An Optimized Breadth-First Search Algorithm for Routing in Optical Access Networks

Andrade, Gabriella; Thomas, D. H.

doi:10.1109/tla.2019.8931195

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…Other exact methods, including Depth-First Search (DFS) [23], Breadth-First Search (BFS) [24], and Branch and Bound (BB) [25], were also used to find competitive solutions, even not the best within a reasonable time. Furthermore, the big graph with a large group of variables or constraints is usually intractable by these algorithms [6].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Searching Time Allocation for Information Collection Under Cooperative Path Planning of Multiple UAVs

Liu

et al. 2022

IEEE Trans. Emerg. Top. Comput. Intell.

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Unmanned Aerial Vehicles (UAVs) play an essential role in information collection where routing and time scheduling are two critical factors and are considered sequently. Given the paths of UAVs, metaheuristic algorithms are explored to allocate searching time of UAVs, which are hard to guarantee the optimal solution for time allocation. In this paper, a novel searchinG timE allocatioN undEr coopeRative pAth pLaning (GENERAL) is proposed to solve the optimal solution of the time allocation given paths of UAVs. GENERAL adopts a semi-greedy construction and a repair procedure to initialize and amend the routing solutions during iterations. Motivated by the Newton's method from convex optimization domains, we introduce a new Perturbed Parametric Nonlinear Complementarity Problem function (PPNCP-function), which reformulates the time allocation problem as a smoothing system of equations according to Karush-Kuhn-Tucker (KKT) theorem. Then a smoothing Newton method is introduced to obtain the optimal time allocation solution with superlinear convergence. Experimental results empirically indicate the GENERAL's effectiveness compared to Ant Colony Optimization with Simulated Annealing (ACO-SA) and Genetic Algorithm (GA). Besides, some numerical results indicate that the smoothing Newton method with PPNCP function is promising. The emerging aspects of this paper include: 1) it integrates the path planning with time allocation problem for maximizing the information collection reward; 2) a new PPNCP-function and a smoothing Newton method have been proposed to solve the optimal time allocation under path planning of UAVs; 3) the theoretical convergence analysis of the smoothing Newton method has been provided in this paper.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal Searching Time Allocation for Information Collection Under Cooperative Path Planning of Multiple UAVs

Liu

et al. 2022

IEEE Trans. Emerg. Top. Comput. Intell.

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“…In regards to the presented service mapping algorithms, Merge Sort is employed at line 6 for the computational hardware sorting and at lines 15 and 21 as a part of the path calculation between blades in algorithm 1, as well as at lines 2, 7, 12, 18 and 25 in algorithm 2 for the same stated purposes. As for the path calculation, the Breadth-First Search (BFS) algorithm [19] is assumed to be employed as one of the leading path calculation algorithms in optical networks in cases in which all the candidate paths between a pair of source-destination nodes have to be computed. The computational complexity of the BFS algorithm is equal to O(|V| + |E|), being V and E the set of nodes and edges of the graph to be traversed.…”

Section: Case Studiesmentioning

confidence: 99%

On the Complexity of Configuration and Orchestration for Enabling Disaggregated Server Provisioning in Optical Composable Data Centres

Pages¹,

Agraz²,

Spadaro³

2022

J. Opt. Commun. Netw.

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Due to the limitations of traditional data center (DC) architectures, the concept of infrastructure disaggregation has been proposed. DC resources are separated into multiple blades to be exploited independently. As a result, composable DC (CDC) infrastructures are achieved, enhancing the modularity of resource provisioning. However, disaggregation introduces additional challenges that need to be carefully analyzed. One relates to the potential complexity increase on the orchestration and infrastructure configuration that need to be performed when provisioning resources to support services. This aspect is highly influenced by the distribution of resources at the physical infrastructure. As such, when analyzing the performance of a CDC, it becomes essential to also study the related operational complexity of the resource orchestration and configuration phases. Furthermore, the requirements of several tenant services may impose heterogeneous deployments over the shared physical infrastructure in the form of either disaggregated single-server or multi-server distributions. The associated orchestration/configuration cost is again highly influenced by the data plane architecture of the CDC. With these aspects in mind, in this paper, we provide a methodology for analysis of the complexity of resource orchestration for a service deployment and the associated configuration cost in optical CDCs, considering various service deployment setups. A selected set of CDC architectures found in the literature is employed to quantitatively illustrate how the data plane design and service deployment strategies affect the complexity of infrastructure configuration and resource orchestration.

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“…Utilizing a CNN-generated distribution, the algorithm overcomes RRT limitations for optimal solutions. Lopes et al [24] optimized Breadth-First Search (BFS) for selecting disjoint shortest paths in an optical access network. The optimized algorithm reduces computing time significantly, demonstrating improved efficiency.…”

Section: Introductionmentioning

confidence: 99%

Research on a Multi-Strategy Improved Sand Cat Swarm Optimization Algorithm for Three-Dimensional UAV Trajectory Path Planning

Liu,

Lu,

Yang

et al. 2024

WEVJ

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In response to the issues of premature convergence, lack of population diversity, and poor convergence accuracy in the traditional Sand Cat Swarm Optimization (SCSO) algorithm, a Multi-Strategy Improved SCSO (MISCSO) algorithm is proposed. Firstly, multiple population strategies are used to avoid premature convergence and falling into local optima traps. Secondly, a distribution estimation learning strategy is introduced to represent the relationships between individuals, using probability models to improve algorithm performance. Next, the diversity of candidate solutions in the elite pool is utilized to expand the search space and enhance the algorithm’s ability to avoid local solutions. Lastly, a Cauchy disturbance strategy is adopted to accelerate the convergence speed of the algorithm, thereby improving the search efficiency and convergence accuracy. The experimental results of CEC2017 tests show that the improved algorithm balances convergence speed and global search capabilities effectively. Finally, the algorithm is applied to actual drone path planning and compared with six other intelligent algorithms, demonstrating the practicality and effectiveness of the improved algorithm.

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An Optimized Breadth-First Search Algorithm for Routing in Optical Access Networks

Cited by 5 publications

References 6 publications

Optimal Searching Time Allocation for Information Collection Under Cooperative Path Planning of Multiple UAVs

Optimal Searching Time Allocation for Information Collection Under Cooperative Path Planning of Multiple UAVs

On the Complexity of Configuration and Orchestration for Enabling Disaggregated Server Provisioning in Optical Composable Data Centres

Research on a Multi-Strategy Improved Sand Cat Swarm Optimization Algorithm for Three-Dimensional UAV Trajectory Path Planning

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