In this paper, it aims to model wind speed time series at multiple sites. The five-parameter Johnson distribution is deployed to relate the wind speed at each site to a Gaussian time series, and the resultant m- dimensional Gaussian stochastic vector process $boldsymbol{Z}(t)$ is employed to model the temporal-spatial correlation of wind speeds at $m$ different sites. $ln$ general, it is computationally tedious to obtain the autocorrelation functions (ACFs) and cross-correlation functions (CCFs) of $boldsymbol{Z}(t)$ , which are different to those of wind speed times series. In order to circumvent this correlation distortion problem, the rank ACF and rank CCF are introduced to characterize the temporal-spatial correlation of wind speeds, whereby the ACFs and CCFs of $boldsymbol{Z}(t)$ can be analytically obtained. $text{Then}$ , Fourier transformation is implemented to establish the cross-spectral density matrix of $boldsymbol{Z}(t)$ , and an analytical approach is proposed to generate samples of wind speeds at $m$ different sites. Finally, simulation experiments are performed to check the proposed methods, and the results verify that the five-parameter Johnson distribution can accurately match distribution functions of wind speeds, and the spectral representation method can well reproduce the temporal-spatial correlation of wind speeds.
{"title":"Simulating Temporally and Spatially Correlated Wind Speed Time Series by Spectral Representation Method","authors":"Qing Xiao;Lianghong Wu;Xiaowen Wu;Matthias Rätsch","doi":"10.23919/CSMS.2023.0007","DOIUrl":"10.23919/CSMS.2023.0007","url":null,"abstract":"In this paper, it aims to model wind speed time series at multiple sites. The five-parameter Johnson distribution is deployed to relate the wind speed at each site to a Gaussian time series, and the resultant m- dimensional Gaussian stochastic vector process \u0000<tex>$boldsymbol{Z}(t)$</tex>\u0000 is employed to model the temporal-spatial correlation of wind speeds at \u0000<tex>$m$</tex>\u0000 different sites. \u0000<tex>$ln$</tex>\u0000 general, it is computationally tedious to obtain the autocorrelation functions (ACFs) and cross-correlation functions (CCFs) of \u0000<tex>$boldsymbol{Z}(t)$</tex>\u0000, which are different to those of wind speed times series. In order to circumvent this correlation distortion problem, the rank ACF and rank CCF are introduced to characterize the temporal-spatial correlation of wind speeds, whereby the ACFs and CCFs of \u0000<tex>$boldsymbol{Z}(t)$</tex>\u0000 can be analytically obtained. \u0000<tex>$text{Then}$</tex>\u0000, Fourier transformation is implemented to establish the cross-spectral density matrix of \u0000<tex>$boldsymbol{Z}(t)$</tex>\u0000, and an analytical approach is proposed to generate samples of wind speeds at \u0000<tex>$m$</tex>\u0000 different sites. Finally, simulation experiments are performed to check the proposed methods, and the results verify that the five-parameter Johnson distribution can accurately match distribution functions of wind speeds, and the spectral representation method can well reproduce the temporal-spatial correlation of wind speeds.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 2","pages":"157-168"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10158516/10158490.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46730014","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}
Machine stator winding insulation degradation is one of the main results of machine aging. It is non-negligible once this degradation process becomes asymmetric between phases. The traditional way to determine the insulation state of health is a partial discharge test. However, this method requires the system offline, which causes production loss and extra administrative burden. This paper presents an idea for better characterizing the insulation machine's state of health using common-mode (CM) behavior in the machine-drive system. With the help of circuit decomposition methods and modeling tools, the CM quantities due to asymmetric aging show a unique characteristic that distinguishes itself from other differential-mode (DM) quantities in the equivalent circuit. It is shown effective to represent the asymmetric aging effect from the detection of system leakage current. This paper provides an analytical method to quantify this characteristic from mathematical approaches, and a proper approximation has been made on the CM equivalent model (CEM) such that the CM behavior is accurately characterized. The proposed method will serve the purpose of predicting machine abnormal behavior using the simple RLC circuit. Researchers can adapt this method to quantify and characterize the machine insulation state of health (SOH).
