The proposed work introducing new coefficients and some modern control parameters such as sensitivity (s(t)) and probability of nectar (p(t)) and modification of the conventional parameter (Φ). With presenting these parameters the performance and searching ability of the BF-PSO is significantly increased compared to standered PSO. This new algorithm is inspired by the intelligent behavior of bu...

Stock price prediction is the main concern for financial firms and private investors. In this paper, we proposed a hybrid BP neural network combining adaptive PSO algorithm (HBP-PSO) to predict the stock price. HBP-PSO takes full use of the global searching capability of PSO and the local searching advantages of BP Neural Network. The PSO algorithm is applied for training the connection weights...

UAVs are attracting more and more attentions for their versatilities and low costs. This paper focuses on their security and considers launching jamming attacks on them. We firstly formulate the UAVs jamming problem. Secondly the PSO (Particle Swarm Optimization) algorithm is introduced and new metrics like AJRL (Area for jamming a receiving link) and NJRL (Number of AJRLs) are defined. Then we...

The work proposes the application of neural networks with particle swarm optimisation (PSO) and genetic algorithms (GA) to compensate for missing data in classifying high voltage bushings. The classification is done using DGA data from 60966 bushings based on IEEEc57.104, IEC599 and IEEE production rates methods for oil impregnated paper (OIP) bushings. PSO and GA were compared in terms of accu...

In order to overcome the shortcomings of traditional clustering algorithms such as local optima and sensitivity to initialization, a new Optimization technique, Particle Swarm Optimization is used in association with Unsupervised Clustering techniques in this paper. This new algorithm uses the capacity of global search in PSO algorithm and solves the problems associated with traditional cluster...

This paper proposes a new approach to using particle swarm optimisation (PSO) within an AdaBoost framework for object detection. Instead of using exhaustive search for finding good features to be used for constructing weak classifiers in AdaBoost, we propose two methods based on PSO. The first uses PSO to evolve and select good features only and the weak classifiers use a simple decision stump....

Particle Swarm Optimization (PSO) technique has proved its ability to deal with very complicated optimization and search problems. Several variants of the original algorithm have been proposed. This paper proposes a novel hybrid PSO evolutionary algorithm for solving the well known geometrical place problems. Finding the geometrical place could be sometimes a hard task. In almost all situations...

Particle Swarm Optimization (PSO) is a swarm intelligence optimization method inspired from birds’ flocking or fish schooling. Many improved versions of PSO are reported in literature, including some by the authors. Original as well as improved versions of PSO have proven their applicability to various fields like science, engineering and industries. Economic dispatch (ED) problem is one of the...