Rocchio algorithm-based particle initialization mechanism for effective PSO classification of high dimensional data

Yahya, Anwar Ali; Osman, Addin; El-Bashir, Mohammad Said

doi:10.1016/j.swevo.2016.11.005

Cited by 17 publications

(3 citation statements)

References 65 publications

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“…Each particle represents a potential solution and guides the search by memorizing its own optimal solution as well as the global optimal solution. [26] The particles iteratively approximate the optimal solution by updating their velocity and position during the search process. Specifically, each particle continuously adjusts its position and velocity according to its own velocity and position update rules, as well as the guidance of the global optimal solution and individual optimal solution.…”

Section: Adaptive Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Laser induced breakdown spectroscopy (LIBS) combined with adaptive particle swarm optimization-based radial basis neural network (APSO-RBF) for rapid identification of soils from different regions

Chen,

Hao,

et al. 2024

Preprint

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Traditional soil identification methods require a lot of time, while this paper proposes a combination of laser-induced breakdown spectroscopy (LIBS) and adaptive particle swarm optimization-based radial basis neural network (APSO-RBF), which is able to quickly and accurately classify soils from different regions, and the experimental results show that the classification accuracy in the test set reaches 99.8148%, which is verified by comparing with the backpropagation ( BP) algorithm, Adaptive Particle Swarm Optimization-Based Backpropagation (APSO-BP) algorithm and Radial Basis Neural Network (RBF) algorithm, which verifies the powerful classification performance of the model.

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Section: Adaptive Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Laser induced breakdown spectroscopy (LIBS) combined with adaptive particle swarm optimization-based radial basis neural network (APSO-RBF) for rapid identification of soils from different regions

Chen,

Hao,

et al. 2024

Preprint

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“…In solving complex problems, combining two or more distribution methods can improve the overall population diversity and, as a result, improve the efficiency and performance of the algorithm [7,20]. Due to the widespread use of meta-heuristic algorithms in other scientific fields such as data mining and image processing, attention to the initialisation methods in increasing the efficiency and effectiveness of these algorithms in other fields has been considered by a large number of researchers [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

A new population initialisation method based on the Pareto 80/20 rule for meta‐heuristic optimisation algorithms

Hasanzadeh

Keynia

2021

IET Software

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In this research, a new method for population initialisation in meta-heuristic algorithms based on the Pareto 80/20 rule is presented. The population in a meta-heuristic algorithm has two important tasks, including pushing the algorithm toward the real optima and preventing the algorithm from trapping in the local optima. Therefore, the starting point of a meta-heuristic algorithm can have a significant impact on the performance and output results of the algorithm. In this research, using the Pareto 80/20 rule, an innovative and new method for creating an initial population in meta-heuristic algorithms is presented. In this method, by using elitism, it is possible to increase the convergence of the algorithm toward the global optima, and by using the complete distribution of the population in the search spaces, the algorithm is prevented from trapping in the local optima. In this research, the proposed initialisation method was implemented in comparison with other initialisation methods using the cuckoo search algorithm. In addition, the efficiency and effectiveness of the proposed method in comparison with other wellknown initialisation methods using statistical tests and in solving a variety of benchmark functions including unimodal, multimodal, fixed dimensional multimodal, and composite functions as well as in solving well-known engineering problems was confirmed. | INTRODUCTIONThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…This algorithm has the characteristics of a simple algorithm, high precision, fast convergence, and so on. For instance, Yahya took advantage of PSO to classify high-dimensional data [41]. Moreover, Malik used PSO to optimize the neural network for the prediction of building energy consumption [42].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Acoustic Signal Processing of a Drum Shearer Based on Improved Variational Mode Decomposition and Cluster Analysis

Peng

Zhu

2020

Sensors

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During operation, the acoustic signal of the drum shearer contains a wealth of information. The monitoring or diagnosis system based on acoustic signal has obvious advantages. However, the signal is challenging to extract and recognize. Therefore, this paper proposes an approach for acoustic signal processing of a shearer based on the parameter optimized variational mode decomposition (VMD) method and a clustering algorithm. First, the particle swarm optimization (PSO) algorithm searched for the best parameter combination of the VMD. According to the results, the approach determined the number of modes and penalty parameters for VMD. Then the improved VMD algorithm decomposed the acoustic signal. It selected the ideal component through the minimum envelope entropy. The PSO was designed to optimize the clustering analysis, and the minimum envelope entropy of the acoustic signal was regarded as the feature for classification. We then use a shearer simulation platform to collect the acoustic signal and use the approach proposed in this paper to process and classify the signal. The experimental results show that the approach proposed can effectively extract the features of the acoustic signal of the shearer. The recognition accuracy of the acoustic signal was high, which has practical application value.

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Rocchio algorithm-based particle initialization mechanism for effective PSO classification of high dimensional data

Cited by 17 publications

References 65 publications

Laser induced breakdown spectroscopy (LIBS) combined with adaptive particle swarm optimization-based radial basis neural network (APSO-RBF) for rapid identification of soils from different regions

Laser induced breakdown spectroscopy (LIBS) combined with adaptive particle swarm optimization-based radial basis neural network (APSO-RBF) for rapid identification of soils from different regions

A new population initialisation method based on the Pareto 80/20 rule for meta‐heuristic optimisation algorithms

A Novel Approach for Acoustic Signal Processing of a Drum Shearer Based on Improved Variational Mode Decomposition and Cluster Analysis

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