Particle Swarm Optimization With Probability Sequence for Global Optimization

Rauf, Hafiz Tayyab; Shoaib, Umar; Lali, M. Ikram Ullah; Alhaisoni, Majed; Irfan, Muhammad Naeem; Khan, Muhammad Attique

doi:10.1109/access.2020.3002725

Cited by 48 publications

(27 citation statements)

References 48 publications

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“…EPSO proposed by Li et al [21] is considered to be PSO's most renowned variant. This particular variant has been employed in various applications of image processing [22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Correlation Filters Using Extended Particle Swarm Optimization Technique

Masood

Zafar

Ali

et al. 2021

Computational and Mathematical Methods in Medicine

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In the past few decades, the field of image processing has seen a rapid advancement in the correlation filters, which serves as a very promising tool for object detection and recognition. Mostly, complex filter equations are used for deriving the correlation filters, leading to a filter solution in a closed loop. Selection of optimal tradeoff (OT) parameters is crucial for the effectiveness of correlation filters. This paper proposes extended particle swarm optimization (EPSO) technique for the optimal selection of OT parameters. The optimal solution is proposed based on two cost functions. The best result for each target is obtained by applying the optimization technique separately. The obtained results are compared with the conventional particle swarm optimization method for various test images belonging from different state-of-the-art datasets. The obtained results depict the performance of filters improved significantly using the proposed optimization method.

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Section: Introductionmentioning

confidence: 99%

Optimization of Correlation Filters Using Extended Particle Swarm Optimization Technique

Masood

Zafar

Ali

et al. 2021

Computational and Mathematical Methods in Medicine

Self Cite

View full text Add to dashboard Cite

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“…For metaheuristic optimizers, the selection of the initial population can significantly affect the convergence speed [52]. Ideally, the initial population should be not only easy to obtain (low computational costs), but also effectively improve the performance of the algorithm.…”

Section: Diagonal Linear Uniform Initialization a Description Of Sampling Techniquementioning

confidence: 99%

Improved Initialization Method for Metaheuristic Algorithms: A Novel Search Space View

Bai

Gao

2021

IEEE Access

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As an essential step of metaheuristic optimizers, initialization seriously affects the convergence speed and solution accuracy. The main motivation of the state-of-the-art initialization method is to generate a small initial population to cover the search space as much as possible uniformly. However, these approaches have suffered from the curse of dimensionality, high computational cost, and sensitivity to parameters, which ultimately reduce the algorithm's convergence speed. In this paper, a new initialization technique named diagonal linear uniform initialization (DLU) is proposed, which follows a novel search view, i.e., adopting the diagonal subspace sampling instead of the whole space. By considering the algorithm's update mechanism, the improved sampling method dramatically improves the convergence speed and solution accuracy of metaheuristic algorithms. Compared with the other eight widely used initialization strategies, the differential evolution (DE) algorithm with DLU obtains the best performance in search accuracy and convergence speed. In the extension experiments, results show that the DLU is still effective for three swarm-based algorithms: particle swarm optimization (PSO), cuckoo search (CS), and artificial bee colony (ABC). Especially for the multi-objective problem, the DLU still demonstrates its powerful performance compared with other strategies.

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“…In recent times, deep learning has confirmed its supremacy for many computer vision and machine learning applications like action recognition [16], gait recognition [17,18], object detection [19,20], and many more [21][22][23]. For malware detection and classification, different researchers have applied deep learning and image processing techniques to accomplish high accuracy because of their ground-breaking capacity to learn the best features.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Malware Classification Method Using Segmentation-Based Fractal Texture Analysis and Deep Convolution Neural Network Features

et al. 2020

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As the number of internet users increases so does the number of malicious attacks using malware. The detection of malicious code is becoming critical, and the existing approaches need to be improved. Here, we propose a feature fusion method to combine the features extracted from pre-trained AlexNet and Inception-v3 deep neural networks with features attained using segmentation-based fractal texture analysis (SFTA) of images representing the malware code. In this work, we use distinctive pre-trained models (AlexNet and Inception-V3) for feature extraction. The purpose of deep convolutional neural network (CNN) feature extraction from two models is to improve the malware classifier accuracy, because both models have characteristics and qualities to extract different features. This technique produces a fusion of features to build a multimodal representation of malicious code that can be used to classify the grayscale images, separating the malware into 25 malware classes. The features that are extracted from malware images are then classified using different variants of support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), and other classifiers. To improve the classification results, we also adopted data augmentation based on affine image transforms. The presented method is evaluated on a Malimg malware image dataset, achieving an accuracy of 99.3%, which makes it the best among the competing approaches.

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Particle Swarm Optimization With Probability Sequence for Global Optimization

Cited by 48 publications

References 48 publications

Optimization of Correlation Filters Using Extended Particle Swarm Optimization Technique

Optimization of Correlation Filters Using Extended Particle Swarm Optimization Technique

Improved Initialization Method for Metaheuristic Algorithms: A Novel Search Space View

Hybrid Malware Classification Method Using Segmentation-Based Fractal Texture Analysis and Deep Convolution Neural Network Features

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