Wei-Li Liu;Yixin Chen;Xiang Li;Jinghui Zhong;Rongjun Chen;Hu Jin
Guardrails commonly play a significant role in guiding pedestrians and managing crowd flow to prevent congestion in public places. However, existing methods of the guardrail layout mainly rely on manual design or mathematical models, which are not flexible or effective enough for crowd control in large public places. To address this limitation, this paper introduces a novel automated optimization framework for guidance guardrails based on a multi-objective evolutionary algorithm. The paper incorporates guidance signs into the guardrails and designs a coding-decoding scheme based on Gray code to enhance the flexibility of the guardrail layout. In addition to optimizing pedestrian passage efficiency and safety, the paper also considers the situation of pedestrian counterflow, making the guardrail layout more practical. Experimental results have demonstrated the effectiveness of the proposed method in alleviating safety hazards caused by potential congestion, as well as its significant improvements in passage efficiency and prevention of pedestrian counterflow.
{"title":"Automatic Optimization of Guidance Guardrail Layout Based on Multi-Objective Evolutionary Algorithm","authors":"Wei-Li Liu;Yixin Chen;Xiang Li;Jinghui Zhong;Rongjun Chen;Hu Jin","doi":"10.23919/CSMS.2024.0020","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0020","url":null,"abstract":"Guardrails commonly play a significant role in guiding pedestrians and managing crowd flow to prevent congestion in public places. However, existing methods of the guardrail layout mainly rely on manual design or mathematical models, which are not flexible or effective enough for crowd control in large public places. To address this limitation, this paper introduces a novel automated optimization framework for guidance guardrails based on a multi-objective evolutionary algorithm. The paper incorporates guidance signs into the guardrails and designs a coding-decoding scheme based on Gray code to enhance the flexibility of the guardrail layout. In addition to optimizing pedestrian passage efficiency and safety, the paper also considers the situation of pedestrian counterflow, making the guardrail layout more practical. Experimental results have demonstrated the effectiveness of the proposed method in alleviating safety hazards caused by potential congestion, as well as its significant improvements in passage efficiency and prevention of pedestrian counterflow.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 4","pages":"353-367"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820945","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918104","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}
Xiao Jing;Xin Pei;Pengpeng Xu;Yun Yue;Chunyang Han
Freeway logistics plays a pivotal role in economic development. Although the rapid development in big data and artificial intelligence motivates long-haul freeway logistics towards informatization and intellectualization, the transportation of bulk commodities still faces serious challenges arisen from dispersed freight demands and the lack of co-ordination among different operators. The present study thereby proposed intelligent algorithms for truck dispatching for freeway logistics. Specifically, our contributions include the establishment of mathematical models for full-truckload (FTL) and less-than-truckload (LTL) transportation modes, respectively, and the introduction of reinforcement learning with deep Q-networks tailored for each transportation mode to improve the decision-making in order acceptance and truck repositioning. Simulation experiments based on the real-world freeway logistics data collected in Guiyang, China show that our algorithms improved operational profitability substantially with a 76% and 30% revenue increase for FTL and LTL modes, respectively, compared with single-stage optimization. These results demonstrate the potential of reinforcement learning in revolutionizing freeway logistics and should lay a foundation for future research in intelligent logistics systems.
{"title":"Reinforcement Learning-Driven Intelligent Truck Dispatching Algorithms for Freeway Logistics","authors":"Xiao Jing;Xin Pei;Pengpeng Xu;Yun Yue;Chunyang Han","doi":"10.23919/CSMS.2024.0016","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0016","url":null,"abstract":"Freeway logistics plays a pivotal role in economic development. Although the rapid development in big data and artificial intelligence motivates long-haul freeway logistics towards informatization and intellectualization, the transportation of bulk commodities still faces serious challenges arisen from dispersed freight demands and the lack of co-ordination among different operators. The present study thereby proposed intelligent algorithms for truck dispatching for freeway logistics. Specifically, our contributions include the establishment of mathematical models for full-truckload (FTL) and less-than-truckload (LTL) transportation modes, respectively, and the introduction of reinforcement learning with deep Q-networks tailored for each transportation mode to improve the decision-making in order acceptance and truck repositioning. Simulation experiments based on the real-world freeway logistics data collected in Guiyang, China show that our algorithms improved operational profitability substantially with a 76% and 30% revenue increase for FTL and LTL modes, respectively, compared with single-stage optimization. These results demonstrate the potential of reinforcement learning in revolutionizing freeway logistics and should lay a foundation for future research in intelligent logistics systems.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 4","pages":"368-386"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820940","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918106","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}
Shuttle tankers scheduling is an important task in offshore oil and gas transportation process, which involves operating time window fulfillment, optimal transportation planning, and proper inventory management. However, conventional approaches like Mixed Integer Linear Programming (MILP) or meta heuristic algorithms often fail in long running time. In this paper, a Graph Pointer Network (GPN) based Hierarchical Curriculum Reinforcement Learning (HCRL) method is proposed to solve Shuttle Tankers Scheduling Problem (STSP). The model is trained to divide STSP into voyage and operation stages and generate routing and inventory management decisions sequentially. An asynchronous training strategy is developed to address the coupling between stages. Comparison experiments demonstrate that the proposed HCRL method achieves 12% shorter tour lengths on average compared to heuristic algorithms. Additional experiments validate its generalizability to unseen instances and scalability to larger instances.