{"title":"Modeling Common-Mode Current Due to Asymmetric Aging of Machine Winding Insulation","authors":"Jin Zhao;Aaron D. Brovont","doi":"10.23919/CSMS.2023.0004","DOIUrl":"10.23919/CSMS.2023.0004","url":null,"abstract":"Machine stator winding insulation degradation is one of the main results of machine aging. It is non-negligible once this degradation process becomes asymmetric between phases. The traditional way to determine the insulation state of health is a partial discharge test. However, this method requires the system offline, which causes production loss and extra administrative burden. This paper presents an idea for better characterizing the insulation machine's state of health using common-mode (CM) behavior in the machine-drive system. With the help of circuit decomposition methods and modeling tools, the CM quantities due to asymmetric aging show a unique characteristic that distinguishes itself from other differential-mode (DM) quantities in the equivalent circuit. It is shown effective to represent the asymmetric aging effect from the detection of system leakage current. This paper provides an analytical method to quantify this characteristic from mathematical approaches, and a proper approximation has been made on the CM equivalent model (CEM) such that the CM behavior is accurately characterized. The proposed method will serve the purpose of predicting machine abnormal behavior using the simple RLC circuit. Researchers can adapt this method to quantify and characterize the machine insulation state of health (SOH).","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 2","pages":"118-128"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10158516/10158518.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42562755","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}
Bin Wu;Shuangwei Bai;Bijina Rajbhandari;Bangyuan Li;Kesheng Wang
Supply chain disruption risk usually poses a serious challenge to the management of emergency supplies procurement between the government and enterprises in cooperation. To research the impact of supply chain disruption on the supply and demand sides of emergency supplies for disaster relief, the emergency procurement model based on quantity flexibility contract is constructed. The model introduces a stockout disruption to measure the degree of supply chain disruption and uses per unit of material relief value to quantify government disaster relief benefits. Further, it analyzes the basic pricing strategy and the agreed order quantity between the government and enterprises, focusing on the negative impact of supply disruption on the government and enterprises. The model deduction and data analysis results show that supply disruption creates a “lose-lose” situation for governments and enterprises, reducing their benefits and willingness to cooperate. Finally, a sensitivity analysis is conducted on the case data to explain the decision-making changes in the contract price and flexibility parameters between the government and enterprises before and after the supply disruption.
{"title":"Quantity Flexibility Contract Model for Emergency Procurement Considering Supply Disruption","authors":"Bin Wu;Shuangwei Bai;Bijina Rajbhandari;Bangyuan Li;Kesheng Wang","doi":"10.23919/CSMS.2023.0002","DOIUrl":"10.23919/CSMS.2023.0002","url":null,"abstract":"Supply chain disruption risk usually poses a serious challenge to the management of emergency supplies procurement between the government and enterprises in cooperation. To research the impact of supply chain disruption on the supply and demand sides of emergency supplies for disaster relief, the emergency procurement model based on quantity flexibility contract is constructed. The model introduces a stockout disruption to measure the degree of supply chain disruption and uses per unit of material relief value to quantify government disaster relief benefits. Further, it analyzes the basic pricing strategy and the agreed order quantity between the government and enterprises, focusing on the negative impact of supply disruption on the government and enterprises. The model deduction and data analysis results show that supply disruption creates a “lose-lose” situation for governments and enterprises, reducing their benefits and willingness to cooperate. Finally, a sensitivity analysis is conducted on the case data to explain the decision-making changes in the contract price and flexibility parameters between the government and enterprises before and after the supply disruption.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 2","pages":"143-156"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10158516/10158491.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42679020","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}
Ning Li;Zhenglian Su;Haifeng Ling;Mumtaz Karatas;Yujun Zheng
Due to their advantages in flexibility, scalability, survivability, and cost-effectiveness, drone swarms have been increasingly used for reconnaissance tasks and have posed great challenges to their opponents on modern battlefields. This paper studies an optimization problem for deploying air defense systems against reconnaissance drone swarms. Given a set of available air defense systems, the problem determines the location of each air defense system in a predetermined region, such that the cost for enemy drones to pass through the region would be maximized. The cost is calculated based on a counterpart drone path planning problem. To solve this adversarial problem, we first propose an exact iterative search algorithm for small-size problem instances, and then propose an evolutionary framework that uses a specific encoding-decoding scheme for large-size problem instances. We implement the evolutionary framework with six popular evolutionary algorithms. Computational experiments on a set of different test instances validate the effectiveness of our approach for defending against reconnaissance drone swarms.
