Genetic Algorithm based Edge Computing Scheduling Strategy

Su, Ching‐Tzong; Gang, Yining; Jin, Chi

doi:10.1145/3478905.3478932

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…The entire process requires close coordination and cooperation, the use of professional equipment, and temperature monitoring systems to ensure that the goods remain at suitable frozen conditions throughout the supply chain, ensuring their quality, safety, and freshness [4].…”

Section: Operation Flow Of Cold Chain Logisticsmentioning

confidence: 99%

Optimization analysis of cold chain logistics network structure based on genetic algorithm

wan,

zheng

2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

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This paper presents an optimization analysis of the cold chain logistics network structure based on genetic algorithms. Firstly, the theoretical overview of the cold chain logistics network is introduced, including the concept, characteristics, and operation process of cold chain logistics, as well as the functional effects of logistics networks. Then, the structure of the cold chain logistics network is studied, including its composition and link analysis, and the formation mechanism of the cold chain logistics network is discussed. Subsequently, the genetic algorithm and its basic ideas and solution steps are explained in detail, and a genetic algorithm-based optimization model for the layout of cold chain logistics network nodes is constructed. The feasibility and effectiveness of the model are verified through case analysis. Finally, the research results of the paper are summarized, emphasizing the importance of optimizing the structure of the cold chain logistics network to improve operational efficiency. The research findings of this paper are of great significance for promoting the development of the cold chain logistics industry.

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Section: Operation Flow Of Cold Chain Logisticsmentioning

confidence: 99%

Optimization analysis of cold chain logistics network structure based on genetic algorithm

wan,

zheng

2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

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“…It can be integrated with other architectural-related software and systems, facilitating data exchange and sharing, thereby improving work efficiency and collaboration. In summary, BIM offers advantages of comprehensiveness, collaboration, visualization, data-driven approach, whole-lifecycle management, and integration, which promote innovation and development in the construction industry [5].…”

Section: Definition and Characteristics Of Bimmentioning

confidence: 99%

Energy-saving optimization design of multi-story office buildings based on genetic algorithm

Zhang

2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

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With the growing global energy consumption issue and increasing demand for environmental protection, energy-efficient design for office buildings has become an important topic in the construction industry. The application of genetic algorithm and Building Information Modeling (BIM) technology offers new perspectives and methods to address this issue. This paper systematically presents the theory of energy-efficient design for office buildings based on genetic algorithms, defines and discusses the parameters of genetic algorithms, and explores the application of BIM technology in energy-efficient design. Subsequently, an optimized design method and process for energy-efficient office buildings based on BIM are proposed, including two key components: energy consumption analysis and economic analysis. Based on this, a methodology for constructing multi-story office building energy-efficient design schemes is presented, and the optimal design scheme is determined using the performance coefficient method. Finally, the energy-saving effectiveness, economic feasibility, and practicality of the optimal design scheme are evaluated through an analysis of the performance coefficient method. This research provides theoretical guidance and practical application methods for energy-efficient design of office buildings, contributing to the development of sustainable cities.

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“…In this case, we designed a hybrid approach by combining the GA [38] with the FSA [39], which we call GA-FSA. The proposed model efficiently schedules tasks to fog/cloud nodes to meet the QoS and minimize cost.…”

Section: Proposed Mechanismmentioning

confidence: 99%

“…GA is a popular evolutionary algorithm widely used in different optimization problems [38]. This algorithm was inspired by Darwin's evolutionary principle based on gene simulations.…”

Section: Proposed Mechanismmentioning

confidence: 99%

Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

Alwabel,

Swain

2024

CMC

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Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data sources. However, the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes, thus making the application placement problem more complex than that in cloud computing. An approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the placement of applications and services while minimizing costs. This approach is particularly relevant in scenarios where latency, resource constraints, and cost considerations are crucial factors for the deployment of applications. In this study, we propose a hybrid approach that combines a genetic algorithm (GA) with the Flamingo Search Algorithm (FSA) to place application modules while minimizing cost. We consider four cost-types for application deployment: Computation, communication, energy consumption, and violations. The proposed hybrid approach is called GA-FSA and is designed to place the application modules considering the deadline of the application and deploy them appropriately to fog or cloud nodes to curtail the overall cost of the system. An extensive simulation is conducted to assess the performance of the proposed approach compared to other state-of-the-art approaches. The results demonstrate that GA-FSA approach is superior to the other approaches with respect to task guarantee ratio (TGR) and total cost.

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Genetic Algorithm based Edge Computing Scheduling Strategy

Cited by 5 publications

References 21 publications

Optimization analysis of cold chain logistics network structure based on genetic algorithm

Optimization analysis of cold chain logistics network structure based on genetic algorithm

Energy-saving optimization design of multi-story office buildings based on genetic algorithm

Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

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