{"title":"Graph Pointer Network Based Hierarchical Curriculum Reinforcement Learning Method Solving Shuttle Tankers Scheduling Problem","authors":"Xiaoyong Gao;Yixu Yang;Diao Peng;Shanghe Li;Chaodong Tan;Feifei Li;Tao Chen","doi":"10.23919/CSMS.2024.0017","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0017","url":null,"abstract":"Shuttle tankers scheduling is an important task in offshore oil and gas transportation process, which involves operating time window fulfillment, optimal transportation planning, and proper inventory management. However, conventional approaches like Mixed Integer Linear Programming (MILP) or meta heuristic algorithms often fail in long running time. In this paper, a Graph Pointer Network (GPN) based Hierarchical Curriculum Reinforcement Learning (HCRL) method is proposed to solve Shuttle Tankers Scheduling Problem (STSP). The model is trained to divide STSP into voyage and operation stages and generate routing and inventory management decisions sequentially. An asynchronous training strategy is developed to address the coupling between stages. Comparison experiments demonstrate that the proposed HCRL method achieves 12% shorter tour lengths on average compared to heuristic algorithms. Additional experiments validate its generalizability to unseen instances and scalability to larger instances.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 4","pages":"339-352"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820942","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918239","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}
Since the increasing demand for surgeries in hospitals, the surgery scheduling problems have attracted extensive attention. This study focuses on solving a surgery scheduling problem with setup time. First, a mathematical model is created to minimize the maximum completion time (makespan) of all surgeries and patient waiting time, simultaneously. The time by the fatigue effect is included in the surgery time, which is caused by doctors' long working time. Second, four mate-heuristics are optimized to address the relevant problems. Three novel strategies are designed to improve the quality of the initial solutions. To improve the convergence of the algorithms, seven local search operators are proposed based on the characteristics of the surgery scheduling problems. Third, Q-learning is used to dynamically choose the optimal local search operator for the current state in each iteration. Finally, by comparing the experimental results of 30 instances, the Q-learning based local search strategy's effectiveness is verified. Among all the compared algorithms, the improved artificial bee colony (ABC) with Q-learning based local search has the best competitiveness.
{"title":"Q-Learning Based Meta-Heuristics for Scheduling Bi-Objective Surgery Problems with Setup Time","authors":"Ruixue Zhang;Hui Yu;Adam Slowik;Kaizhou Gao","doi":"10.23919/CSMS.2024.0021","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0021","url":null,"abstract":"Since the increasing demand for surgeries in hospitals, the surgery scheduling problems have attracted extensive attention. This study focuses on solving a surgery scheduling problem with setup time. First, a mathematical model is created to minimize the maximum completion time (makespan) of all surgeries and patient waiting time, simultaneously. The time by the fatigue effect is included in the surgery time, which is caused by doctors' long working time. Second, four mate-heuristics are optimized to address the relevant problems. Three novel strategies are designed to improve the quality of the initial solutions. To improve the convergence of the algorithms, seven local search operators are proposed based on the characteristics of the surgery scheduling problems. Third, Q-learning is used to dynamically choose the optimal local search operator for the current state in each iteration. Finally, by comparing the experimental results of 30 instances, the Q-learning based local search strategy's effectiveness is verified. Among all the compared algorithms, the improved artificial bee colony (ABC) with Q-learning based local search has the best competitiveness.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 4","pages":"321-338"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820941","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918240","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 significant wave height prediction holds critical value for marine energy development, coastal infrastructure planning, and ensuring safety in maritime operations. The precision of such predictions carries substantial theoretical and practical weight. This survey delivers an exhaustive evaluation and integration of the latest studies and advances in the domain of significant wave height prediction, serving as a methodical guidepost for academicians. The study introduces an all-encompassing predictive framework for significant wave height, which not only integrates diverse established forecasting techniques but also paves the way for novel research trajectories and creative breakthroughs. The framework is structured into four principal layers, i.e., feature selection, basic prediction, data decomposition, and parameter optimization. The ensuing sections meticulously dissect the methodologies within these strata, elucidating their core concepts, distinctive features, merits, and constraints, and their applicability to significant wave height prediction. To wrap up, the study delves into fresh research inquiries and avenues pertinent to the discipline, thereby broadening the comprehension of significant wave height prediction. In essence, this scholarly article imparts critical knowledge beneficial to the realm of marine technology.
