A modified particle swarm optimization algorithm using Renyi entropy-based clustering

Çomak, Emre

doi:10.1007/s00521-015-1941-9

Cited by 11 publications

(3 citation statements)

References 12 publications

(18 reference statements)

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“…The Renyi entropy, as an extension of the information entropy, reflects the features of the signal in terms of its time–frequency distribution. 28 Compared to information entropy, Renyi entropy is more sensitive to the subtle signal changes, making it easier to identify small changes in the signal. Therefore, this paper optimizes the PSO algorithm based on the Renyi entropy model, and obtains characteristics information of the particle swarm in the search process.…”

Section: Theoretical Descriptionsmentioning

confidence: 99%

Wind turbine gearbox fault diagnosis via adaptive IMFogram

Yi,

Zhang,

Huang

et al. 2023

Structural Health Monitoring

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Wind turbines usually operate in mountainous, offshore and other field environments. As the wind speed is constantly changing, the gearboxes of wind turbines operate under variable operating conditions, resulting in a non-stationery and time-varying signal characteristic. For existing time–frequency analysis (TFA) methods, they undergo low time–frequency energy concentration as well as noise interference during the fault feature extraction. IMFogram, a recently proposed time–frequency representation (TFR) method, calculates local frequency and amplitude information of signals based on fast iterative filtering (FIF). Moreover, it is able to remove uncertainty on the time–frequency plane, allowing a long enough time window to reduce the local boundary effects associated with the signal decomposition methods. Therefore, it has potential advantages for TFA. However, the actual performance of IMFogram calculation largely depends on the decomposition effect provided by FIF and the selection of model parameters. With the purpose of obtaining a more distinct and focused TFR of the wind turbine vibration signal, a novel TFA method, namely Adaptive IMFogram (AIMFogram), is proposed in this paper. Firstly, a range of intrinsic mode functions (IMFs) are gained after the decomposition of vibration signals using an improved variational nonlinear chirp mode decomposition, which is treated as the input to AIMFogram to solve the unsatisfied result by traditional FIF under strong noise. Secondly, Renyi entropy-based particle swarm optimization is conducted to perform adaptive parameter optimization of the AIMFogram. Later, using the optimal parameter, the proposed AIMFogram achieves a high-resolution TFR of complex multi-component vibration signals. To verify whether the proposed AIMFogram is effective or not, the method was successfully employed to the fault diagnosis of the rolling bearing in an experimental bench and the gearbox in a 850-kW wind turbine.

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Section: Theoretical Descriptionsmentioning

confidence: 99%

Wind turbine gearbox fault diagnosis via adaptive IMFogram

Yi,

Zhang,

Huang

et al. 2023

Structural Health Monitoring

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“…Particle swarm optimisation (PSO) [ 34 ] can use entropy for the simulated set of states (“particles”) (EA-PSO) [ 12 ], and then it may apply cross-entropy in the meta-optimisation of the search space. Various modifications and extensions exist, such as memetic based [ 13 ], niche strategy [ 35 ], or clustering [ 36 ]. The evolutionary approach is used in Hu et al [ 37 ], while Zhang et al [ 38 ] employs direct competition.…”

Section: Related Workmentioning

confidence: 99%

Cross-Entropy as a Metric for the Robustness of Drone Swarms

Cofta

Ledziński

Śmigiel

et al. 2020

Entropy

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Due to their growing number and increasing autonomy, drones and drone swarms are equipped with sophisticated algorithms that help them achieve mission objectives. Such algorithms vary in their quality such that their comparison requires a metric that would allow for their correct assessment. The novelty of this paper lies in analysing, defining and applying the construct of cross-entropy, known from thermodynamics and information theory, to swarms. It can be used as a synthetic measure of the robustness of algorithms that can control swarms in the case of obstacles and unforeseen problems. Based on this, robustness may be an important aspect of the overall quality. This paper presents the necessary formalisation and applies it to a few examples, based on generalised unexpected behaviour and the results of collision avoidance algorithms used to react to obstacles.

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“…Exploring the topological neighborhood of the k-nearest neighbors and employing pattern search are considered to be useful tools to improve the performance of PSO [15]. Cormark [16] proposed a PSO procedure using Renyi entropy clustering, which contains two steps, initialization and particle removal. Bharti and Singh [17] proposed a binary PSO procedure with an opposition-based learning mechanism using chaotic mapping, dynamic inertia weight, and a mutation operator.…”

Section: Introductionmentioning

confidence: 99%

A New Chaotic Starling Particle Swarm Optimization Algorithm for Clustering Problems

Wang

Liu

Sun

et al. 2018

Mathematical Problems in Engineering

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A new method using collective responses of starling birds is developed to enhance the global search performance of standard particle swarm optimization (PSO). The method is named chaotic starling particle swarm optimization (CSPSO). In CSPSO, the inertia weight is adjusted using a nonlinear decreasing approach and the acceleration coefficients are adjusted using a chaotic logistic mapping strategy to avoid prematurity of the search process. A dynamic disturbance term (DDT) is used in velocity updating to enhance convergence of the algorithm. A local search method inspired by the behavior of starling birds utilizing the information of the nearest neighbors is used to determine a new collective position and a new collective velocity for selected particles. Two particle selection methods, Euclidean distance and fitness function, are adopted to ensure the overall convergence of the search process. Experimental results on benchmark function optimization and classic clustering problems verified the effectiveness of this proposed CSPSO algorithm.

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A modified particle swarm optimization algorithm using Renyi entropy-based clustering

Cited by 11 publications

References 12 publications

Wind turbine gearbox fault diagnosis via adaptive IMFogram

Wind turbine gearbox fault diagnosis via adaptive IMFogram

Cross-Entropy as a Metric for the Robustness of Drone Swarms

A New Chaotic Starling Particle Swarm Optimization Algorithm for Clustering Problems

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