good cooperative coevolution of distributed memetic evolutionary model, but also to superior designs of each special component.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"2 4","pages":"334-353"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10004846/10004910.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45416730","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

Model Construction and Numerical Simulation for Hydroplaning of Complex Tread Tires 复杂胎面轮胎打滑模型构建及数值模拟

复杂系统建模与仿真(英文) Pub Date : 2022-12-01 DOI: 10.23919/CSMS.2022.0020

Senwang Tao;Jinbiao Wang;Ruonan Dong

引用次数: 0

Dual-Stage Hybrid Learning Particle Swarm Optimization Algorithm for Global Optimization Problems 全局优化问题的双阶段混合学习粒子群优化算法

复杂系统建模与仿真(英文) Pub Date : 2022-12-01 DOI: 10.23919/CSMS.2022.0018

Wei Li;Yangtao Chen;Qian Cai;Cancan Wang;Ying Huang;Soroosh Mahmoodi

{"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":"2 4","pages":"288-306"},"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}

引用次数: 2

Brain-Controlled Multi-Robot at Servo-Control Level Based on Nonlinear Model Predictive Control 基于非线性模型预测控制的脑控多机器人伺服控制

复杂系统建模与仿真(英文) Pub Date : 2022-12-01 DOI: 10.23919/CSMS.2022.0019

Zhenge Yang;Luzheng Bi;Weiming Chi;Haonan Shi;Cuntai Guan

{"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":"2 4","pages":"307-321"},"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}

引用次数: 1

Distributed Model Predictive Contouring Control for Real-Time Multi-Robot Motion Planning 实时多机器人运动规划的分布式模型预测轮廓控制

复杂系统建模与仿真(英文) Pub Date : 2022-12-01 DOI: 10.23919/CSMS.2022.0017

Jianbin Xin;Yaoguang Qu;Fangfang Zhang;Rudy Negenborn

引用次数: 0

An Integrated Observer Framework Based Mechanical Parameters Identification for Adaptive Control of Permanent Magnet Synchronous Motor 基于观测器的永磁同步电机机械参数辨识方法

复杂系统建模与仿真(英文) Pub Date : 2022-12-01 DOI: 10.23919/CSMS.2022.0022

Zhong Liao;Zhaohua Liu;Lei Chen;Mingyang Lyu;Zhengheng Wang;Dian Wang;Faming Wu;Hualiang Wei

{"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":"2 4","pages":"354-367"},"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}

引用次数: 1

Modeling and Analyzing of Breast Tumor Deterioration Process with Petri Nets and Logistic Regression 乳腺肿瘤恶化过程的Petri网和Logistic回归建模与分析

复杂系统建模与仿真(英文) Pub Date : 2022-09-30 DOI: 10.23919/CSMS.2022.0016

Xuyue Wang;Wangyang Yu;Zeyuan Ding;Xiaojun Zhai;Sangeet Saha

{"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":"2 3","pages":"264-272"},"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}

引用次数: 1

An Evolutionary Adaptive System for Prediction of Strategy Influence: A Case Study of Government Regulation Guided Brand Innovation 战略影响预测的进化适应系统——以政府规制引导的品牌创新为例

复杂系统建模与仿真(英文) Pub Date : 2022-09-30 DOI: 10.23919/CSMS.2022.0011

Jiali Lin;Qiaomei Li;Guangsheng Lin;Zhihui He;Dazhi Jiang;Hao Liu

{"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":"2 3","pages":"197-212"},"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}

引用次数: 0