Qijia Chen;Dongcheng Li;Man Zhao;W. Eric Wong;Hui Li
Software often contains defects, and automated repair techniques offer a promising way to address these issues. This paper examines the current state of research in learning-based Automated Program Repair (APR). It reviews existing learning-based APR approaches and systematically categorizes them into five major types: supervised learning, unsupervised learning, transfer learning, ensemble learning, and language model learning. Finally, the paper discusses the challenges faced in this field, providing valuable insights for future research.
{"title":"Learning-Based Automated Program Repair: A Systematic Literature Review","authors":"Qijia Chen;Dongcheng Li;Man Zhao;W. Eric Wong;Hui Li","doi":"10.23919/CSMS.2025.0004","DOIUrl":"https://doi.org/10.23919/CSMS.2025.0004","url":null,"abstract":"Software often contains defects, and automated repair techniques offer a promising way to address these issues. This paper examines the current state of research in learning-based Automated Program Repair (APR). It reviews existing learning-based APR approaches and systematically categorizes them into five major types: supervised learning, unsupervised learning, transfer learning, ensemble learning, and language model learning. Finally, the paper discusses the challenges faced in this field, providing valuable insights for future research.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"5 4","pages":"305-322"},"PeriodicalIF":0.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10969585","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145705858","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}
This paper introduces a novel color image encryption algorithm based on a five-dimensional continuous memristor hyperchaotic system (5D-MHS), combined with a two-dimensional Salomon map and an optimized Arnold transform. Firstly, convert the test image to a 2D pixel matrix then processed in blocks, and each block of the pixel matrix is permuted with chaotic sequences generated by 5D-MHS and 2D Salomon map. Then, the permuted image is permuted for three rounds with the optimized Arnold algorithm. Finally, one of the chaotic sequences generated by 5D-MHS is employed to diffuse the permuted image to obtain the final ciphertext image. In this paper, several pseudo-random sequences are generated and mixed in the permutation stage to achieve higher security. The algorithm achieves a key space of 2472, the information entropy of the ciphertext image for the color image is 7.9998, number of pixels change rate (NPCR) and unified average changing intensity (UACI) reached 99.6131 % and 33.4361 %, respectively, and the correlation between pixels is close to O. The simulation results show that the encryption algorithm is efficient and the key system is secure.
{"title":"Color Image Encryption Based on Five-Dimensional Continuous Hyperchaotic System and Optimized Arnold Algorithm","authors":"Zhenju Wang;Cong Wang;Ping Ma;Yue Meng;Hongli Zhang","doi":"10.23919/CSMS.2024.0028","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0028","url":null,"abstract":"This paper introduces a novel color image encryption algorithm based on a five-dimensional continuous memristor hyperchaotic system (5D-MHS), combined with a two-dimensional Salomon map and an optimized Arnold transform. Firstly, convert the test image to a 2D pixel matrix then processed in blocks, and each block of the pixel matrix is permuted with chaotic sequences generated by 5D-MHS and 2D Salomon map. Then, the permuted image is permuted for three rounds with the optimized Arnold algorithm. Finally, one of the chaotic sequences generated by 5D-MHS is employed to diffuse the permuted image to obtain the final ciphertext image. In this paper, several pseudo-random sequences are generated and mixed in the permutation stage to achieve higher security. The algorithm achieves a key space of 2<sup>472</sup>, the information entropy of the ciphertext image for the color image is 7.9998, number of pixels change rate (NPCR) and unified average changing intensity (UACI) reached 99.6131 % and 33.4361 %, respectively, and the correlation between pixels is close to O. The simulation results show that the encryption algorithm is efficient and the key system is secure.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"5 2","pages":"138-154"},"PeriodicalIF":0.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10969590","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937986","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}
A novel consensus-based particle swarm optimization-guided surrogate-enhanced methodology (ACP-S) is developed to solve expensive black-box optimization (EBBO) problems. The proposed methodology consists of three stages: the global exploration and grouping stage, local exploitation via the surrogate model, and the ranking, refinement, and feedback stage. The methodology only searches a subset of the entire search space that contains high-quality optimal solutions and may include the global optimal solution. The proposed three-stage method is fast and deterministic in computing high-quality optimal solutions. Extensive experimental results demonstrate that this proposed method can obtain high-quality optimal solutions and outperforms several existing methods on small or large EBBO test functions.
