A Novel Hybrid PSO-K-Means Clustering Algorithm Using Gaussian Estimation of Distribution Method and Lévy Flight

Gao, Hanjie; Li, Yintong; Kabalyants, Petr; Xu, Hao; Martı́nez-Béjar, Rodrigo

doi:10.1109/access.2020.3007498

Cited by 26 publications

(19 citation statements)

References 54 publications

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“…To discover global optimal clustering centres, several researchers proposed combining nature-inspired optimisation algorithms to optimise the given objective function [9]. Gao et al [10] proposed using particle swarm optimisation based on the Gaussian estimation of distribution method to update population information. Experimental results showed that the algorithm has high effectiveness and robustness.…”

Section: Introductionmentioning

confidence: 99%

Novel Automated K-means++ Algorithm for Financial Data Sets

Zhang

et al. 2021

Mathematical Problems in Engineering

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The K-means algorithm has been extensively investigated in the field of text clustering because of its linear time complexity and adaptation to sparse matrix data. However, it has two main problems, namely, the determination of the number of clusters and the location of the initial cluster centres. In this study, we propose an improved K-means++ algorithm based on the Davies-Bouldin index (DBI) and the largest sum of distance called the SDK-means++ algorithm. Firstly, we use the term frequency-inverse document frequency to represent the data set. Secondly, we measure the distance between objects by cosine similarity. Thirdly, the initial cluster centres are selected by comparing the distance to existing initial cluster centres and the maximum density. Fourthly, clustering results are obtained using the K-means++ method. Lastly, DBI is used to obtain optimal clustering results automatically. Experimental results on real bank transaction volume data sets show that the SDK-means++ algorithm is more effective and efficient than two other algorithms in organising large financial text data sets. The F-measure value of the proposed algorithm is 0.97. The running time of the SDK-means++ algorithm is reduced by 42.9% and 22.4% compared with that for K-means and K-means++ algorithms, respectively.

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Section: Introductionmentioning

confidence: 99%

Novel Automated K-means++ Algorithm for Financial Data Sets

Zhang

et al. 2021

Mathematical Problems in Engineering

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“…In this same year, the GLP-SOK algorithm provided better results than the classical or latest generation of clustering algorithms. GLPSOK implements the Gaussian distribution method and Lévy flight to help search for PSO [104].…”

Section: Pso Applicationsmentioning

confidence: 99%

PSO, a Swarm Intelligence-Based Evolutionary Algorithm as a Decision-Making Strategy: A Review

et al. 2022

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Companies are constantly changing in their organization and the way they treat information. In this sense, relevant data analysis processes arise for decision makers. Similarly, to perform decision-making analyses, multi-criteria and metaheuristic methods represent a key tool for such analyses. These analysis methods solve symmetric and asymmetric problems with multiple criteria. In such a way, the symmetry transforms the decision space and reduces the search time. Therefore, the objective of this research is to provide a classification of the applications of multi-criteria and metaheuristic methods. Furthermore, due to the large number of existing methods, the article focuses on the particle swarm algorithm (PSO) and its different extensions. This work is novel since the review of the literature incorporates scientific articles, patents, and copyright registrations with applications of the PSO method. To mention some examples of the most relevant applications of the PSO method; route planning for autonomous vehicles, the optimal application of insulin for a type 1 diabetic patient, robotic harvesting of agricultural products, hybridization with multi-criteria methods, among others. Finally, the contribution of this article is to propose that the PSO method involves the following steps: (a) initialization, (b) update of the local optimal position, and (c) obtaining the best global optimal position. Therefore, this work contributes to researchers not only becoming familiar with the steps, but also being able to implement it quickly. These improvements open new horizons for future lines of research.

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“…The global optimal control strategy can achieve the global optimization in offline states, which is widely studied at present. The most widely used intelligent optimization algorithms mainly include genetic algorithm [24][25][26], simulated annealing (SA) algorithm [27], particle swarm optimization (PSO) algorithm [14,[28][29][30][31][32], and other intelligent optimization algorithm strategies. In addition, the neural network, working condition recognition, machine learning and other technologies are used to design energy management strategies [33][34][35][36][37][38][39].…”

Section: A Litterature Reviewmentioning

confidence: 99%

An Improved Energy Management Strategy for 24t Heavy-Duty Hybrid Emergency Rescue Vehicle With Dual-Motor Torque Increasing

Xue

Pan

et al. 2021

IEEE Access

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Emergency rescue vehicles are special equipment for fire, flood, earthquake and other disasters. They need to have high efficiency and high mobility under various terrain and road conditions. The fuel economy of emergency rescue vehicles is very poor in climbing and complex terrain due to the heavy weight of vehicle chassis and upper loading. The dual-motor torque increasing hybrid power system designed in this paper greatly improves the dynamic performance and mobility of emergency rescue vehicles, and the use of dual-motor drive in steep slopes and complex terrain can avoid the engine working in low efficiency area. In order to further improve the fuel economy of vehicles under various working conditions, a fuzzy logic control strategy based on improved particle swarm optimization (PSO) is proposed in this paper. The torques of engine and dual-motor are reasonably distributed under different working conditions. A novel fuzzy parameter optimization mode is designed, which reduces the workload of optimization calculation and ensures the optimization accuracy. The off-line simulation and hardware-inthe-loop experiments are carried out for the dual-motor torque increasing hybrid power system model and the proposed improved energy control strategy. The results show that the fuzzy logic control strategy optimized by simulated annealing particle swarm optimization algorithm (SimuAPSO-FLC) has the best effect and meets the requirements of high efficiency, high mobility and stability of the emergency rescue vehicle.INDEX TERMS Energy management strategy, SimuAPSO-FLC, 24t heavy-load hybrid emergency rescue vehicle, dual-motor torque increasing

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A Novel Hybrid PSO-K-Means Clustering Algorithm Using Gaussian Estimation of Distribution Method and Lévy Flight

Cited by 26 publications

References 54 publications

Novel Automated K-means++ Algorithm for Financial Data Sets

Novel Automated K-means++ Algorithm for Financial Data Sets

PSO, a Swarm Intelligence-Based Evolutionary Algorithm as a Decision-Making Strategy: A Review

An Improved Energy Management Strategy for 24t Heavy-Duty Hybrid Emergency Rescue Vehicle With Dual-Motor Torque Increasing

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