{"title":"Advance in Significant Wave Height Prediction: A Comprehensive Survey","authors":"Jinyuan Mo;Xianghan Wang;Shengjun Huang;Rui Wang","doi":"10.23919/CSMS.2024.0019","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0019","url":null,"abstract":"The significant wave height prediction holds critical value for marine energy development, coastal infrastructure planning, and ensuring safety in maritime operations. The precision of such predictions carries substantial theoretical and practical weight. This survey delivers an exhaustive evaluation and integration of the latest studies and advances in the domain of significant wave height prediction, serving as a methodical guidepost for academicians. The study introduces an all-encompassing predictive framework for significant wave height, which not only integrates diverse established forecasting techniques but also paves the way for novel research trajectories and creative breakthroughs. The framework is structured into four principal layers, i.e., feature selection, basic prediction, data decomposition, and parameter optimization. The ensuing sections meticulously dissect the methodologies within these strata, elucidating their core concepts, distinctive features, merits, and constraints, and their applicability to significant wave height prediction. To wrap up, the study delves into fresh research inquiries and avenues pertinent to the discipline, thereby broadening the comprehension of significant wave height prediction. In essence, this scholarly article imparts critical knowledge beneficial to the realm of marine technology.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 4","pages":"402-439"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820943","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918107","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}
Liangliang Sun;Xirang Hao;Yunpeng Li;Jinghan Xue;Natalia M. Matsveichuk;Yuri N. Sotskov;Qichun Zhang
The integrated process of steelmaking, continuous casting, and hot rolling (SM-CC-HR) covers the entire process from refining liquid steel to manufacturing semi-finished steel products. Its batch planning and scheduling are connected to the production contract plan at the upper level and the production process control at the lower level, which is the key to achieving efficient and full process steel manufacturing. Batch planning determines the location of the slabs to be produced in the converter, ladle, continuous casting machine, and hot rolling mill by combining production orders, process standards, and production conditions. Production scheduling, guided by batch planning, combines production performance indicators, process constraints, etc., to determine the equipment selection and start-stop times of specific production units at each process. The synergy of the two aims to optimize production profitability, energy consumption, efficiency, etc., through rational decisions, ensuring the efficiency and flexibility of the steel production process. However, due to the traditional “divide and conquer” management mode and the influence of many uncertain factors, it is difficult to ensure the flexible balance between the demand and capacity, as well as a reasonable matching of logistics and resources among the production processes that operate independently. Considering the uncertain environment and the integration of SM-CC-HR, this paper summarizes the research status of previous scholars from three aspects: mathematical modeling, model optimization, and algorithm optimization based on the Lagrangian framework. It discusses the research status of batch planning and scheduling methods for SM-CC-HR production based on the Lagrangian relaxation framework, analyzes the problems existing in current research, and points out the main research directions and important research contents in the future, in order to promote the research and application of batch planning and scheduling problems for SM-CC-HR under uncertain environments.