{"title":"Novel Consensus-Based Particle Swarm Optimization-Guided Surrogate-Enhanced Methodology for Solving Expensive Black-Box Optimization","authors":"Yongfeng Zhang;Zhihao Liu;Kaili Jia;Zhenbin Zhang;Hsiaodong Chiang","doi":"10.23919/CSMS.2025.0006","DOIUrl":"https://doi.org/10.23919/CSMS.2025.0006","url":null,"abstract":"A novel consensus-based particle swarm optimization-guided surrogate-enhanced methodology (ACP-S) is developed to solve expensive black-box optimization (EBBO) problems. The proposed methodology consists of three stages: the global exploration and grouping stage, local exploitation via the surrogate model, and the ranking, refinement, and feedback stage. The methodology only searches a subset of the entire search space that contains high-quality optimal solutions and may include the global optimal solution. The proposed three-stage method is fast and deterministic in computing high-quality optimal solutions. Extensive experimental results demonstrate that this proposed method can obtain high-quality optimal solutions and outperforms several existing methods on small or large EBBO test functions.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"5 4","pages":"340-353"},"PeriodicalIF":0.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10969580","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145705865","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 fidelity of financial market simulation is restricted by the so-called “non-identifiability” difficulty when calibrating high-frequency data. This paper first analyzes the inherent loss of data information in this difficulty, and proposes to use the Kolmogorov-Smirnov test (K-S) as the objective function for high-frequency calibration. Empirical studies verify that K-S has better identifiability of calibrating high-frequency data, while also leads to a much harder multi-modal landscape in the calibration space. To this end, we propose the adaptive stochastic ranking based negatively correlated search algorithm for improving the balance between exploration and exploitation. Experimental results on both simulated data and real market data demonstrate that the proposed method can obtain up to 36.0% improvement in high-frequency data calibration problems over the compared methods.
{"title":"Towards Calibrating Financial Market Simulators with High-Frequency Data","authors":"Peng Yang;Junji Ren;Feng Wang;Ke Tang","doi":"10.23919/CSMS.2025.0002","DOIUrl":"https://doi.org/10.23919/CSMS.2025.0002","url":null,"abstract":"The fidelity of financial market simulation is restricted by the so-called “non-identifiability” difficulty when calibrating high-frequency data. This paper first analyzes the inherent loss of data information in this difficulty, and proposes to use the Kolmogorov-Smirnov test (K-S) as the objective function for high-frequency calibration. Empirical studies verify that K-S has better identifiability of calibrating high-frequency data, while also leads to a much harder multi-modal landscape in the calibration space. To this end, we propose the adaptive stochastic ranking based negatively correlated search algorithm for improving the balance between exploration and exploitation. Experimental results on both simulated data and real market data demonstrate that the proposed method can obtain up to 36.0% improvement in high-frequency data calibration problems over the compared methods.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"5 4","pages":"388-403"},"PeriodicalIF":0.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10969534","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145705929","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 lack of data is constraining research on the bistatic ballistic target (BT). In the bistatic radar system, the angle between the target axis and the radar line of sight (RLOS) is generally large, which means that the specular scattering center (SSC) is likely to be present with the target's micro-motion. This results in a brief flash in the time-frequency representation (TFR) but has been less frequently reported. We propose a micro-motion echo simulation method with SSC by calculating its position vector and modeling its visible coefficient. Furthermore, the model with four estimated parameters is utilized to optimize the simulated TFR. The simulation results intuitively and numerically agree well with the TFR obtained by electromagnetic calculation.