{"title":"Optimization of Air Defense System Deployment Against Reconnaissance Drone Swarms","authors":"Ning Li;Zhenglian Su;Haifeng Ling;Mumtaz Karatas;Yujun Zheng","doi":"10.23919/CSMS.2023.0003","DOIUrl":"10.23919/CSMS.2023.0003","url":null,"abstract":"Due to their advantages in flexibility, scalability, survivability, and cost-effectiveness, drone swarms have been increasingly used for reconnaissance tasks and have posed great challenges to their opponents on modern battlefields. This paper studies an optimization problem for deploying air defense systems against reconnaissance drone swarms. Given a set of available air defense systems, the problem determines the location of each air defense system in a predetermined region, such that the cost for enemy drones to pass through the region would be maximized. The cost is calculated based on a counterpart drone path planning problem. To solve this adversarial problem, we first propose an exact iterative search algorithm for small-size problem instances, and then propose an evolutionary framework that uses a specific encoding-decoding scheme for large-size problem instances. We implement the evolutionary framework with six popular evolutionary algorithms. Computational experiments on a set of different test instances validate the effectiveness of our approach for defending against reconnaissance drone swarms.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 2","pages":"102-117"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10158516/10158519.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41698610","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}
Bin Chen;Runkang Guo;Zhengqiu Zhu;Chuan Ai;Xiaogang Qiu
The Corona Virus Disease 2019 (COVID-19) pandemic is still imposing a devastating impact on public health, the economy, and society. Predicting the development of epidemics and exploring the effects of various mitigation strategies have been a research focus in recent years. However, the spread simulation of COVID-19 in the dynamic social system is relatively unexplored. To address this issue, considering the outbreak of COVID-19 at Nanjing Lukou Airport in 2021, we constructed an artificial society of Nanjing Lukou Airport based on the Artificial societies, Computational experiments, and Parallel execution (ACP) approach. Specifically, the artificial society includes an environmental model, population model, contact networks model, disease spread model, and intervention strategy model. To reveal the dynamic variation of individuals in the airport, we first modeled the movement of passengers and designed an algorithm to generate the moving traces. Then, the mobile contact networks were constructed and aggregated with the static networks of staff and passengers. Finally, the complex dynamical network of contacts between individuals was generated. Based on the artificial society, we conducted large-scale computational experiments to study the spread characteristics of COVID-19 in an airport and to investigate the effects of different intervention strategies. Learned from the reproduction of the outbreak, it is found that the increase in cumulative incidence exhibits a linear growth mode, different from that (an exponential growth mode) in a static network. In terms of mitigation measures, promoting unmanned security checks and boarding in an airport is recommended, as to reduce contact behaviors between individuals and staff.
{"title":"Simulation of COVID-19 Outbreak in Nanjing Lukou Airport Based on Complex Dynamical Networks","authors":"Bin Chen;Runkang Guo;Zhengqiu Zhu;Chuan Ai;Xiaogang Qiu","doi":"10.23919/CSMS.2023.0001","DOIUrl":"10.23919/CSMS.2023.0001","url":null,"abstract":"The Corona Virus Disease 2019 (COVID-19) pandemic is still imposing a devastating impact on public health, the economy, and society. Predicting the development of epidemics and exploring the effects of various mitigation strategies have been a research focus in recent years. However, the spread simulation of COVID-19 in the dynamic social system is relatively unexplored. To address this issue, considering the outbreak of COVID-19 at Nanjing Lukou Airport in 2021, we constructed an artificial society of Nanjing Lukou Airport based on the Artificial societies, Computational experiments, and Parallel execution (ACP) approach. Specifically, the artificial society includes an environmental model, population model, contact networks model, disease spread model, and intervention strategy model. To reveal the dynamic variation of individuals in the airport, we first modeled the movement of passengers and designed an algorithm to generate the moving traces. Then, the mobile contact networks were constructed and aggregated with the static networks of staff and passengers. Finally, the complex dynamical network of contacts between individuals was generated. Based on the artificial society, we conducted large-scale computational experiments to study the spread characteristics of COVID-19 in an airport and to investigate the effects of different intervention strategies. Learned from the reproduction of the outbreak, it is found that the increase in cumulative incidence exhibits a linear growth mode, different from that (an exponential growth mode) in a static network. In terms of mitigation measures, promoting unmanned security checks and boarding in an airport is recommended, as to reduce contact behaviors between individuals and staff.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 1","pages":"71-82"},"PeriodicalIF":0.0,"publicationDate":"2023-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10065394/10065402.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47381255","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}
Xuesong Yan;Hao Zuo;Chengyu Hu;Wenyin Gong;Victor S. Sheng
A chip mounter is the core equipment in the production line of the surface-mount technology, which is responsible for finishing the mount operation. It is the most complex and time-consuming stage in the production process. Therefore, it is of great significance to optimize the load balance and mounting efficiency of the chip mounter and improve the mounting efficiency of the production line. In this study, according to the specific type of chip mounter in the actual production line of a company, a maximum and minimum model is established to minimize the maximum cycle time of the chip mounter in the production line. The production efficiency of the production line can be improved by optimizing the workload scheduling of each chip mounter. On this basis, a hybrid adaptive optimization algorithm is proposed to solve the load scheduling problem of the mounter. The hybrid algorithm is a hybrid of an adaptive genetic algorithm and the improved ant colony algorithm. It combines the advantages of the two algorithms and improves their global search ability and convergence speed. The experimental results show that the proposed hybrid optimization algorithm has a good optimization effect and convergence in the load scheduling problem of chip mounters.
