Self-Adaptive Crossover Particle Swarm Optimizer for Multi-dimension Functions Optimization

The Mahalanobis–Taguchi system is considered as a promising and powerful tool for handling binary classification cases. Though, the Mahalanobis–Taguchi system has several restrictions in screening useful features and determining the decision boundary in an optimal manner. In this article, an integrated Mahalanobis classification system is proposed which builds on the concept of Mahalanobis distance and its space. The integrated Mahalanobis classification system integrates the decision boundary searching process, based on particle swarm optimizer, directly into the feature selection phase for constructing the Mahalanobis distance space. This integration (a) avoids the need for user-dependent input parameters and (b) improves the classification performance. For the feature selection phase, both the use of binary particle swarm optimizer and binary gravitational search algorithm is investigated. To deal with possible overfitting problems in case of sparse data sets, k-fold cross-validation is considered. The integrated Mahalanobis classification system procedure is benchmarked with the classical Mahalanobis–Taguchi system as well as the recently proposed two-stage Mahalanobis classification system in terms of classification performance. Results are presented on both an experimental case study of complex-shaped metallic turbine blades with various damage types and a synthetic case study of cylindrical dogbone samples with creep and microstructural damage. The results indicate that the proposed integrated Mahalanobis classification system shows good and robust classification performance.

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A self-adaptive particle swarm optimization based K-means (SAPSO-K) clustering method to evaluate fabric tactile comfort

Journal of the Textile Institute ◽

10.1080/00405000.2021.1908484 ◽

2021 ◽

pp. 1-12

Author(s):

Yi Sun ◽

Gui Liu ◽

Dongming Zheng ◽

Haochen Zou ◽

Zhenrui Liu ◽

...

Keyword(s):

Particle Swarm Optimization ◽

Particle Swarm ◽

Clustering Method ◽

Swarm Optimization ◽

Adaptive Particle Swarm Optimization ◽

Self Adaptive

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Parameters Identification of the Fractional-Order Permanent Magnet Synchronous Motor Models Using Chaotic Ensemble Particle Swarm Optimizer

Applied Sciences ◽

10.3390/app11031325 ◽

2021 ◽

Vol 11 (3) ◽

pp. 1325

Author(s):

Dalia Yousri ◽

Magdy B. Eteiba ◽

Ahmed F. Zobaa ◽

Dalia Allam

Keyword(s):

Permanent Magnet ◽

Linear Optimization ◽

Permanent Magnet Synchronous Motor ◽

Particle Swarm ◽

Synchronous Motor ◽

Variable Order ◽

Particle Swarm Optimizer ◽

Novel Variants ◽

Fractional Models ◽

Complex Nonlinear Systems

In this paper, novel variants for the Ensemble Particle Swarm Optimizer (EPSO) are proposed where ten chaos maps are merged to enhance the EPSO’s performance by adaptively tuning its main parameters. The proposed Chaotic Ensemble Particle Swarm Optimizer variants (C.EPSO) are examined with complex nonlinear systems concerning equal order and variable-order fractional models of Permanent Magnet Synchronous Motor (PMSM). The proposed variants’ results are compared to that of its original version to recommend the most suitable variant for this non-linear optimization problem. A comparison between the introduced variants and the previously published algorithms proves the developed technique’s efficiency for further validation. The results emerge that the Chaotic Ensemble Particle Swarm variants with the Gauss/mouse map is the most proper variant for estimating the parameters of equal order and variable-order fractional PMSM models, as it achieves better accuracy, higher consistency, and faster convergence speed, it may lead to controlling the motor’s unwanted chaotic performance and protect it from ravage.

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