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A Developer-Focused Genetic algorithm for IoT Application Placement in the Computing Continuum

IEEE Transactions on Services Computing 2025年第3期18卷 1185-1198页

作者： Herrera, Juan Luis Moya, Alejandro Berrocal, Javier Murillo, Juan Manuel Navarro, Elena University of Bologna Department of Computer Science Engineering Italy University of Castilla-La Mancha LoUISE Research Group Spain University of Extremadura Department of Computer Science and Communications Engineering Spain

The rise of the Internet of Things (IoT) paradigm has led to an interest in applying it not only in tasks for the general public but also to stringent domains such as healthcare. However, the developers of these next-generation IoT applications must consider additional non-functional requirements related to the criticality of the processes they automate, such as low response times or low deployment costs, as well as technical constraints, which include organizational, legal and policy-related constraints on where data can be processed or stored. While the Computing Continuum paradigm emerges as a valuable alternative for placing such applications, identifying the deployments that satisfy all these requirements becomes a tough challenge. The NP-hard nature of the problem makes it impractical to manually find such a deployment, and traditional approaches fail to consider the technical constraints. In this paper, we present the Genetic algorithm for Application Placement (GAAP), an evolutionary computing-based meta-heuristic designed to help IoT application developers find deployments that satisfy their Quality of Service, business and technical constraints. Our evaluation of an Internet of Medical Things use case shows that GAAP supports larger scenarios than traditional approaches and gives IoT application developers more options while providing better scalability. © 2008-2012 IEEE.

关键词： Internet Of Things Microservice Architectures Quality Of Service Costs Time Factors Electrocardiography Biomedical Monitoring Blood Pressure Genetic algorithms Business Internet Of Things Computing Continuum Quality Of Service Service Placement Deployment Genetic algorithm Internet Of Things Internet Of Things Applications Response Time Service Quality Technical Constraints Deployment Cost Low Response Time Internet Of Medical Things Heuristic objective Function Replica Decision Variables Solution Space Quality Of Experience Internet Of Things Devices Pareto Front multi objective algorithm Capital Expenditure Crossover Operator Quality Of Service Requirements Operational Expenditure Set Of Hosts Workflow Dijkstras algorithm Non Dominated Solutions Average Response Time Pareto Optimal Solutions Response Cost multi objective Genetic algorithm

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Optimised planning of distribution network with photovoltaic system, battery storage, and DSTATCOM

Optimised planning of distribution network with photovoltaic...

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IET Renewable Power Generation

作者： Roy Ghatak, Sriparna Sannigrahi, Surajit Acharjee, Parimal School of Electrical Engineering KIIT University Bhubaneswar751024 India Electrical Engineering Department NIT Durgapur M. G. Avenue Durgapur713209 India

In this circumstance of global warming, energy market deregulation, and enormous load growth, distribution network entails a proficient strategy to maintain the reliability and efficiency of the power service. Incorporation of solar photovoltaic (PV) system and battery storage (BS) in coordination with distributed static compensator (DSTATCOM) is a competent and practical approach to alleviate the power quality and reliability concern. In this study, a comprehensive strategic model is presented to optimally deploy PV, BS, and DSTATCOM to maximise voltage profile improvement, reliability, economic, and ecological benefit of the network. An accurate and precise novel voltage profile improvement indicator namely network voltage profile improvement index is proposed. Benefit-cost ratio and environment benefit index are proposed to quantify economic and environmental benefits, respectively. Similarly, reliability indices such as expected energy not served are used to appraise the system reliability. A fuzzy based extended version of NSGA II is utilised for the optimal deployment of the devices considering security limits. The proposed method is tested on 33-bus and 69-bus distribution networks considering time variant practical load models and the obtained results validate the efficacy and efficiency of the proposed method when compared with other multi-objective algorithms. © 2018 The Institution of Engineering and Technology.

关键词： Battery storage Cost benefit analysis Deregulation Electric batteries Electric power distribution Global warming Photovoltaic cells Reliability Solar power generation Static synchronous compensators Economic and environmental benefits Energy market de regulation Expected energy not served multi objective algorithm Planning of distribution networks Solar photovoltaic system Voltage profile improvement Voltage profile improvement index

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Comparison of multiobjective algorithms for the Assembly Line Balancing Design Problem

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IFAC-PapersOnLine 2016年第12期49卷 313-318页

作者： Oesterle, Jonathan Amodeo, Lionel Fraunhofer Institute for Manufacturing Engineering and Automation Stuttgart70569 Germany University of Technology of Troyes Troyes10000 France

The proposed article presents a planning method for a mixed-model line under consideration of product, processes and resources alternatives and addresses shortcomings in the current literature. In order to compare product and process alternatives and the impact of the line balancing and equipment selection, a detailed cost model is developed. Four resolution methods, belonging to the family of evolutionary algorithms and swarm intelligence, are proposed. Based on the fine tuning of their parameter values through a Design of Experiment, all algorithms are compared in order to highlight their strengths and weaknesses. © 2016

关键词： Design of experiments algorithms Computer aided manufacturing Manufacture multiobjective optimization Assembly line balancing Design problems Economic design Equipment selection multi objective algorithm Optimization problems Planning method Resolution methods

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