{"title":"Bistatic Echo Simulation Method of Micro-Motion Target with Specular Scattering Conditions","authors":"Yiqi Zhu;Zhiming Xu;Xiaofeng Ai;Xiaoxia Xie;Feng Zhao;Jinhui Zhao","doi":"10.23919/CSMS.2024.0038","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0038","url":null,"abstract":"The lack of data is constraining research on the bistatic ballistic target (BT). In the bistatic radar system, the angle between the target axis and the radar line of sight (RLOS) is generally large, which means that the specular scattering center (SSC) is likely to be present with the target's micro-motion. This results in a brief flash in the time-frequency representation (TFR) but has been less frequently reported. We propose a micro-motion echo simulation method with SSC by calculating its position vector and modeling its visible coefficient. Furthermore, the model with four estimated parameters is utilized to optimize the simulated TFR. The simulation results intuitively and numerically agree well with the TFR obtained by electromagnetic calculation.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"5 3","pages":"296-304"},"PeriodicalIF":0.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10969592","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896768","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 human brain is composed of a large number of neurons that work together to process the generation, transmission, reception, and processing of information. The topological structure of the human brain has small-world characteristics, and the synchronization and neuron firing are influenced by the electromagnetic field. In this paper, we use four-stable discrete memristors to simulate the external electromagnetic field, and construct a memristive small-world neural network (MSNN) model based on Rulkov neurons, and conduct numerical simulations. We have found that the MSNN exhibits multiple coexisting behaviors of synchronous, asynchronous, and chimeric states under different initial conditions of the discrete memristors. At the same time, changing the strength of electromagnetic induction can affect the synchronization performance of the MSNN. Finally, we find that increasing the electromagnetic induction strength can enhance the neuron firing action potential.
{"title":"Synchronization in Memristive Small-World Neural Networks Under Electromagnetic Radiation","authors":"Jiapeng Ouyang;Yalian Wu;Yichuang Sun;Minglin Ma","doi":"10.23919/CSMS.2024.0036","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0036","url":null,"abstract":"The human brain is composed of a large number of neurons that work together to process the generation, transmission, reception, and processing of information. The topological structure of the human brain has small-world characteristics, and the synchronization and neuron firing are influenced by the electromagnetic field. In this paper, we use four-stable discrete memristors to simulate the external electromagnetic field, and construct a memristive small-world neural network (MSNN) model based on Rulkov neurons, and conduct numerical simulations. We have found that the MSNN exhibits multiple coexisting behaviors of synchronous, asynchronous, and chimeric states under different initial conditions of the discrete memristors. At the same time, changing the strength of electromagnetic induction can affect the synchronization performance of the MSNN. Finally, we find that increasing the electromagnetic induction strength can enhance the neuron firing action potential.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"5 3","pages":"252-260"},"PeriodicalIF":0.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10969582","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896767","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}
Mariane Gonçalves de Alcantara;Aneirson Francisco da Silva;Fernando Augusto Silva Marins;Rafael de Carvalho Miranda
Simulation optimization is a rapidly growing research field, fueled by advances in computational technology. These advances have made it possible to solve complex stochastic optimization problems through simulation. While most published articles focus on single-objective optimization, multl-objective optimization is gaining prominence, allowing the approach of real-world problems that present multiple, conflicting objectives. In this context, the objective of this article was to conduct a systematic literature review to identify articles that present solution methods for Multi-Objective Simulation Optimization (MOSO) problems. The focus was on practical optimization applications in conjunction with Discrete Event Simulation (DES) models, aiming to identify the main aspects of the problems addressed, the methods used, and research opportunities, contributing to future projects. By exploring the characteristics of MOSO problems associated with DES and the applied solution methods, this article innovatively presents a guide to help professionals improve their decision-making processes and assist researchers in developing new research.
