Mai Sun;Chaoli Sun;Xiaobo Li;Guochen Zhang;Farooq Akhtar
Some optimization problems in scientific research, such as the robustness optimization for the Internet of Things and the neural architecture search, are large-scale in decision space and expensive for objective evaluation. In order to get a good solution in a limited budget for the large-scale expensive optimization, a random grouping strategy is adopted to divide the problem into some low-dimensional sub-problems. A surrogate model is then trained for each sub-problem using different strategies to select training data adaptively. After that, a dynamic infill criterion is proposed corresponding to the models currently used in the surrogate-assisted sub-problem optimization. Furthermore, an escape mechanism is proposed to keep the diversity of the population. The performance of the method is evaluated on CEC'2013 benchmark functions. Experimental results show that the algorithm has better performance in solving expensive large-scale optimization problems.
{"title":"Large-Scale Expensive Optimization with a Switching Strategy","authors":"Mai Sun;Chaoli Sun;Xiaobo Li;Guochen Zhang;Farooq Akhtar","doi":"10.23919/CSMS.2022.0013","DOIUrl":"10.23919/CSMS.2022.0013","url":null,"abstract":"Some optimization problems in scientific research, such as the robustness optimization for the Internet of Things and the neural architecture search, are large-scale in decision space and expensive for objective evaluation. In order to get a good solution in a limited budget for the large-scale expensive optimization, a random grouping strategy is adopted to divide the problem into some low-dimensional sub-problems. A surrogate model is then trained for each sub-problem using different strategies to select training data adaptively. After that, a dynamic infill criterion is proposed corresponding to the models currently used in the surrogate-assisted sub-problem optimization. Furthermore, an escape mechanism is proposed to keep the diversity of the population. The performance of the method is evaluated on CEC'2013 benchmark functions. Experimental results show that the algorithm has better performance in solving expensive large-scale optimization problems.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/9906545/09906551.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49401534","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}
With the popularization of multi-variety and small-batch production patterns, the flexible job shop scheduling problem (FJSSP) has been widely studied. The sharing of processing resources by multiple machines frequently occurs due to space constraints in a flexible shop, which results in resource preemption for processing workpieces. Resource preemption complicates the constraints of scheduling problems that are otherwise difficult to solve. In this paper, the flexible job shop scheduling problem under the process resource preemption scenario is modeled, and a two-layer rule scheduling algorithm based on deep reinforcement learning is proposed to achieve the goal of minimum scheduling time. The simulation experiments compare our scheduling algorithm with two traditional metaheuristic optimization algorithms among different processing resource distribution scenarios in static scheduling environment. The results suggest that the two-layer rule scheduling algorithm based on deep reinforcement learning is more effective than the meta-heuristic algorithm in the application of processing resource preemption scenarios. Ablation experiments, generalization, and dynamic experiments are performed to demonstrate the excellent performance of our method for FJSSP under resource preemption.
