复杂系统建模与仿真(英文)最新文献_第5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 : 2023-03-09 DOI: 10.23919/CSMS.2022.0024

Fangqing Gu;Haosen Liu;Hailin Liu

{"title":"A Coevolutionary Algorithm for Many-Objective Optimization Problems with Independent and Harmonious Objectives","authors":"Fangqing Gu;Haosen Liu;Hailin Liu","doi":"10.23919/CSMS.2022.0024","DOIUrl":"https://doi.org/10.23919/CSMS.2022.0024","url":null,"abstract":"Evolutionary algorithm is an effective strategy for solving many-objective optimization problems. At present, most evolutionary many-objective algorithms are designed for solving many-objective optimization problems where the objectives conflict with each other. In some cases, however, the objectives are not always in conflict. It consists of multiple independent objective subsets and the relationship between objectives is unknown in advance. The classical evolutionary many-objective algorithms may not be able to effectively solve such problems. Accordingly, we propose an objective set decomposition strategy based on the partial set covering model. It decomposes the objectives into a collection of objective subsets to preserve the nondominance relationship as much as possible. An optimization subproblem is defined on each objective subset. A coevolutionary algorithm is presented to optimize all subproblems simultaneously, in which a nondominance ranking is presented to interact information among these sub-populations. The proposed algorithm is compared with five popular many-objective evolutionary algorithms and four objective set decomposition based evolutionary algorithms on a series of test problems. Numerical experiments demonstrate that the proposed algorithm can achieve promising results for the many-objective optimization problems with independent and harmonious objectives.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 1","pages":"59-70"},"PeriodicalIF":0.0,"publicationDate":"2023-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10065394/10065401.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49952454","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

Search-Based Software Test Data Generation for Path Coverage Based on a Feedback-Directed Mechanism 基于反馈导向机制的路径覆盖搜索软件测试数据生成

复杂系统建模与仿真(英文) Pub Date : 2023-03-09 DOI: 10.23919/CSMS.2022.0027

Stuart Dereck Semujju;Han Huang;Fangqing Liu;Yi Xiang;Zhifeng Hao

{"title":"Search-Based Software Test Data Generation for Path Coverage Based on a Feedback-Directed Mechanism","authors":"Stuart Dereck Semujju;Han Huang;Fangqing Liu;Yi Xiang;Zhifeng Hao","doi":"10.23919/CSMS.2022.0027","DOIUrl":"10.23919/CSMS.2022.0027","url":null,"abstract":"Automatically generating test cases by evolutionary algorithms to satisfy the path coverage criterion has attracted much research attention in software testing. In the context of generating test cases to cover many target paths, the efficiency of existing methods needs to be further improved when infeasible or difficult paths exist in the program under test. This is because a significant amount of the search budget (i.e., time allocated for the search to run) is consumed when computing fitness evaluations of individuals on infeasible or difficult paths. In this work, we present a feedback-directed mechanism that temporarily removes groups of paths from the target paths when no improvement is observed for these paths in subsequent generations. To fulfill this task, our strategy first organizes paths into groups. Then, in each generation, the objective scores of each individual for all paths in each group are summed up. For each group, the lowest value of the summed up objective scores among all individuals is assigned as the best aggregated score for a group. A group is removed when no improvement is observed in its best aggregated score over the last two generations. The experimental results show that the proposed approach can significantly improve path coverage rates for programs under test with infeasible or difficult paths in case of a limited search budget. In particular, the feedback-directed mechanism reduces wasting the search budget on infeasible paths or on difficult target paths that require many fitness evaluations before getting an improvement.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 1","pages":"12-31"},"PeriodicalIF":0.0,"publicationDate":"2023-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10065394/10065399.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46941872","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

A Parallel High-Utility Itemset Mining Algorithm Based on Hadoop 基于Hadoop的并行高效项集挖掘算法

复杂系统建模与仿真(英文) Pub Date : 2023-03-09 DOI: 10.23919/CSMS.2022.0023

Zaihe Cheng;Wei Shen;Wei Fang;Jerry Chun-Wei Lin

{"title":"A Parallel High-Utility Itemset Mining Algorithm Based on Hadoop","authors":"Zaihe Cheng;Wei Shen;Wei Fang;Jerry Chun-Wei Lin","doi":"10.23919/CSMS.2022.0023","DOIUrl":"10.23919/CSMS.2022.0023","url":null,"abstract":"High-utility itemset mining (HUIM) can consider not only the profit factor but also the profitable factor, which is an essential task in data mining. However, most HUIM algorithms are mainly developed on a single machine, which is inefficient for big data since limited memory and processing capacities are available. A parallel efficient high-utility itemset mining (P-EFIM) algorithm is proposed based on the Hadoop platform to solve this problem in this paper. In P-EFIM, the transaction-weighted utilization values are calculated and ordered for the itemsets with the MapReduce framework. Then the ordered itemsets are renumbered, and the low-utility itemsets are pruned to improve the dataset utility. In the Map phase, the P-EFIM algorithm divides the task into multiple independent subtasks. It uses the proposed S-style distribution strategy to distribute the subtasks evenly across all nodes to ensure load-balancing. Furthermore, the P-EFIM uses the EFIM algorithm to mine each subtask dataset to enhance the performance in the Reduce phase. Experiments are performed on eight datasets, and the results show that the runtime performance of P-EFIM is significantly higher than that of the PHUI-Growth, which is also HUIM algorithm based on the Hadoop framework.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 1","pages":"47-58"},"PeriodicalIF":0.0,"publicationDate":"2023-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10065394/10065400.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47778808","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

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

Total Contents 全部内容

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