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Multi-Container Migration Strategy Optimization for Industrial Robotics Workflow Based on Hybrid Tabu-evolutionary Algorithm

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IEEE TRANSACTIONS ON SERVICES computing 2024年第5期17卷 2640-2653页

作者： Xie, Xingju Wu, Xiaojun Hu, Qiao Yuan, Sheng Xi An Jiao Tong Univ Sch Software Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Shaanxi Key Lab Intelligent Robots Xian 710049 Peoples R China

Industrial Robot Monitoring System (IRMS) is an important guarantee to maintain the normal operation of industrial robot systems. For IRMSs in the edge-cloud environment, live migration technology enables them to improve system resource utilization and reliability such as dynamic resource management or fault tolerance without interrupting monitoring services. Therefore, it is important to research the optimization of live migration for IRMS. For multi-container migration, parallel migration can reduce service downtime, serial migration can reduce pre-copy migration time, and hybrid migration with a reasonable serial-parallel relationship can combine the advantages of both. In this paper, we propose a multi-container migration architecture based on shared bandwidth, which considers the resource-constrained characteristics of the edge-cloud environment. Moreover, we present a multi-container hybrid migration planning model with the total migration time as the optimization objective, which uses a matrix representation of serial-parallel relationship. To solve this model, we develop a heuristic algorithm based on a hybrid Tabu-evolutionary algorithm. The algorithm can find the dominant solution quickly by global search and improve the solution quality by subspace search. The experimental results show that the proposed algorithm can quickly give the hybrid migration strategy for a set of containers, effectively reducing the total migration time.

关键词： Containers Monitoring Bandwidth Cloud computing Computer architecture Optimization Industrial robots container migration edge computing evolutionary computing and genetic algorithms hybrid migration industrial robot monitoring system live migration optimization scheduling

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A secure and scalable blockchain-based model for electronic health record management

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SCIENTIFIC REPORTS 2025年第1期15卷 1-30页

作者： Pampattiwar, Kalyani Chavan, Pallavi DY Patil Deemed Be Univ SIES Grad Sch Technol Navi Mumbai 400706 Maharashtra India DY Patil Deemed be Univ Ramrao Adik Inst Technol Navi Mumbai 400706 Maharashtra India DY Patil Deemed be Univ Ramrao Adik Inst Technol Dept Informat Technol Navi Mumbai 400706 Maharashtra India

In the contemporary digital era, the storage of Electronic Health Records on open platforms presents significant security and privacy challenges. Addressing these concerns requires standardizing the clinical deployment models currently in use. This paper proposes a robust model that overcomes critical issues related to security, privacy, access control, and ownership transfer of patients' records. The model incorporates data collection to assess clinical needs, followed by deployment-level checks to mitigate network attacks and enhance Quality-of-Service according to scalability demands. A Modified genetic Algorithm is employed to improve blockchain scalability. The model also introduces mechanisms for ensuring database integrity, mitigating external attacks, and enhancing usability. It further supports platform-level modularization, access control, department-specific configurations, and patient-level confidentiality. The proposed solution outperforms existing systems, particularly in terms of ease of use, deployment delay, deployment complexity, and module-level efficiency, making it a highly suitable option for implementing secure, customized clinic security systems based on Electronic Health Records

关键词： Electronic health record Deployment Security and privacy protection Access control evolutionary computing and genetic algorithms Usage experience

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InterEvo-TR: Interactive evolutionary Test Generation With Readability Assessment

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2023年第4期49卷 2580-2596页

作者： Delgado-Perez, Pedro Ramirez, Aurora Valle-Gomez, Kevin J. Medina-Bulo, Inmaculada Romero, Jose Raul Univ Cadiz Cadiz 11003 Spain Univ Cordoba Cordoba 14071 Spain

Automated test case generation has proven to be useful to reduce the usually high expenses of software testing. However, several studies have also noted the skepticism of testers regarding the comprehension of generated test suites when compared to manually designed ones. This fact suggests that involving testers in the test generation process could be helpful to increase their acceptance of automatically-produced test suites. In this paper, we propose incorporating interactive readability assessments made by a tester into EvoSuite, a widely-known evolutionary test generation tool. Our approach, InterEvo-TR, interacts with the tester at different moments during the search and shows different test cases covering the same coverage target for their subjective evaluation. The design of such an interactive approach involves a schedule of interaction, a method to diversify the selected targets, a plan to save and handle the readability values, and some mechanisms to customize the level of engagement in the revision, among other aspects. To analyze the potential and practicability of our proposal, we conduct a controlled experiment in which 39 participants, including academics, professional developers, and student collaborators, interact with InterEvo-TR. Our results show that the strategy to select and present intermediate results is effective for the purpose of readability assessment. Furthermore, the participants' actions and responses to a questionnaire allowed us to analyze the aspects influencing test code readability and the benefits and limitations of an interactive approach in the context of test case generation, paving the way for future developments based on interactivity.

