Energy-efficient resource allocation for multiuser OFDMA system based on hybrid genetic simulated annealing

Xu, Lei; Zhou, Xun-zhao; Li, Qianmu; Zhang, Xiaofei

doi:10.1007/s00500-016-2047-8

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…In order to overcome the problem of the above solutions, an HGSA algorithm is developed in Reference 28by combining GA and SA. In HGSA, the GA is used to generate the initial population randomly, and then the children population is generated through crossover and mutation operations.…”

Section: Hgsa‐based Latency‐minimum Offloading Decisionmentioning

confidence: 99%

Latency‐minimum offloading decision and resource allocation for fog‐enabled Internet of Things networks

Wang

Chen

2020

Trans Emerging Tel Tech

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The rapid growth of the number of sensing devices enables computation offloading to be a promising solution to alleviate the burden of core network communication and provide low delay services, especially for those computation-intensive and delay-sensitive tasks. For meeting the processing requirements of these tasks sufficiently, a latency-minimum offloading decision and resource allocation scheme for fog-enabled Internet of Things (IoT) networks is developed in this article. Specifically, we formulate a joint optimization problem of the offloading decision, the local computation capability, and the computing resource allocation of fog node to minimize the task completion time with energy constraint, in which practically considers M/M/1 waiting queues in the wireless channel and fog node. To solve this mixed integer nonlinear programming problem with low complexity, we first calculate the optimal values of local computation capability and computing resource allocation of fog node by decomposing the original optimization problem into two independent subproblems. Subsequently, we propose a hybrid genetic simulated annealing-based latency-minimum offloading decision algorithm to optimize the offloading decision. Finally, the numerical results verify that our proposed scheme achieves significant advantages compared to other alternative schemes in terms of completion time and energy consumption, and they also confirm the advantage of convergence speed and quality.

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Section: Hgsa‐based Latency‐minimum Offloading Decisionmentioning

confidence: 99%

Latency‐minimum offloading decision and resource allocation for fog‐enabled Internet of Things networks

Wang

Chen

2020

Trans Emerging Tel Tech

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Multiobjective cooperative swarm intelligence algorithm for uplink resource allocation in LTE‐A networks

Moses¹,

Balakrishnan²

2019

Trans Emerging Tel Tech

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Nowadays, Long-Term Evolution Advanced (LTE-A) network is the primary innovation in 4G networks. The LTE-A networks convey exceptional data rates and low latency for few sorts of application. Sometimes, in LTE-A, the multiobjective uplink resource allocation is the perplexing optimization problem, and this problem is considered as 0-1 multiobjective knapsack trouble. In order to overcome this trouble, a multiobjective cooperative swarm intelligence algorithm for resource allocation is proposed in this paper. In our proposed work, hybrid firefly algorithm (FFA) and particle swarm optimization (PSO) algorithm are utilized to solve 0-1 multiobjective knapsack problem. Initially, a priority and urgency factor (urgency of packets)-based user ranking and quantifying scheme is designed for scheduling process. After the scheduling process, the resource allocations employ three objective functions, such as maximization of resource utilization, maximization of quality of service (QoS), and interference minimization. The optimization problem is overcome by using the hybrid FFA and PSO algorithm. The experimental outcomes demonstrate that it has the best QoS and less interference in the resource allocation in LTE-A network than state-of-art methods in the proposed strategy.Trans Emerging Tel Tech. 2019;30:e3748.wileyonlinelibrary.com/journal/ett

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Research on the application of search algorithm in computer communication network

Ai¹,

Chai²,

Zhang³

et al. 2022

Journal of Intelligent Systems

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This article mitigates the challenges of previously reported literature by reducing the operating cost and improving the performance of network. A genetic algorithm-based tabu search methodology is proposed to solve the link capacity and traffic allocation (CFA) problem in a computer communication network. An efficient modern super-heuristic search method is used to influence the fixed cost, delay cost, and variable cost of a link on the total operating cost in the computer communication network are discussed. The article analyses a large number of computer simulation results to verify the effectiveness of the tabu search algorithm for CFA problems and also improves the quality of solutions significantly compared with traditional Lagrange relaxation and subgradient optimization algorithms. The experimental results show that with the increase of the weighted coefficient of variable cost, the proportion of variable cost in the total cost increases from 10 to 35%. The growth is relatively slow, and the fixed cost is still the main component. In addition, due to the increase in the variable cost, the tabu search algorithm will also choose the link with large luxury to reduce the variable cost, which makes the fixed cost slightly increase, while the network delay cost and average delay slightly decrease. The proposed method, when compared with the genetic algorithm, has more advantages for large-scale or heavy-load networks.

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Energy-efficient resource allocation for multiuser OFDMA system based on hybrid genetic simulated annealing

Cited by 3 publications

References 25 publications

Latency‐minimum offloading decision and resource allocation for fog‐enabled Internet of Things networks

Latency‐minimum offloading decision and resource allocation for fog‐enabled Internet of Things networks

Multiobjective cooperative swarm intelligence algorithm for uplink resource allocation in LTE‐A networks

Research on the application of search algorithm in computer communication network

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