{"title":"A Review of Integrated Optimization Method of Batch Planning and Scheduling for Steelmaking-Continuous Casting-Hot Rolling Production Under Uncertain Environment Based on Lagrangian Relaxation Framework","authors":"Liangliang Sun;Xirang Hao;Yunpeng Li;Jinghan Xue;Natalia M. Matsveichuk;Yuri N. Sotskov;Qichun Zhang","doi":"10.23919/CSMS.2024.0018","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0018","url":null,"abstract":"The integrated process of steelmaking, continuous casting, and hot rolling (SM-CC-HR) covers the entire process from refining liquid steel to manufacturing semi-finished steel products. Its batch planning and scheduling are connected to the production contract plan at the upper level and the production process control at the lower level, which is the key to achieving efficient and full process steel manufacturing. Batch planning determines the location of the slabs to be produced in the converter, ladle, continuous casting machine, and hot rolling mill by combining production orders, process standards, and production conditions. Production scheduling, guided by batch planning, combines production performance indicators, process constraints, etc., to determine the equipment selection and start-stop times of specific production units at each process. The synergy of the two aims to optimize production profitability, energy consumption, efficiency, etc., through rational decisions, ensuring the efficiency and flexibility of the steel production process. However, due to the traditional “divide and conquer” management mode and the influence of many uncertain factors, it is difficult to ensure the flexible balance between the demand and capacity, as well as a reasonable matching of logistics and resources among the production processes that operate independently. Considering the uncertain environment and the integration of SM-CC-HR, this paper summarizes the research status of previous scholars from three aspects: mathematical modeling, model optimization, and algorithm optimization based on the Lagrangian framework. It discusses the research status of batch planning and scheduling methods for SM-CC-HR production based on the Lagrangian relaxation framework, analyzes the problems existing in current research, and points out the main research directions and important research contents in the future, in order to promote the research and application of batch planning and scheduling problems for SM-CC-HR under uncertain environments.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 4","pages":"387-401"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820944","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918105","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 lot-streaming flowshop scheduling problem with equal-size sublots (ELFSP) is a significant extension of the classic flowshop scheduling problem, focusing on optimize makespan. In response, an improved dynamic O-learning (IDQL) algorithm is proposed, utilizing makespan as feedback. To prevent blind search, a dynamic search strategy is introduced. Additionally, the Nawaz-Enscore-Ham (NEH) algorithm is employed to diversify solution sets, enhancing local optimality. Addressing the limitations of the dynamic $varepsilon$ -greedy strategy, the Glover operator complements local search efforts. Simulation experiments, comparing the IDQL algorithm with other intelligent algorithms, validate its effectiveness. The performance of the IDQL algorithm surpasses that of its counterparts, as evidenced by the experimental analysis. Overall, the proposed approach offers a promising solution to the complex ELFSP, showcasing its capability to efficiently minimize makespan and optimize scheduling processes in flowshop environments with equal-size sublots.
{"title":"Improved Dynamic Q-Learning Algorithm to Solve the Lot-Streaming Flowshop Scheduling Problem with Equal-Size Sublots","authors":"Ping Wang;Renato De Leone;Hongyan Sang","doi":"10.23919/CSMS.2024.0010","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0010","url":null,"abstract":"The lot-streaming flowshop scheduling problem with equal-size sublots (ELFSP) is a significant extension of the classic flowshop scheduling problem, focusing on optimize makespan. In response, an improved dynamic O-learning (IDQL) algorithm is proposed, utilizing makespan as feedback. To prevent blind search, a dynamic search strategy is introduced. Additionally, the Nawaz-Enscore-Ham (NEH) algorithm is employed to diversify solution sets, enhancing local optimality. Addressing the limitations of the dynamic \u0000<tex>$varepsilon$</tex>\u0000-greedy strategy, the Glover operator complements local search efforts. Simulation experiments, comparing the IDQL algorithm with other intelligent algorithms, validate its effectiveness. The performance of the IDQL algorithm surpasses that of its counterparts, as evidenced by the experimental analysis. Overall, the proposed approach offers a promising solution to the complex ELFSP, showcasing its capability to efficiently minimize makespan and optimize scheduling processes in flowshop environments with equal-size sublots.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 3","pages":"223-235"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10737161","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524111","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}
Wei Zhang;Hongtao Tang;Wenyi Wang;Mengzhen Zhuang;Deming Lei;Xi Vincent Wang
The casting production process typically involves single jobs and small batches, with multiple constraints in the molding and smelting operations. To address the discrete optimization challenge of casting production scheduling, this paper presents a multi-objective batch scheduling model for molding and smelting operations on unrelated batch processing machines with incompatible job families and non-identical job sizes. The model aims to minimise the makespan, number of batches, and average vacancy rate of sandboxes. Based on the genetic algorithm, virus optimization algorithm, and two local search strategies, a hybrid algorithm (GA-VOA-BMS) has been designed to solve the model. The GA-VOA-BMS applies a novel Batch First Fit (BFF) heuristic for incompatible job families to improve the quality of the initial population, adopting the batch moving strategy and batch merging strategy to further enhance the quality of the solution and accelerate the convergence of the algorithm. The proposed algorithm was then compared with multi-objective swarm optimization algorithms, namely NSGA-II, SPEA-II, and PESA-II, to evaluate its effectiveness. The results of the performance comparison indicate that the proposed algorithm outperforms the others in terms of both quality and stability.