关键词： Test pattern generators Search problems Optimization Codes Task analysis Statistics Sociology evolutionary computing and genetic algorithms interactive search-based software engineering readability testing tools

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Local Optimization Often is Ill-conditioned in genetic Programming for Symbolic Regression 24

Local Optimization Often is Ill-conditioned in Genetic Progr...

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24th International Symposium on Symbolic and Numeric algorithms for Scientific computing (SYNASC)

作者： Kronberger, Gabriel Univ Appl Sci Upper Austria Josef Ressel Ctr Symbol Regress Heurist & Evolutionary Algorithms Lab Hagenberg Campus Muhlkreis Austria

ISBN: (纸本)9781665465458

Gradient-based local optimization has been shown to improve results of genetic programming (GP) for symbolic regression. Several state-of-the-art GP implementations use iterative nonlinear least squares (NLS) algorithms such as the Levenberg-Marquardt algorithm for local optimization. The effectiveness of NLS algorithms depends on appropriate scaling and conditioning of the optimization problem. This has so far been ignored in symbolic regression and GP literature. In this study we use a singular value decomposition of NLS Jacobian matrices to determine the numeric rank and the condition number. We perform experiments with a GP implementation and six different benchmark datasets. Our results show that rank-deficient and ill-conditioned Jacobian matrices occur frequently and for all datasets. The issue is less extreme when restricting GP tree size and when using many non-linear functions in the function set.

关键词： evolutionary computing and genetic algorithms Nonlinear approximation Least squares methods Gradient methods

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Evolving Bipartite Authentication Graph Partitions

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE computing 2019年第1期16卷 58-71页

作者： Pope, Aaron Tauritz, Daniel Kent, Alexander Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65409 USA Los Alamos Natl Lab Los Alamos NM 87545 USA

As large scale enterprise computer networks become more ubiquitous, finding the appropriate balance between user convenience and user access control is an increasingly challenging proposition. Suboptimal partitioning of users' access and available services contributes to the vulnerability of enterprise networks. Previous edge-cut partitioning methods unduly restrict users' access to network resources. This paper introduces a novel method of network partitioning superior to the current state-of-the-art which minimizes user impact by providing alternate avenues for access that reduce vulnerability. Networks are modeled as bipartite authentication access graphs and a multi-objective evolutionary algorithm is used to simultaneously minimize the size of large connected components while minimizing overall restrictions on network users. Results are presented on a real world data set that demonstrates the effectiveness of the introduced method compared to previous naive methods.

关键词： evolutionary computing and genetic algorithms network-level security and protection authentication graph theory

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Integrating Agent Actions with genetic Action Sequence Method 19

Integrating Agent Actions with Genetic Action Sequence Metho...

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genetic and evolutionary Computation Conference (GECCO)

作者： Kim, Man-Je Kim, Jun Suk Lee, Donghyeon Kim, Sungjin James Kim, Min-Jung Ahn, Chang Wook Gwangju Inst Sci & Technol Gwangju South Korea LG Elect Seoul South Korea

ISBN: (纸本)9781450367486

Reinforcement learning in general is suitable for putting actions in a specific order within a short sequence, but in the long run its greedy nature leads to eventual incompetence. This paper presents a brief description and implementative analysis of Action Sequence which was designed to deal with such a "penny-wise and pound-foolish" problem. Based on a combination of genetic operations and Monte-Carlo tree search, our proposed method is expected to show improved computational efficiency especially on problems with high complexity, in which situational difficulties are often troublesome to resolve with naive behaviors. We tested the method on a video game environment to validate its overall performance.

关键词： Artificial Intelligence evolutionary computing and genetic algorithms Video Game

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Multi-Objective Optimization for Virtual Machine Allocation and Replica Placement in Virtualized Hadoop

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2018年第11期29卷 2568-2581页

作者： Guerrero, Carlos Lera, Isaac Bermejo, Belen Juiz, Carlos Balearic Isl Univ Comp Sci Dept E-07122 Palma De Mallorca Spain

Resource management is a key factor in the performance and efficient utilization of cloud systems, and many research works have proposed efficient policies to optimize such systems. However, these policies have traditionally managed the resources individually, neglecting the complexity of cloud systems and the interrelation between their elements. To illustrate this situation, we present an approach focused on virtualized Hadoop for a simultaneous and coordinated management of virtual machines and file replicas. Specifically, we propose determining the virtual machine allocation, virtual machine template selection, and file replica placement with the objective of minimizing the power consumption, physical resource waste, and file unavailability. We implemented our solution using the non-dominated sorting genetic algorithm-II, which is a multi-objective optimization algorithm. Our approach obtained important benefits in terms of file unavailability and resource waste, with overall improvements of approximately 400 and 170 percent compared to three other optimization strategies. The benefits for the power consumption were smaller, with an improvement of approximately 1.9 percent.