{"title":"Load Optimization Scheduling of Chip Mounter Based on Hybrid Adaptive Optimization Algorithm","authors":"Xuesong Yan;Hao Zuo;Chengyu Hu;Wenyin Gong;Victor S. Sheng","doi":"10.23919/CSMS.2022.0026","DOIUrl":"10.23919/CSMS.2022.0026","url":null,"abstract":"A chip mounter is the core equipment in the production line of the surface-mount technology, which is responsible for finishing the mount operation. It is the most complex and time-consuming stage in the production process. Therefore, it is of great significance to optimize the load balance and mounting efficiency of the chip mounter and improve the mounting efficiency of the production line. In this study, according to the specific type of chip mounter in the actual production line of a company, a maximum and minimum model is established to minimize the maximum cycle time of the chip mounter in the production line. The production efficiency of the production line can be improved by optimizing the workload scheduling of each chip mounter. On this basis, a hybrid adaptive optimization algorithm is proposed to solve the load scheduling problem of the mounter. The hybrid algorithm is a hybrid of an adaptive genetic algorithm and the improved ant colony algorithm. It combines the advantages of the two algorithms and improves their global search ability and convergence speed. The experimental results show that the proposed hybrid optimization algorithm has a good optimization effect and convergence in the load scheduling problem of chip mounters.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 1","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2023-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10065394/10065396.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42369062","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}
At present, home health care (HHC) has been accepted as an effective method for handling the healthcare problems of the elderly. The HHC scheduling and routing problem (HHCSRP) attracts wide concentration from academia and industrial communities. This work proposes an HHCSRP considering several care centers, where a group of customers (i.e., patients and the elderly) require being assigned to care centers. Then, various kinds of services are provided by caregivers for customers in different regions. By considering the skill matching, customers' appointment time, and caregivers' workload balancing, this article formulates an optimization model with multiple objectives to achieve minimal service cost and minimal delay cost. To handle it, we then introduce a brain storm optimization method with particular multi-objective search mechanisms (MOBSO) via combining with the features of the investigated HHCSRP. Moreover, we perform experiments to test the effectiveness of the designed method. Via comparing the MOBSO with two excellent optimizers, the results confirm that the developed method has significant superiority in addressing the considered HHCSRP.
{"title":"A Multi-Objective Scheduling and Routing Problem for Home Health Care Services via Brain Storm Optimization","authors":"Xiaomeng Ma;Yaping Fu;Kaizhou Gao;Lihua Zhu;Ali Sadollah","doi":"10.23919/CSMS.2022.0025","DOIUrl":"10.23919/CSMS.2022.0025","url":null,"abstract":"At present, home health care (HHC) has been accepted as an effective method for handling the healthcare problems of the elderly. The HHC scheduling and routing problem (HHCSRP) attracts wide concentration from academia and industrial communities. This work proposes an HHCSRP considering several care centers, where a group of customers (i.e., patients and the elderly) require being assigned to care centers. Then, various kinds of services are provided by caregivers for customers in different regions. By considering the skill matching, customers' appointment time, and caregivers' workload balancing, this article formulates an optimization model with multiple objectives to achieve minimal service cost and minimal delay cost. To handle it, we then introduce a brain storm optimization method with particular multi-objective search mechanisms (MOBSO) via combining with the features of the investigated HHCSRP. Moreover, we perform experiments to test the effectiveness of the designed method. Via comparing the MOBSO with two excellent optimizers, the results confirm that the developed method has significant superiority in addressing the considered HHCSRP.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"3 1","pages":"32-46"},"PeriodicalIF":0.0,"publicationDate":"2023-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/10065394/10065398.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46756248","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}
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.
{"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}
Stuart Dereck Semujju;Han Huang;Fangqing Liu;Yi Xiang;Zhifeng Hao
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.
{"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}
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.
{"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}
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