Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

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A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture

Automated and accurate classification of MR brain images is of importance for the analysis and interpretation of these images and many methods have been proposed. In this paper, we present a neural network (NN) based method to classify a given MR brain image as normal or abnormal. This method first employs wavelet transform to extract features from images, and then applies the technique of principle component analysis (PCA) to reduce the dimensions of features. The reduced features are sent to a back propagation (BP) NN, with which scaled conjugate gradient (SCG) is adopted to find the optimal weights of the NN. We applied this method on 66 images (18 normal, 48 abnormal). The classification accuracies on both training and test images are 100%, and the computation time per image is only 0.0451s.

A hybrid method for MRI brain image classification

Fruit classification using computer vision and feedforward neural network

Emotion recognition represents the position and motion of facial muscles. It contributes significantly in many fields. Current approaches have not obtained good results. This paper aimed to propose a new emotion recognition system based on facial expression images. We enrolled 20 subjects and let each subject pose seven different emotions: happy, sadness, surprise, anger, disgust, fear, and neutral. Afterward, we employed biorthogonal wavelet entropy to extract multiscale features, and used fuzzy multiclass support vector machine to be the classifier. The stratified cross validation was employed as a strict validation model. The statistical analysis showed our method achieved an overall accuracy of 96.77±0.10%. Besides, our method is superior to three state-of-the-art methods. In all, this proposed method is efficient.

Facial Emotion Recognition Based on Biorthogonal Wavelet Entropy, Fuzzy Support Vector Machine, and Stratified Cross Validation

Path finding is a fundamental research topic in transportation planning, intelligent transportation system, routine selection, etc It is usually simplified as the shortest path (SP) in deterministic networks However, some parameters in real life are stochastic In this article, a more pragmatic model for stochastic networks was proposed, which not only considers determinist variables but also the mean and variances of random variables In order to fasten the solution of our model, a novel method was proposed, which combines artificial immune system (AIS), chaos operator, and particle swarm optimization (PSO) Numerical experiments were presented to demonstrate that this proposed model is valid, effective, and more close to real-life, and CIPSO outperforms GA and PSO in respect of route optimality and convergence time

Find multi-objective paths in stochastic networks via chaotic immune PSO

This paper proposes a hybrid classifier for polarimetric SAR images. The feature sets consist of span image, the H/A/α decomposition, and the GLCM-based texture features. Then, a probabilistic neural network (PNN) was adopted for classification, and a novel algorithm proposed to enhance its performance. Principle component analysis (PCA) was chosen to reduce feature dimensions, random division to reduce the number of neurons, and Brent’s search (BS) to find the optimal bias values. The results on San Francisco and Flevoland sites are compared to that using a 3-layer BPNN to demonstrate the validity of our algorithm in terms of confusion matrix and overall accuracy. In addition, the importance of each improvement of the algorithm was proven.

/pdf/remote-sensing-image-classification-based-on-an-improved-22u8ksfuge.pdf

Remote-Sensing Image Classification Based on an Improved Probabilistic Neural Network

http://www2.tissch.ir/fa/07706e6de5d8dfb7c726f3027e4a50c4.pdf

A novel algorithm for all pairs shortest path problem based on matrix multiplication and pulse coupled neural network

To enhance color images more effectively, a novel strategy is presented in this paper. We firstly translate the image to be enhanced from RGB space into HIS space, secondly keep its H component unchanged, and thirdly stretch its S component exponentially, and at last process its I component in the following manner: couple both the gray value and the spatial information into an inner activity item of corresponding neuron, integrate the human visual system into a dynamic component of corresponding neuron, and compare the inner activity item with dynamic component to obtain the enhanced image. Experiments demonstrate the effectiveness and validity of our strategy.

Geng Wei

Papers

Find multi-objective paths in stochastic networks via chaotic immune PSO

Remote-Sensing Image Classification Based on an Improved Probabilistic Neural Network

A novel algorithm for all pairs shortest path problem based on matrix multiplication and pulse coupled neural network

Color image enhancement based on HVS and PCNN

Cellular image segmentation based on particle swarm neural network: Cellular image segmentation based on particle swarm neural network