{"title":"Optimal Design of Flexible Job Shop Scheduling Under Resource Preemption Based on Deep Reinforcement Learning","authors":"Zhen Chen;Lin Zhang;Xiaohan Wang;Pengfei Gu","doi":"10.23919/CSMS.2022.0007","DOIUrl":"10.23919/CSMS.2022.0007","url":null,"abstract":"With the popularization of multi-variety and small-batch production patterns, the flexible job shop scheduling problem (FJSSP) has been widely studied. The sharing of processing resources by multiple machines frequently occurs due to space constraints in a flexible shop, which results in resource preemption for processing workpieces. Resource preemption complicates the constraints of scheduling problems that are otherwise difficult to solve. In this paper, the flexible job shop scheduling problem under the process resource preemption scenario is modeled, and a two-layer rule scheduling algorithm based on deep reinforcement learning is proposed to achieve the goal of minimum scheduling time. The simulation experiments compare our scheduling algorithm with two traditional metaheuristic optimization algorithms among different processing resource distribution scenarios in static scheduling environment. The results suggest that the two-layer rule scheduling algorithm based on deep reinforcement learning is more effective than the meta-heuristic algorithm in the application of processing resource preemption scenarios. Ablation experiments, generalization, and dynamic experiments are performed to demonstrate the excellent performance of our method for FJSSP under resource preemption.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/9841527/09841531.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42448649","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}
Laparoscopic cholecystectomy is used to treat cholecystitis and cholelithiasis. Because the high risk of the surgery prevents novice doctors from practicing it on real patients, VR-based surgical simulation has been developed to simulate surgical procedures to train surgeons without patients, cadavers, or animals. In this study, we propose a real-time system designed to provide plausible visual and tactile simulation of the main surgical procedures. To achieve this, the physical properties of organs are modeled by particles, and cluster-based shape matching is used to simulate soft deformation. The haptic interaction between tools and soft tissue is modeled as a collision between a capsule and particles. Constraint-based haptic rendering is used to generate feedback force and the non-penetrating position of the virtual tool. The proposed system can simulate the major steps of laparoscopic cholecystectomy, such as the anatomy of Calot's triangle, clipping of the cystic duct and biliary artery, disjunction of the cystic duct and biliary artery, and separation of the gallbladder bed. The experimental results show that haptic rendering can be performed at a high frequency (> 900 Hz), whereas mesh skinning and graphics rendering can be performed at 60 frames per second (fps).
{"title":"Real-Time Laparoscopic Cholecystectomy Simulation Using a Particle-Based Physical System","authors":"Hongyu Wu;Fan Ye;Yang Gao;Yu Cong;Aimin Hao","doi":"10.23919/CSMS.2022.0009","DOIUrl":"10.23919/CSMS.2022.0009","url":null,"abstract":"Laparoscopic cholecystectomy is used to treat cholecystitis and cholelithiasis. Because the high risk of the surgery prevents novice doctors from practicing it on real patients, VR-based surgical simulation has been developed to simulate surgical procedures to train surgeons without patients, cadavers, or animals. In this study, we propose a real-time system designed to provide plausible visual and tactile simulation of the main surgical procedures. To achieve this, the physical properties of organs are modeled by particles, and cluster-based shape matching is used to simulate soft deformation. The haptic interaction between tools and soft tissue is modeled as a collision between a capsule and particles. Constraint-based haptic rendering is used to generate feedback force and the non-penetrating position of the virtual tool. The proposed system can simulate the major steps of laparoscopic cholecystectomy, such as the anatomy of Calot's triangle, clipping of the cystic duct and biliary artery, disjunction of the cystic duct and biliary artery, and separation of the gallbladder bed. The experimental results show that haptic rendering can be performed at a high frequency (> 900 Hz), whereas mesh skinning and graphics rendering can be performed at 60 frames per second (fps).","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/9841527/09841530.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47061598","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}
Two-stage hybrid flow shop scheduling has been extensively considered in single-factory settings. However, the distributed two-stage hybrid flow shop scheduling problem (DTHFSP) with fuzzy processing time is seldom investigated in multiple factories. Furthermore, the integration of reinforcement learning and metaheuristic is seldom applied to solve DTHFSP. In the current study, DTHFSP with fuzzy processing time was investigated, and a novel Q-learning-based teaching-learning based optimization (QTLBO) was constructed to minimize makespan. Several teachers were recruited for this study. The teacher phase, learner phase, teacher's self-learning phase, and learner's self-learning phase were designed. The Q-learning algorithm was implemented by 9 states, 4 actions defined as combinations of the above phases, a reward, and an adaptive action selection, which were applied to dynamically adjust the algorithm structure. A number of experiments were conducted. The computational results demonstrate that the new strategies of QTLBO are effective; furthermore, it presents promising results on the considered DTHFSP.
