Density-based particle swarm optimization algorithm for data clustering

Alswaitti, Mohammed; Albughdadi, Mohanad; Isa, Nor Ashidi Mat

doi:10.1016/j.eswa.2017.08.050

Cited by 96 publications

(26 citation statements)

References 36 publications

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“…Currently, PSO algorithms have been successfully applied to many problems, e.g., clustering [39], control [40], feature selection and image processing [41], [42]. There also exist a number of approaches that involve PSO for training fuzzy systems [43]- [45] and artificial neural networks [46].…”

Section: B Psomentioning

confidence: 99%

Particle Swarm Optimized Autonomous Learning Fuzzy System

Shen

Angelov

2021

IEEE Trans. Cybern.

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The antecedent and consequent parts of a first-order evolving intelligent system (EIS) determine the validity of the learning results and overall system performance. Nonetheless, the state-of-the-art techniques mostly stress on the novelty from the system identification point of view but pay less attention to the optimality of the learned parameters. Using the recently introduced autonomous learning multiple model (ALMMo) system as the implementation basis, this paper introduces a particle swarmbased approach for EIS optimization. The proposed approach is able to simultaneously optimize the antecedent and consequent parameters of ALMMo and effectively enhance the system performance by iteratively searching for optimal solutions in the problem spaces. In addition, the proposed optimization approach does not adversely influence the "one pass" learning ability of ALMMo. Once the optimization process is complete, ALMMo can continue to learn from new data to incorporate unseen data patterns recursively without a full retraining. Experimental studies with a number of real-world benchmark problems validate the proposed concept and general principles. It is also verified that the proposed optimization approach can be applied to other types of EISs with similar operating mechanisms.

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Section: B Psomentioning

confidence: 99%

Particle Swarm Optimized Autonomous Learning Fuzzy System

Shen

Angelov

2021

IEEE Trans. Cybern.

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“…A multimodal medical image fusion method based on the optimum Laplacian wavelet mask which uses a hybrid CSA-Gray Wolf Optimization algorithm-is proposed in Daniel et al 14 In Mohapatra et al, 15 an improved CSA is used to pretrain the extreme learning machine for a binary medical data sets classifier. In Alswaitti et al, 16 an improved approach for PSO-based data clustering is proposed. The kernel density estimation technique and estimated multidimensional gravitational learning coefficients are used to ensure the balance between exploitation and exploration processes in PSO.…”

Section: A Comparison Of Nature-inspired Optimization Algorithmsmentioning

confidence: 99%

A Comparison of Some Nature-Inspired Optimization Metaheuristics Applied in Biomedical Image Registration

Bejinariu

Costin

2018

Methods Inf Med

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Computational Intelligence Re-meets Medical Image Processing Analysis of Machine Learning Algorithms for Diagnosis of Diffuse Lung Diseases Background In the last decades, new optimization methods based on the nature's intelligence were developed. These metaheuristics can find a nearly optimal solution faster than other traditional algorithms even for high-dimensional optimization problems. All these algorithms have a similar structure, the difference being made by the strategies used during the evolutionary process. Objectives A set of three nature-inspired algorithms, including Cuckoo Search algorithm (CSA), Particle Swarm Optimization (PSO), and Multi-Swarm Optimization (MSO), are compared in terms of strategies used in the evolutionary process and also of the results obtained in case of particular optimization problems. Methods The three algorithms were applied for biomedical image registration (IR) and compared in terms of performances. The expected geometric transform has seven parameters and is composed of rotation against a point in the image, scaling on both axis with different factors, and translation. Results The evaluation consisted of 25 runs of each IR procedure and revealed that (1) PSO offers the most precise solutions; (2) CSA and MSO are more stable in the sense that their solutions are less scattered; and (3) MSO and PSO have a higher convergence speed. Conclusions The evaluation of PSO, MSO, and CSA was made for multimodal IR problems. It is possible that for other optimization problems and also for other settings of the optimization algorithms, the results can be different. Therefore, the nature-inspired algorithms demonstrated their efficacy for this class of optimization problems.

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“…Among the EC algorithms, the PSO algorithm which is a population-based heuristic algorithm has received much research attention owing to its easy implementation and competitiveness in finding a relatively satisfactory solution with a reasonable convergence speed, see e.g., [11], [12], [23], [34], [36], [39], [40], [48], [51]. Moreover, among the ECbased clustering approaches, PSO algorithms have proven to be a strong competitor to other optimization algorithms [2], [18], [38], [47]. For instance, a PSO-based clustering technique has been proposed in [38] where the initial swarm adopts clusters formed by the K-means clustering algorithm.…”

Section: Introduction Accident and Emergency (Aande) Departments In Namentioning

confidence: 99%

A Novel Particle Swarm Optimization Approach for Patient Clustering From Emergency Departments

Liu

Wang

Liu

et al. 2019

IEEE Trans. Evol. Computat.

116

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In this paper, a novel particle swarm optimization (PSO) algorithm is proposed in order to improve the accuracy of traditional clustering approaches with applications in analyzing real-time patient attendance data from an accident & emergency (A&E) department in a local UK hospital. In the proposed randomly occurring distributedly delayed particle swarm optimization (RODDPSO) algorithm, the evolutionary state is determined by evaluating the evolutionary factor in each iteration, based on which the velocity updating model switches from one mode to another. With the purpose of reducing the possibility of getting trapped in the local optima and also expanding the search space, randomly occurring time-delays that reflect the history of previous personal best and global best particles are introduced in the velocity updating model in a distributed manner. Eight well-known benchmark functions are employed to evaluate the proposed RODDPSO algorithm which is shown via extensive comparisons to outperform some currently popular PSO algorithms. To further illustrate the application potential, the RODDPSO algorithm is successfully exploited in the patient clustering problem for data analysis with respect to a local A&E department in West London. Experiment results demonstrate that the RODDPSO-based clustering method is superior over two other well-known clustering algorithms.

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Density-based particle swarm optimization algorithm for data clustering

Cited by 96 publications

References 36 publications

Particle Swarm Optimized Autonomous Learning Fuzzy System

Particle Swarm Optimized Autonomous Learning Fuzzy System

A Comparison of Some Nature-Inspired Optimization Metaheuristics Applied in Biomedical Image Registration

A Novel Particle Swarm Optimization Approach for Patient Clustering From Emergency Departments

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