Discrete Particle Swarm Optimization Algorithm for Data Clustering

Karthi, R.; Arumugam, S.; Kumar, Kamlesh

doi:10.1007/978-3-642-03211-0_7

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…The value of increasing or decreasing one satellite (the definition of optimization index B 1 ) is larger than the maximum value of optimization index B 2 . Modelling and Simulation in Engineering [20,21], and the main components of the DPSO algorithm are as follows.…”

Section: Mathematical Modelmentioning

confidence: 99%

Deployment of On-Orbit Service Vehicles Using a Fuzzy Adaptive Particle Swarm Optimization Algorithm

Xing-long

Liu

et al. 2021

Modelling and Simulation in Engineering

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On the basis that satellites given fixed count and orbit elements can be served in bounded time when an on-orbit serving mission order is set at any uncertain time in a given time interval, the deployment of on-orbit service vehicle (OSV) serving satellites becomes a complex multiple nested optimization problem, and the essence of deployment is to determine the count and orbit elements of OSVs. In consideration of the characteristics of this deployment problem, we propose a fuzzy adaptive particle swarm optimization (FAPSO) algorithm to solve this problem. First, on the basis of double pulse rendezvous hypothesis, a transfer optimization model of a single OSV serving multiple satellites is established based on genetic algorithm (GA), and this is used to compute the indexes of the subsequent two optimization models. Second, an assignment optimization model of OSVs is established based on the discrete particle swarm optimization (DPSO) algorithm, laying the foundation of the next optimization model. Finally, the FAPSO algorithm, which improves the performance of PSO algorithm by adjusting the inertia weight, is proposed to solve the deployment problem of multiple OSVs. The simulation results demonstrate that all optimization models in this study are feasible, and the FAPSO algorithm, which has a better convergence result than that obtained using the other optimization algorithms, can effectively solve the deployment problem of OSVs.

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Section: Mathematical Modelmentioning

confidence: 99%

Deployment of On-Orbit Service Vehicles Using a Fuzzy Adaptive Particle Swarm Optimization Algorithm

Xing-long

Liu

et al. 2021

Modelling and Simulation in Engineering

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“…Our analysis shows that adopting WOA-DD is easier than other preexisting schemes. In Figure 7, we have analyzed the proposed WOA-SDN algorithm with existing chaotic whale optimization algorithm (C-WOA) 27 and WOA 28 algorithm with several benchmark functions by considering the fitness value with the number of iterations. We discovered that when WOA is in assistance with the SDN controller, it outperforms.…”

Section: Analysis Of Woa With Different Benchmark Functionmentioning

confidence: 99%

A novel dynamic framework to detect DDoS in SDN using metaheuristic clustering

Shakil

Mohammed

Arul

et al. 2019

Trans Emerging Tel Tech

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“…This shortcoming is especially noticeable in applications to road information extraction. Some studies investigated the performance evaluation of well‐known PSO variants for data clustering (Omran et al., ; Karthi et al., ; Ouadfel et al., ). Samadzadegan and Saeedi () verified the potential of the PSO algorithm for the clustering of multidimensional lidar data in urban areas and suggested a reliable method that does not let the k ‐means converge to a local minimum.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, due to the increasing computational speed of computers, heuristics are used to solve clustering problems. Clustering techniques based on evolutionary computing and swarm intelligence algorithms, like PSO, have outperformed many classical methods of clustering (Karthi et al., ). The PSO clustering technique determines the optimum number of clusters without any prior knowledge about the dataset; this compensates for the fundamental limitation of k ‐means noted above.…”

Section: Introductionmentioning

confidence: 99%

Road vectorisation from high‐resolution imagery based on dynamic clustering using particle swarm optimisation

Ameri

Zoej

2015

The Photogrammetric Record

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This paper introduces an innovative automatic road‐vectorisation algorithm based on dynamic pixel clustering using particle swarm optimisation. A new cost function is designed to optimise the number and position of road keypoints and is capable of deriving road centrelines without considering geometric, spectral or topological characteristics in the road model. The algorithm is applied to different high‐resolution images (IKONOS, QuickBird and aerial photographs) and is evaluated with respect to RMSE, correctness and completeness. Moreover, a new quality parameter is defined to evaluate a “kinking” effect in roads. Extraction of different road shapes with an acceptable precision in both urban and rural environments proves the efficiency of the algorithm in yielding complete road networks.

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Discrete Particle Swarm Optimization Algorithm for Data Clustering

Cited by 7 publications

References 8 publications

Deployment of On-Orbit Service Vehicles Using a Fuzzy Adaptive Particle Swarm Optimization Algorithm

Deployment of On-Orbit Service Vehicles Using a Fuzzy Adaptive Particle Swarm Optimization Algorithm

A novel dynamic framework to detect DDoS in SDN using metaheuristic clustering

Road vectorisation from high‐resolution imagery based on dynamic clustering using particle swarm optimisation

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