{"title":"Q-Learning-Based Teaching-Learning Optimization for Distributed Two-Stage Hybrid Flow Shop Scheduling with Fuzzy Processing Time","authors":"Bingjie Xi;Deming Lei","doi":"10.23919/CSMS.2022.0002","DOIUrl":"10.23919/CSMS.2022.0002","url":null,"abstract":"Two-stage hybrid flow shop scheduling has been extensively considered in single-factory settings. However, the distributed two-stage hybrid flow shop scheduling problem (DTHFSP) with fuzzy processing time is seldom investigated in multiple factories. Furthermore, the integration of reinforcement learning and metaheuristic is seldom applied to solve DTHFSP. In the current study, DTHFSP with fuzzy processing time was investigated, and a novel Q-learning-based teaching-learning based optimization (QTLBO) was constructed to minimize makespan. Several teachers were recruited for this study. The teacher phase, learner phase, teacher's self-learning phase, and learner's self-learning phase were designed. The Q-learning algorithm was implemented by 9 states, 4 actions defined as combinations of the above phases, a reward, and an adaptive action selection, which were applied to dynamically adjust the algorithm structure. A number of experiments were conducted. The computational results demonstrate that the new strategies of QTLBO are effective; furthermore, it presents promising results on the considered DTHFSP.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/9841527/09841529.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43454572","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}
Economic globalization has transformed many manufacturing enterprises from a single-plant production mode to a multi-plant cooperative production mode. The distributed flexible job-shop scheduling problem (DFJSP) has become a research hot topic in the field of scheduling because its production is closer to reality. The research of DFJSP is of great significance to the organization and management of actual production process. To solve the heterogeneous DFJSP with minimal completion time, a hybrid chemical reaction optimization (HCRO) algorithm is proposed in this paper. Firstly, a novel encoding-decoding method for flexible manufacturing unit (FMU) is designed. Secondly, half of initial populations are generated by scheduling rule. Combined with the new solution acceptance method of simulated annealing (SA) algorithm, an improved method of critical-FMU is designed to improve the global and local search ability of the algorithm. Finally, the elitist selection strategy and the orthogonal experimental method are introduced to the algorithm to improve the convergence speed and optimize the algorithm parameters. In the experimental part, the effectiveness of the simulated annealing algorithm and the critical-FMU refinement methods is firstly verified. Secondly, in the comparison with other existing algorithms, the proposed optimal scheduling algorithm is not only effective in homogeneous FMUs examples, but also superior to existing algorithms in heterogeneous FMUs arithmetic cases.
{"title":"Distributed Flexible Job-Shop Scheduling Problem Based on Hybrid Chemical Reaction Optimization Algorithm","authors":"Jialei Li;Xingsheng Gu;Yaya Zhang;Xin Zhou","doi":"10.23919/CSMS.2022.0010","DOIUrl":"10.23919/CSMS.2022.0010","url":null,"abstract":"Economic globalization has transformed many manufacturing enterprises from a single-plant production mode to a multi-plant cooperative production mode. The distributed flexible job-shop scheduling problem (DFJSP) has become a research hot topic in the field of scheduling because its production is closer to reality. The research of DFJSP is of great significance to the organization and management of actual production process. To solve the heterogeneous DFJSP with minimal completion time, a hybrid chemical reaction optimization (HCRO) algorithm is proposed in this paper. Firstly, a novel encoding-decoding method for flexible manufacturing unit (FMU) is designed. Secondly, half of initial populations are generated by scheduling rule. Combined with the new solution acceptance method of simulated annealing (SA) algorithm, an improved method of critical-FMU is designed to improve the global and local search ability of the algorithm. Finally, the elitist selection strategy and the orthogonal experimental method are introduced to the algorithm to improve the convergence speed and optimize the algorithm parameters. In the experimental part, the effectiveness of the simulated annealing algorithm and the critical-FMU refinement methods is firstly verified. Secondly, in the comparison with other existing algorithms, the proposed optimal scheduling algorithm is not only effective in homogeneous FMUs examples, but also superior to existing algorithms in heterogeneous FMUs arithmetic cases.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/9841527/09841532.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47043042","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 trajectory planning of multiple unmanned aerial vehicles (UAVs) is the core of efficient UAV mission execution. Existing studies have mainly transformed this problem into a single-objective optimization problem using a single metric to evaluate multi-UAV trajectory planning methods. However, multi-UAV trajectory planning evolves into a many-objective optimization problem due to the complexity of the demand and the environment. Therefore, a multi-UAV cooperative trajectory planning model based on many-objective optimization is proposed to optimize trajectory distance, trajectory time, trajectory threat, and trajectory coordination distance costs of UAVs. The NSGA-III algorithm, which overcomes the problems of traditional trajectory planning, is used to solve the model. This paper also designs a segmented crossover strategy and introduces dynamic crossover probability in the crossover operator to improve the solving efficiency of the model and accelerate the convergence speed of the algorithm. Experimental results prove the effectiveness of the multi-UAV cooperative trajectory planning algorithm, thereby addressing different actual needs.
