An ant colony optimization algorithm with evolutionary experience-guided pheromone updating strategies for multi-objective optimization

Zhao, Haitong; Zhang, Changsheng

doi:10.1016/j.eswa.2022.117151

Cited by 13 publications

(3 citation statements)

References 20 publications

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“…techniques, including metaheuristic algorithms, artificial intelligence (AI), 25,26 Software-Defined Networking (SDN), and Information-Centric Networking (ICN). Researchers are incorporating AI to construct intelligent algorithms that learn from network data, similar to learning from experience, resulting in networks that gradually become smarter, faster, and more trustworthy through real-time feedback.…”

Section: Notementioning

confidence: 99%

“…Researchers are constantly looking for ways to improve computer network routing in NDN and to develop new methodologies that allow networks to run better and more efficiently as technology progresses. They employ a variety of techniques, including metaheuristic algorithms, artificial intelligence (AI), 25,26 Software‐Defined Networking (SDN), and Information‐Centric Networking (ICN). Researchers are incorporating AI to construct intelligent algorithms that learn from network data, similar to learning from experience, resulting in networks that gradually become smarter, faster, and more trustworthy through real‐time feedback.…”

Section: Future Recommendationsmentioning

confidence: 99%

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A review of SDN‐enabled routing protocols for Named Data Networking

Yassine,

Najib,

Abdellah

2024

Engineering Reports

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Named Data Networking is a novel network architecture for the future internet in which data is named rather than addressed. This enables routers to forward data more efficiently based on its name, outperforming IP‐based routing. Another networking architecture that divides the control plane and the data plane is software‐defined networking (SDN). In an SDN network, the control plane decides on routing while the data plane forwards data packets. This separation increases routing flexibility and scalability. Numerous techniques to improve routing can be achieved with NDN and SDN integration. This paper provides an in‐depth examination of routing approaches in NDN based on SDN, emphasizing design principles, algorithms, and performance measures. We begin by summarizing the NDN architecture and delving into its essential components. We next go into the core routing ideas in NDN and categorize and study several routing solutions based on Software Defined Networks. Finally, we highlight the need for scalable, effective, and secure routing systems that may satisfy the expanding requirements of the contemporary internet. We also suggest open research topics in NDN routing based on SDN. This review provides an extensive overview of current centralized routing approaches in NDN, including their limitations and future possibilities.

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Section: Notementioning

confidence: 99%

Section: Future Recommendationsmentioning

confidence: 99%

A review of SDN‐enabled routing protocols for Named Data Networking

Yassine,

Najib,

Abdellah

2024

Engineering Reports

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“…Optimization algorithms have developed rapidly. The common optimization algorithms have particle swarm optimization (PSO) [9], genetic algorithm (GA) [10], simulated annealing (SA) [11], etc. Reference [12] pointed out that when solving complex problems, particle swarm optimization shows a poor local search ability and a slow convergence speed.…”

Section: Introductionmentioning

confidence: 99%

Design of Gas Turbine Cooling System Based on Improved Jumping Spider Optimization Algorithm

Liu

Chen³

2022

Machines

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The gas turbine cooling system is a complex MIMO system with a strong coupling, nonlinear, time-varying and large disturbance amplitude. In order to automatically control the target flow, target temperature and pipeline pressure, in this paper, the decoupler and regulator of a gas turbine cooling system are designed. Firstly, the working principle of a gas turbine cooling system and the coupling between the controlled variables of the system are analyzed. The decoupler of the system is designed by using the diagonal matrix decoupling method. The transfer function models of the coupling system are built through system identification, and the decoupling matrix of the system is calculated according to the diagonal matrix decoupling method and transfer function models. Then, the engine cooling control system simulation model is constructed and an improved jumping spider optimization algorithm is proposed. The parameters of the controller are optimized by the improved jumping spider optimization algorithm. Finally, the control system simulation is done and compared with the jumping spider optimization algorithm and the particle swarm optimization algorithm. The simulation results show that the improved jumping spider optimization algorithm is more suitable for the multivariable strong coupling nonlinear engine cooling system. For the flow and pressure control, the transient time and overshoot are reduced, and the steady-state error is less than 1%. For the temperature control, the result of the improved jumping spider optimization algorithm is more smooth, without overshoot, and almost does not exceed the set inlet water temperature. The overshoot, steady-state errors and transient time of the system have been improved, which proves the feasibility and significance of the improved jumping spider optimization algorithm by comparing the control performance and optimization time.

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