{"title":"A Multi-Objective Hybrid Algorithm for the Casting Scheduling Problem with Unrelated Batch Processing Machine","authors":"Wei Zhang;Hongtao Tang;Wenyi Wang;Mengzhen Zhuang;Deming Lei;Xi Vincent Wang","doi":"10.23919/CSMS.2024.0011","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0011","url":null,"abstract":"The casting production process typically involves single jobs and small batches, with multiple constraints in the molding and smelting operations. To address the discrete optimization challenge of casting production scheduling, this paper presents a multi-objective batch scheduling model for molding and smelting operations on unrelated batch processing machines with incompatible job families and non-identical job sizes. The model aims to minimise the makespan, number of batches, and average vacancy rate of sandboxes. Based on the genetic algorithm, virus optimization algorithm, and two local search strategies, a hybrid algorithm (GA-VOA-BMS) has been designed to solve the model. The GA-VOA-BMS applies a novel Batch First Fit (BFF) heuristic for incompatible job families to improve the quality of the initial population, adopting the batch moving strategy and batch merging strategy to further enhance the quality of the solution and accelerate the convergence of the algorithm. The proposed algorithm was then compared with multi-objective swarm optimization algorithms, namely NSGA-II, SPEA-II, and PESA-II, to evaluate its effectiveness. The results of the performance comparison indicate that the proposed algorithm outperforms the others in terms of both quality and stability.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 3","pages":"236-257"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10737162","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524224","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}
Zhenglong Chong;Cong Wang;Hongli Zhang;Ping Ma;Xinkai Li
The design of a chaotic image encryption algorithm plays an essential role in enhancing information and communication security. The performance of such algorithms is intricately linked to the complexity of the chaotic sequence and the underlying encryption algorithm. To additionally enhance the complexity of hyperchaotic systems, this study presents a novel construction of a Five-Dimensional (5D) memristive hyperchaotic system through the introduction of the flux-controlled memristor model. The system's dynamic characteristics are examined through various analytical methods, including phase portraits, bifurcation diagrams, and Lyapunov exponent spectra. Accordingly, the sequences produced by the hyperchaotic system, which passed the National Institute of Standards and Technology (NIST) test, are employed to inform the creation of a novelty image encryption technique that combines hash function, Deoxyribonucleic Acid (DNA) encoding, logistic, and Two-Dimensional Hyperchaotic Map (2D-SFHM). It improves the sensitivity of key and plaintext images to image encryption, expands the algorithm key space, and increases the complexity of the encryption algorithm. Experimental findings and analysis validate the exceptional encryption capabilities of the novel algorithm. The algorithm exhibits a considerable key space 2 512 , and the ciphertext image demonstrates an information entropy of 7.9994, with inter-pixel correlation approaching zero, etc., showcasing its resilience against different types of attacks on images.
{"title":"Image Encryption Algorithm Based on a Hybrid Model of Novel Memristive Hyperchaotic Systems, DNA Coding, and Hash Functions","authors":"Zhenglong Chong;Cong Wang;Hongli Zhang;Ping Ma;Xinkai Li","doi":"10.23919/CSMS.2024.0015","DOIUrl":"https://doi.org/10.23919/CSMS.2024.0015","url":null,"abstract":"The design of a chaotic image encryption algorithm plays an essential role in enhancing information and communication security. The performance of such algorithms is intricately linked to the complexity of the chaotic sequence and the underlying encryption algorithm. To additionally enhance the complexity of hyperchaotic systems, this study presents a novel construction of a Five-Dimensional (5D) memristive hyperchaotic system through the introduction of the flux-controlled memristor model. The system's dynamic characteristics are examined through various analytical methods, including phase portraits, bifurcation diagrams, and Lyapunov exponent spectra. Accordingly, the sequences produced by the hyperchaotic system, which passed the National Institute of Standards and Technology (NIST) test, are employed to inform the creation of a novelty image encryption technique that combines hash function, Deoxyribonucleic Acid (DNA) encoding, logistic, and Two-Dimensional Hyperchaotic Map (2D-SFHM). It improves the sensitivity of key and plaintext images to image encryption, expands the algorithm key space, and increases the complexity of the encryption algorithm. Experimental findings and analysis validate the exceptional encryption capabilities of the novel algorithm. The algorithm exhibits a considerable key space 2\u0000<sup>512</sup>\u0000, and the ciphertext image demonstrates an information entropy of 7.9994, with inter-pixel correlation approaching zero, etc., showcasing its resilience against different types of attacks on images.","PeriodicalId":65786,"journal":{"name":"复杂系统建模与仿真(英文)","volume":"4 3","pages":"303-319"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10737163","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524110","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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