{"title":"Multi-UAV Cooperative Trajectory Planning Based on Many-Objective Evolutionary Algorithm","authors":"Hui Bai;Tian Fan;Yuan Niu;Zhihua Cui","doi":"10.23919/CSMS.2022.0006","DOIUrl":"10.23919/CSMS.2022.0006","url":null,"abstract":"The trajectory planning of multiple unmanned aerial vehicles (UAVs) is the core of efficient UAV mission execution. Existing studies have mainly transformed this problem into a single-objective optimization problem using a single metric to evaluate multi-UAV trajectory planning methods. However, multi-UAV trajectory planning evolves into a many-objective optimization problem due to the complexity of the demand and the environment. Therefore, a multi-UAV cooperative trajectory planning model based on many-objective optimization is proposed to optimize trajectory distance, trajectory time, trajectory threat, and trajectory coordination distance costs of UAVs. The NSGA-III algorithm, which overcomes the problems of traditional trajectory planning, is used to solve the model. This paper also designs a segmented crossover strategy and introduces dynamic crossover probability in the crossover operator to improve the solving efficiency of the model and accelerate the convergence speed of the algorithm. Experimental results prove the effectiveness of the multi-UAV cooperative trajectory planning algorithm, thereby addressing different actual needs.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/9841527/09841533.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44738922","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}
Xiaoning Shen;Jiaqi Lu;Xuan You;Liyan Song;Zhongpei Ge
A constrained multi-objective optimization model for the low-carbon vehicle routing problem (VRP) is established. A carbon emission measurement method considering various practical factors is introduced. It minimizes both the total carbon emissions and the longest time consumed by the sub-tours, subject to the limited number of available vehicles. According to the characteristics of the model, a region enhanced discrete multi-objective fireworks algorithm is proposed. A partial mapping explosion operator, a hybrid mutation for adjusting the sub-tours, and an objective-driven extending search are designed, which aim to improve the convergence, diversity, and spread of the non-dominated solutions produced by the algorithm, respectively. Nine low-carbon VRP instances with different scales are used to verify the effectiveness of the new strategies. Furthermore, comparison results with four state-of-the-art algorithms indicate that the proposed algorithm has better performance of convergence and distribution on the low-carbon VRP. It provides a promising scalability to the problem size.
