Particle Swarm Optimizer (PSO) is such a complex stochastic process so that analysis on the stochastic behavior of the PSO is not easy. The choosing of parameters plays an important role since it is critical in the performance of PSO. As far as our investigation is concerned, most of the relevant researches are based on computer simulations and few of them are based on theoretical approach. In ...

The Particle Swarm Optimization (PSO) Algorithm is one of swarm intelligence optimization algorithms. Usually the population’s values of PSO algorithm are random which leads to random distribution of search quality and search velocity. This paper presents PSO based on uniform design (UD). UD is widely used in various applications and introduced to generate an initial population, in which the po...

In this paper an extensive theoretical and empirical analysis of recently introduced Particle Swarm Optimization algorithm with Convergence Related parameters (CR-PSO) is presented. The convergence of the classical PSO algorithm is addressed in detail. The conditions that should be imposed on parameters of the algorithm in order for it to converge in mean-square have been derived. The practical...

The popular fuzzy c-means algorithm (FCM) converges to a local minimum of the objective function. Hence, different initializations may lead to different results. The important issue is how to avoid getting a bad local minimum value to improve the cluster accuracy. The particle swarm optimization (PSO) is a popular and robust strategy for optimization problems. But the main difficulty in applyin...

A grid computing system consists of a group of programs and resources that are spread across machines in the grid. A grid system has a dynamic environment and decentralized distributed resources, so it is important to provide efficient scheduling for applications. Task scheduling is an NP-hard problem and deterministic algorithms are inadequate and heuristic algorithms such as particle swarm op...

The paper extends our pervious work on solving multi-contingency transient stability constrained optimal power flow problems (MC-TSCOPF) with the approach of particles swarm optimization (PSO). A hybrid PSO method that incorporates with a new wavelet theory based mutation operation, intends to improve the searching strategies on previously used PSO methods, is proposed to solve MC-TSCOPF proble...

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...