A Novel Hybrid Clustering Analysis Based on Combination of K-Means and PSO Algorithm

Krishna, Eluri Rama; Devarakonda, Nagaraju; Al-Shamri, Mohammad Yahya H.; Revathi, D.

doi:10.1007/978-981-16-6460-1_10

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…In 2022 their Krishna and colleagues [59] presented a hybrid clustering algorithm that synergistically combines Particle Swarm Optimization (PSO) with the K-means algorithm. This research addresses the challenge of optimizing both global and local search strategies in clustering tasks.…”

Section: Related Workmentioning

confidence: 99%

SA-PSO-GK++: A New Hybrid Clustering Approach for Analyzing Medical Data

Abdo,

Abdelkader,

Abdel-Hamid

2024

IEEE Access

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Data clustering is an unsupervised learning task that has been extensively studied, given its wide applicability in various domains. Traditional algorithms often struggle to achieve a balance between exploration and exploitation, leading to sub-optimal solutions. This paper presents a novel hybrid algorithm named SA-PSO-GK++ that synergistically combines Particle Swarm Optimization (PSO), K-means++, Simulated Annealing (SA), and Gaussian Estimation of Distribution to tackle this issue effectively. The proposed SA-PSO-GK++ aims to overcome the drawbacks of existing methods by leveraging the strengths of each individual algorithm. The K-means++ initialization reduces the risk of poor initial centroids, while PSO aids in efficient search space exploration. GED provides a statistical model of the particle space, enabling the algorithm to generate new potential solutions that are statistically guided by the current best solutions. Additionally, the incorporation of Simulated Annealing allows the algorithm to escape local minima, thereby enhancing its global search capability. We evaluate the effectiveness of SA-PSO-GK++ using benchmark datasets from the UCI Machine Learning Repository, including the Iris, Breast cancer, Heart datasets and contraceptive method choice datasets. The proposed method outperforms conventional and some of the state-of-the-art hybrid clustering algorithms in terms of sum of euclidean distance, normalized index, and error rates. These advantages make SA-PSO-GK++ a compelling option for a wide range of clustering applications. The results offer promising avenues for future research in optimizing and applying this innovative clustering technique in diverse domains.

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Section: Related Workmentioning

confidence: 99%

SA-PSO-GK++: A New Hybrid Clustering Approach for Analyzing Medical Data

Abdo,

Abdelkader,

Abdel-Hamid

2024

IEEE Access

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“…In particular, ECML (Zhang et al, 2022 ) also has strong search ability, which can greatly reduce the computational cost of cluster node analysis. In the actual construction of UAV cluster task allocation algorithm, genetic algorithm, particle swarm optimization algorithm (Lipare et al, 2021 ; Krishna et al, 2022 ; Pu et al, 2022 ) and ant colony algorithm are often introduced, and bionic intelligence is used to realize better cooperation between UAV cluster individuals, so as to achieve the overall task goal with the best effect (Wangsheng et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Trans-UTPA: PSO and MADDPG based multi-UAVs trajectory planning algorithm for emergency communication

Cao

Liu

et al. 2023

Front. Neurorobot.

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Communication infrastructure is damaged by disasters and it is difficult to support communication services in affected areas. UAVs play an important role in the emergency communication system. Due to the limited airborne energy of a UAV, it is a critical technical issue to effectively design flight routes to complete rescue missions. We fully consider the distribution of the rescue area, the type of mission, and the flight characteristics of the UAV. Firstly, according to the distribution of the crowd, the PSO algorithm is used to cluster the target-POI of the task area, and the neural collaborative filtering algorithm is used to prioritize the target-POI. Then we also design a Trans-UTPA algorithm. Based on MAPPO 's policy network and value function, we introduce transformer model to make Trans-UTPA's policy learning have no action space limitation and can be multi-task parallel, which improves the efficiency and generalization of sample processing. In a three-dimensional space, the UAV selects the emergency task to be performed (data acquisition and networking communication) based on strategic learning of state information (location information, energy consumption information, etc.) and action information (horizontal flight, ascent, and descent), and then designs the UAV flight path based on the maximization of the global value function. The experimental results show that the performance of the Trans-UTPA algorithm is further improved compared with the USCTP algorithm in terms of the success rate of each UAV reaching the target position, the number of collisions, and the average reward of the algorithm. Among them, the average reward of the algorithm exceeds the USCTP algorithm by 13%, and the number of collisions is reduced by 60%. Compared with the heuristic algorithm, it can cover more target-POIs, and has less energy consumption than the heuristic algorithm.

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