{"title":"A Region Enhanced Discrete Multi-Objective Fireworks Algorithm for Low-Carbon Vehicle Routing Problem","authors":"Xiaoning Shen;Jiaqi Lu;Xuan You;Liyan Song;Zhongpei Ge","doi":"10.23919/CSMS.2022.0008","DOIUrl":"10.23919/CSMS.2022.0008","url":null,"abstract":"A constrained multi-objective optimization model for the low-carbon vehicle routing problem (VRP) is established. A carbon emission measurement method considering various practical factors is introduced. It minimizes both the total carbon emissions and the longest time consumed by the sub-tours, subject to the limited number of available vehicles. According to the characteristics of the model, a region enhanced discrete multi-objective fireworks algorithm is proposed. A partial mapping explosion operator, a hybrid mutation for adjusting the sub-tours, and an objective-driven extending search are designed, which aim to improve the convergence, diversity, and spread of the non-dominated solutions produced by the algorithm, respectively. Nine low-carbon VRP instances with different scales are used to verify the effectiveness of the new strategies. Furthermore, comparison results with four state-of-the-art algorithms indicate that the proposed algorithm has better performance of convergence and distribution on the low-carbon VRP. It provides a promising scalability to the problem size.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/9841527/09841528.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45050673","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 Differential Evolution (DE) algorithm, which is an efficient optimization algorithm, has been used to solve various optimization problems. In this paper, adaptive dimensional learning with a tolerance framework for DE is proposed. The population is divided into an elite subpopulation, an ordinary subpopulation, and an inferior subpopulation according to the fitness values. The ordinary and elite subpopulations are used to maintain the current evolution state and to guide the evolution direction of the population, respectively. The inferior subpopulation learns from the elite subpopulation through the dimensional learning strategy. If the global optimum is not improved in a specified number of iterations, a tolerance mechanism is applied. Under the tolerance mechanism, the inferior and elite subpopulations implement the restart strategy and the reverse dimensional learning strategy, respectively. In addition, the individual status and algorithm status are used to adaptively adjust the control parameters. To evaluate the performance of the proposed algorithm, six state-of-the-art DE algorithm variants are compared on the benchmark functions. The results of the simulation show that the proposed algorithm outperforms other variant algorithms regarding function convergence rate and solution accuracy.
{"title":"Adaptive Dimensional Learning with a Tolerance Framework for the Differential Evolution Algorithm","authors":"Wei Li;Xinqiang Ye;Ying Huang;Soroosh Mahmoodi","doi":"10.23919/CSMS.2022.0001","DOIUrl":"10.23919/CSMS.2022.0001","url":null,"abstract":"The Differential Evolution (DE) algorithm, which is an efficient optimization algorithm, has been used to solve various optimization problems. In this paper, adaptive dimensional learning with a tolerance framework for DE is proposed. The population is divided into an elite subpopulation, an ordinary subpopulation, and an inferior subpopulation according to the fitness values. The ordinary and elite subpopulations are used to maintain the current evolution state and to guide the evolution direction of the population, respectively. The inferior subpopulation learns from the elite subpopulation through the dimensional learning strategy. If the global optimum is not improved in a specified number of iterations, a tolerance mechanism is applied. Under the tolerance mechanism, the inferior and elite subpopulations implement the restart strategy and the reverse dimensional learning strategy, respectively. In addition, the individual status and algorithm status are used to adaptively adjust the control parameters. To evaluate the performance of the proposed algorithm, six state-of-the-art DE algorithm variants are compared on the benchmark functions. The results of the simulation show that the proposed algorithm outperforms other variant algorithms regarding function convergence rate and solution accuracy.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/9770077/09770099.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42811654","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}
Kangjia Qiao;Jing Liang;Boyang Qu;Kunjie Yu;Caitong Yue;Hui Song
To address complex single objective global optimization problems, a new Level-Based Learning Differential Evolution (LBLDE) is developed in this study. In this approach, the whole population is sorted from the best to the worst at the beginning of each generation. Then, the population is partitioned into multiple levels, and different levels are used to exert different functions. In each level, a control parameter is used to select excellent exemplars from upper levels for learning. In this case, the poorer individuals can choose more learning exemplars to improve their exploration ability, and excellent individuals can directly learn from the several best individuals to improve the quality of solutions. To accelerate the convergence speed, a difference vector selection method based on the level is developed. Furthermore, specific crossover rates are assigned to individuals at the lowest level to guarantee that the population can continue to update during the later evolutionary process. A comprehensive experiment is organized and conducted to obtain a deep insight into LBLDE and demonstrates the superiority of LBLDE in comparison with seven peer DE variants.