{"title":"Systematic Literature Review on Multi-Objective Simulation Optimization","authors":"Mariane Gonçalves de Alcantara;Aneirson Francisco da Silva;Fernando Augusto Silva Marins;Rafael de Carvalho Miranda","doi":"10.23919/CSMS.2024.0037","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0037","url":null,"abstract":"Simulation optimization is a rapidly growing research field, fueled by advances in computational technology. These advances have made it possible to solve complex stochastic optimization problems through simulation. While most published articles focus on single-objective optimization, multl-objective optimization is gaining prominence, allowing the approach of real-world problems that present multiple, conflicting objectives. In this context, the objective of this article was to conduct a systematic literature review to identify articles that present solution methods for Multi-Objective Simulation Optimization (MOSO) problems. The focus was on practical optimization applications in conjunction with Discrete Event Simulation (DES) models, aiming to identify the main aspects of the problems addressed, the methods used, and research opportunities, contributing to future projects. By exploring the characteristics of MOSO problems associated with DES and the applied solution methods, this article innovatively presents a guide to help professionals improve their decision-making processes and assist researchers in developing new research.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"5 2","pages":"190-220"},"PeriodicalIF":0.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10969588","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937988","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}
Changao Li;Leilei Meng;Saif Ullah;Peng Duan;Biao Zhang;Hongyan Sang
In real production, machines are operated by workers, and the constraints of worker flexibility should be considered. The flexible job shop scheduling problem with both machine and worker resources (DRCFJSP) has become a research hotspot in recent years. In this paper, DRCFJSP with the objective of minimizing the makespan is studied, and it should solve three sub-problems: machine allocation, worker allocation, and operations sequencing. To solve DRCFJSP, a novel hybrid algorithm (CEAM-CP) of cooperative evolutionary algorithm with multiple populations (CEAM) and constraint programming (CP) is proposed. Specifically, the CEAM-CP algorithm is comprised of two main stages. In the first stage, CEAM is used based on three-layer encoding and full active decoding. Moreover, CEAM has three populations, each of which corresponds to one layer encoding and determines one sub-problem. Moreover, each population evolves cooperatively by multiple cross operations. To further improve the solution quality obtained by CEAM, CP is adopted in the second stage. Experiments are conducted on 13 benchmark instances to assess the effectiveness of multiple crossover operations, CP, and CEAM-CP. Most importantly, the proposed CEAM-CP improves 9 best-known solutions out of 13 benchmark instances.
{"title":"Novel Hybrid Algorithm of Cooperative Evolutionary Algorithm and Constraint Programming for Dual Resource Constrained Flexible Job Shop Scheduling Problems","authors":"Changao Li;Leilei Meng;Saif Ullah;Peng Duan;Biao Zhang;Hongyan Sang","doi":"10.23919/CSMS.2024.0041","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0041","url":null,"abstract":"In real production, machines are operated by workers, and the constraints of worker flexibility should be considered. The flexible job shop scheduling problem with both machine and worker resources (DRCFJSP) has become a research hotspot in recent years. In this paper, DRCFJSP with the objective of minimizing the makespan is studied, and it should solve three sub-problems: machine allocation, worker allocation, and operations sequencing. To solve DRCFJSP, a novel hybrid algorithm (CEAM-CP) of cooperative evolutionary algorithm with multiple populations (CEAM) and constraint programming (CP) is proposed. Specifically, the CEAM-CP algorithm is comprised of two main stages. In the first stage, CEAM is used based on three-layer encoding and full active decoding. Moreover, CEAM has three populations, each of which corresponds to one layer encoding and determines one sub-problem. Moreover, each population evolves cooperatively by multiple cross operations. To further improve the solution quality obtained by CEAM, CP is adopted in the second stage. Experiments are conducted on 13 benchmark instances to assess the effectiveness of multiple crossover operations, CP, and CEAM-CP. Most importantly, the proposed CEAM-CP improves 9 best-known solutions out of 13 benchmark instances.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"5 3","pages":"236-251"},"PeriodicalIF":0.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10969581","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896787","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 permutation flowshop scheduling problem (DPFSP) has received increasing attention in recent years, which always assumes that the machine can process without restrictions. However, in practical production, machine preventive maintenance is required to prevent machine breakdowns. Therefore, this paper studies the DPFSP with preventive maintenance (PM/DPFSP) aiming at minimizing the total flowtime. For solving the problem, a discrete gray wolf optimization algorithm with restart mechanism (DGWO_RM) is proposed. In the initialization phase, a heuristic algorithm that takes into consideration preventive maintenance and idle time is employed to elevate the quality of the initial solution. Next, four local search strategies are proposed for further enhancing the exploitation capability. Furthermore, a restart mechanism is integrated into algorithm to avert the risk of converging prematurely to a suboptimal solution, thereby ensuring a broader exploration of potential solutions. Finally, comprehensive experiments studies are carried out to illustrate the effectiveness of the proposed strategy and to verify the performance of DGWO_RM. The obtained results show that the proposed DGWO_RM significantly outperforms the four state-of-the-art algorithms in solving PM/DPFSP.
