Best selection for the parameters of fractional-order particle swarm optimizer

2017 ◽  
Author(s):  
Po-Yuan Yang ◽  
Jinn-Tsong Tsai ◽  
Jyh-Horng Chou
Keyword(s):  
Fractional Order ◽  
Particle Swarm ◽  
Particle Swarm Optimizer ◽  
Selection For

scholarly journals Controllability of Fractional-Order Particle Swarm Optimizer and Its Application in the Classification of Heart Disease

2021 ◽  
Vol 11 (23) ◽  
pp. 11517
Author(s):  
Fu-I Chou ◽  
Tian-Hsiang Huang ◽  
Po-Yuan Yang ◽  
Chin-Hsuan Lin ◽  
Tzu-Chao Lin ◽  
...  
Keyword(s):  
Heart Disease ◽  
Fractional Order ◽  
Dimensional Space ◽  
Particle Swarm ◽  
Particle Swarm Optimizer ◽  
Search Spaces ◽  
Average Value ◽  
Swarm Algorithms ◽  
Target Values ◽  
The Stability

This study proposes a method to improve fractional-order particle swarm optimizer to overcome the shortcomings of traditional swarm algorithms, such as low search accuracy in a high-dimensional space, falling into local minimums, and nonrobust results. In natural phenomena, our controllable fractional-order particle swarm optimizer can explore search spaces in detail to obtain high resolutions. Moreover, the proposed algorithm is memorable, i.e., position updates focus on the particle position of previous and last generations, rendering it conservative when updating the position, and obtained results are robust. For verifying the algorithm’s effectiveness, 11 test functions compare the average value, overall best value, and standard deviation of the controllable fractional-order particle swarm optimizer and controllable particle swarm optimizer; experimental results show that the stability of the former is better than the latter. Furthermore, the solution position found by the controllable fractional-order particle swarm optimizer is more reliable. Therefore, the improved method proposed herein is effective. Moreover, this research describes how a heart disease prediction application uses the optimizer we proposed to optimize XGBoost hyperparameters with custom target values. The final verification of the obtained prediction model is effective and reliable, which shows the controllability of our proposed fractional-order particle swarm optimizer.

scholarly journals Adaptive-Uniform-Experimental-Design-Based Fractional-Order Particle Swarm Optimizer with Non-Linear Time-Varying Evolution

2019 ◽  
Vol 9 (24) ◽  
pp. 5537 ◽  
Author(s):  
Po-Yuan Yang ◽  
Fu-I Chou ◽  
Jinn-Tsong Tsai ◽  
Jyh-Horng Chou
Keyword(s):  
Experimental Design ◽  
Fractional Order ◽  
Optimization Problems ◽  
Linear Time ◽  
Particle Swarm ◽  
Time Varying ◽  
Particle Swarm Optimizer ◽  
Local Optima ◽  
Non Linear ◽  
Velocity Derivative

An adaptive-uniform-experimental-design-based fractional particle swarm optimizer (AUFPSO) with non-linear time-varying evolution (NTE) is proposed. A particle swarm optimizer (PSO) is an excellent evolutionary algorithm due to its simple structure and rapid convergence. Nevertheless, PSO has notable drawbacks. Although many proposed methods and strategies have enhanced its effectiveness and performance, PSO is limited by its tendency to fall into local optima and its tendency to obtain different solutions in each search (i.e., its weak robustness). Introducing fractional-order calculus in PSO (FPSO) can correct the order of the velocity derivative for each particle, which enhances the diversity and algorithmic effectiveness. This study used NTE of the order of the velocity derivative, inertia weight, cognitive parameter, and social parameter in an FPSO used to search for a global optimal solution. To obtain the best combination of FPSO and NTE, an adaptive uniform experimental design (AUED) method was used to deal with this essential issue. The AUED method integrates a uniform layout with the best combination phase and a stepwise ratio to assist in selecting the best combination for FPSO-NTE. Experimental applications in 15 global numerical optimization problems confirmed that the AUFPSO-NTE had a better performance and robustness than existing PSO-related algorithms.

Quality inspection of complex-shaped metal parts by vibrations and an integrated Mahalanobis classification system

2020 ◽  
pp. 147592172097970
Author(s):  
Liangliang Cheng ◽  
Vahid Yaghoubi ◽  
Wim Van Paepegem ◽  
Mathias Kersemans
Keyword(s):  
Feature Selection ◽  
Mahalanobis Distance ◽  
Classification System ◽  
Gravitational Search Algorithm ◽  
Particle Swarm ◽  
Classification Performance ◽  
Decision Boundary ◽  
Particle Swarm Optimizer ◽  
Selection Phase

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.

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

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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