{"title":"Differential Evolution with Level-Based Learning Mechanism","authors":"Kangjia Qiao;Jing Liang;Boyang Qu;Kunjie Yu;Caitong Yue;Hui Song","doi":"10.23919/CSMS.2022.0004","DOIUrl":"10.23919/CSMS.2022.0004","url":null,"abstract":"To address complex single objective global optimization problems, a new Level-Based Learning Differential Evolution (LBLDE) is developed in this study. In this approach, the whole population is sorted from the best to the worst at the beginning of each generation. Then, the population is partitioned into multiple levels, and different levels are used to exert different functions. In each level, a control parameter is used to select excellent exemplars from upper levels for learning. In this case, the poorer individuals can choose more learning exemplars to improve their exploration ability, and excellent individuals can directly learn from the several best individuals to improve the quality of solutions. To accelerate the convergence speed, a difference vector selection method based on the level is developed. Furthermore, specific crossover rates are assigned to individuals at the lowest level to guarantee that the population can continue to update during the later evolutionary process. A comprehensive experiment is organized and conducted to obtain a deep insight into LBLDE and demonstrates the superiority of LBLDE in comparison with seven peer DE variants.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/9770077/09770098.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45087811","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}
To improve the accuracy of typhoon prediction, it is necessary to detect the internal structure of a typhoon. The motion model of a floating weather sensing node becomes the key to affect the channel frequency expansion performance and communication quality. This study proposes a floating weather sensing node motion modeling method based on the chaotic mapping. After the chaotic attractor is obtained by simulation, the position trajectory of the floating weather sensing node is obtained by space and coordinate conversion, and the three-dimensional velocity of each point on the position trajectory is obtained by multidimensional linear interpolation. On this basis, the established motion model is used to study the Doppler frequency shift, which is based on the software and physical platform. The software simulates the relative motion of the transceiver and calculates the Doppler frequency shift. The physical platform can add the Doppler frequency shift to the actual transmitted signal. The results show that this method can effectively reflect the influence of the floating weather sensing node motion on signal transmission. It is helpful to research the characteristics of the communication link and the design of a signal transceiver for typhoon detection to further improve the communication quality and to obtain more accurate interior structure characteristic data of a typhoon.
{"title":"Motion Model of Floating Weather Sensing Node for Typhoon Detection","authors":"Hui Lu;Xinyu Dong;Xianbin Cao","doi":"10.23919/CSMS.2021.0029","DOIUrl":"10.23919/CSMS.2021.0029","url":null,"abstract":"To improve the accuracy of typhoon prediction, it is necessary to detect the internal structure of a typhoon. The motion model of a floating weather sensing node becomes the key to affect the channel frequency expansion performance and communication quality. This study proposes a floating weather sensing node motion modeling method based on the chaotic mapping. After the chaotic attractor is obtained by simulation, the position trajectory of the floating weather sensing node is obtained by space and coordinate conversion, and the three-dimensional velocity of each point on the position trajectory is obtained by multidimensional linear interpolation. On this basis, the established motion model is used to study the Doppler frequency shift, which is based on the software and physical platform. The software simulates the relative motion of the transceiver and calculates the Doppler frequency shift. The physical platform can add the Doppler frequency shift to the actual transmitted signal. The results show that this method can effectively reflect the influence of the floating weather sensing node motion on signal transmission. It is helpful to research the characteristics of the communication link and the design of a signal transceiver for typhoon detection to further improve the communication quality and to obtain more accurate interior structure characteristic data of a typhoon.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9420428/9770077/09770101.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49090832","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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