关键词： Virtual machine allocation file replica placement hadoop evolutionary computing and genetic algorithms

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Adaptive α-stable differential evolution in numerical optimization (vol 16, pg 637, 2017)

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NATURAL computing 2017年第4期16卷 659-659页

作者： Choi, Tae Jong Ahn, Chang Wook Sungkyunkwan Univ Dept Comp Engn 2066 Seobu Ro Suwon Gyeonggi Do South Korea GIST Sch Elect Engn & Comp Sci 123 Cheomdangwagi Ro Gwangju 61005 South Korea

Although Differential Evolution (DE) is a simple yet powerful evolutionary algorithm, it requires an adaptive parameter control to achieve its optimal performance. In this paper, DE with an adaptive parameter control using the $$\alpha$$ -stable distribution is proposed. First, the proposed algorithm allocated a carefully calculated stable distribution, evaluated by an adaptation manner, to each individual. After that, each individual adjusts its own control parameters by using the assigned stable distribution. Thus, we propose a parameter control scheme that adapts the stability parameter of the $$\alpha$$ -stable distribution to allocate proper stable distributions to each individual, used for tuning control parameters. We compared the optimization performances of the proposed algorithm with conventional DE and state-of-the-art DE variants at 30 and 100 dimensions of conventional benchmark problems. Also, we evaluated the optimization performances at high dimensional problems i.e., 100, 200, and 300 dimensions of CEC2008 benchmark problems. Our experiment results showed that the proposed algorithm is able to discover better final solutions than the compared DE algorithms and has the robust performance at both lower and higher dimensions.

关键词： Artificial intelligence evolutionary computing and genetic algorithms Global optimization

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Minimizing Fatigue Damage in Aircraft Structures

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IEEE INTELLIGENT SYSTEMS 2016年第4期31卷 22-29页

作者： Ruotsalainen, Marja Jylha, Juha Visa, Ari Tampere Univ Technol Ind Projects FIN-33101 Tampere Finland Tampere Univ Technol PhD Comp & Elect Engn FIN-33101 Tampere Finland Tampere Univ Technol Dept Signal Proc FIN-33101 Tampere Finland

Aircraft structural health monitoring refers to a process in which sensors assess structural state in terms of aging and deterioration. A knowledge discovery approach based on dynamic time warping and genetic programm... 详细信息

关键词： Aerospace Safety Aircraft Condition Monitoring Data Mining Fatigue genetic algorithms Mechanical Engineering computing Socio Economic Effects Fatigue Damage Minimization Aircraft Structures Aircraft Structural Health Monitoring SHM Flight Safety Economic Considerations Knowledge Discovery Approach Dynamic Time Warping genetic Programming Artificial Intelligence Aircraft Fleet Aircraft Military Aircraft Structural Engineering Artificial Intelligence Monitoring Aerospace Electronics Wireless Sensor Networks computing Methodologies Artificial Intelligence Intelligent Systems Applications And Expert Knowledge Intensive Systems Decision Support Machine Learning Pattern Recognition Engineering evolutionary computing and genetic algorithms

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Accurate and Efficient Estimation of Logic Circuits Reliability Bounds

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IEEE TRANSACTIONS ON COMPUTERS 2015年第5期64卷 1217-1229页

作者： Ibrahim, Walid Shousha, Marwa Chinneck, John W. UAE Univ Coll Informat Technol Abu Dhabi U Arab Emirates Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada

As the sizes of CMOS devices rapidly scale deep into the nanometer range, the manufacture of nanocircuits will become extremely complex and will inevitably introduce more defects, including more transient faults that appear during operation. For this reason, accurately calculating the reliability of future designs will be extremely critical for nanocircuit designers as they investigate design alternatives to optimize the tradeoffs between area-power-delay and reliability. However, accurate calculation of the reliability of large and highly connected circuits is complex and very time consuming. This paper presents a complete solution for estimating logic circuit reliability bounds with high accuracy in reasonable time, even for very large and complex circuits. The solution combines a novel criticality scoring algorithm to rank the reliability of individual input vectors with a heuristic search to find the input vector having the lowest reliability. The solution scales well with circuit size, and is independent of the interconnect complexity or the logic depth. Extensive computational results show that the speed of our method is orders of magnitude faster than exact solutions provided by Bayesian network exact inferences, while maintaining identical or sufficiently close accuracy.

关键词： Reliability optimization heuristics design inference engines worst-case analysis evolutionary computing and genetic algorithms simulated annealing

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