{"title":"Efficient Multi-Start Gray Wolf Optimization Algorithm for the Distributed Permutation Flowshop Scheduling Problem with Preventive Maintenance","authors":"Congcong Sun;Hongyan Sang;Leilei Meng;Biao Zhang;Tao Meng","doi":"10.23919/CSMS.2025.0001","DOIUrl":"https://doi.org/10.23919/CSMS.2025.0001","url":null,"abstract":"The distributed permutation flowshop scheduling problem (DPFSP) has received increasing attention in recent years, which always assumes that the machine can process without restrictions. However, in practical production, machine preventive maintenance is required to prevent machine breakdowns. Therefore, this paper studies the DPFSP with preventive maintenance (PM/DPFSP) aiming at minimizing the total flowtime. For solving the problem, a discrete gray wolf optimization algorithm with restart mechanism (DGWO_RM) is proposed. In the initialization phase, a heuristic algorithm that takes into consideration preventive maintenance and idle time is employed to elevate the quality of the initial solution. Next, four local search strategies are proposed for further enhancing the exploitation capability. Furthermore, a restart mechanism is integrated into algorithm to avert the risk of converging prematurely to a suboptimal solution, thereby ensuring a broader exploration of potential solutions. Finally, comprehensive experiments studies are carried out to illustrate the effectiveness of the proposed strategy and to verify the performance of DGWO_RM. The obtained results show that the proposed DGWO_RM significantly outperforms the four state-of-the-art algorithms in solving PM/DPFSP.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"5 2","pages":"107-124"},"PeriodicalIF":0.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10969583","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937987","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}
Automatic guided vehicles (AGVs) are extensively employed in manufacturing workshops for their high degree of automation and flexibility. This paper investigates a limited AGV scheduling problem (LAGVSP) in matrix manufacturing workshops with undirected material flow, aiming to minimize both total task delay time and total task completion time. To address this LAGVSP, a mixed-integer linear programming model is built, and a nondominated sorting genetic algorithm II based on dual population co-evolution (NSGA-IIDPC) is proposed. In NSGA-IIDPC, a single population is divided into a common population and an elite population, and they adopt different evolutionary strategies during the evolution process. The dual population co-evolution mechanism is designed to accelerate the convergence of the non-dominated solution set in the population to the Pareto front through information exchange and competition between the two populations. In addition, to enhance the quality of initial population, a minimum cost function strategy based on load balancing is adopted. Multiple local search operators based on ideal point are proposed to find a better local solution. To improve the global exploration ability of the algorithm, a dual population restart mechanism is adopted. Experimental tests and comparisons with other algorithms are conducted to demonstrate the effectiveness of NSGA-IIDPC in solving the LAGVSP.
{"title":"Effective NSGA-II Algorithm for a Limited AGV Scheduling Problem in Matrix Manufacturing Workshops with Undirected Material Flow","authors":"Xuewu Wang;Jianing Zhang;Yi Hua;Rui Yu","doi":"10.23919/CSMS.2024.0023","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0023","url":null,"abstract":"Automatic guided vehicles (AGVs) are extensively employed in manufacturing workshops for their high degree of automation and flexibility. This paper investigates a limited AGV scheduling problem (LAGVSP) in matrix manufacturing workshops with undirected material flow, aiming to minimize both total task delay time and total task completion time. To address this LAGVSP, a mixed-integer linear programming model is built, and a nondominated sorting genetic algorithm II based on dual population co-evolution (NSGA-IIDPC) is proposed. In NSGA-IIDPC, a single population is divided into a common population and an elite population, and they adopt different evolutionary strategies during the evolution process. The dual population co-evolution mechanism is designed to accelerate the convergence of the non-dominated solution set in the population to the Pareto front through information exchange and competition between the two populations. In addition, to enhance the quality of initial population, a minimum cost function strategy based on load balancing is adopted. Multiple local search operators based on ideal point are proposed to find a better local solution. To improve the global exploration ability of the algorithm, a dual population restart mechanism is adopted. Experimental tests and comparisons with other algorithms are conducted to demonstrate the effectiveness of NSGA-IIDPC in solving the LAGVSP.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"5 1","pages":"68-85"},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10934760","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143655063","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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