Particle swarm optimization is a popular method for solving difficult optimization problems. There have been attempts to formulate the method in formal probabilistic or stochastic terms (e.g. bare bones particle swarm) with the aim to achieve more generality and explain the practical behavior of the method. Here we present a Bayesian interpretation of the particle swarm optimization. This inter...

This paper presents a dynamic particle swarm optimization based search for optimal fusion configuration of sensors in distributed detection network in presence of a nonstationary binary symmetric channel. The wireless channel in sensor networks is a non-stationary random process, which moves the optima of the original problem, otherwise static. The optimal fusion configuration minimizes the pro...

This paper proposes the use of a particle filter with embedded particle swarm optimization as an efficient and effective way of dealing with 3d model-based human body tracking. A particle swarm optimization algorithm is utilized in the particle filter to shift the particles toward more promising configurations of the human model. The algorithm is shown to be able of tracking full articulated bo...

In this paper, an effective combination of two Metaheuristic algorithms, namely Invasive Weed Optimization and the Particle Swarm Optimization, has been proposed. This hybridization called as HIWOPSO, consists of two main phases of Invasive Weed Optimization (IWO) and Particle Swarm Optimization (PSO). Invasive weed optimization is the natureinspired algorithm which is inspired by colonial beha...

In optimizing the particle swarm optimization (PSO) that inevitable existence problem of prematurity and the local convergence, this paper base on this aspects is put forward a kind of modified particle swarm optimization algorithm, take the gradient descent method (BP algorithm) as a particle swarm operator embedded in particle swarm algorithm, and at the same time use to attenuation wall (Dam...

in this paper, a novel method is developed for designing the output feedback controller for static synchronous series compensator (sssc). in the proposed method, the problem of selecting the output feedback gains for the sssc controllers is changed into an optimization problem with a time domain-based objective function.then, it is solved by using the particle swarm optimization (pso) algorithm...

in this paper, the gain in ld-celp speech coding algorithm is predicted using three neural models, that are equipped by genetic and particle swarm optimization (pso) algorithms to optimize the structure and parameters of neural networks. elman, multi-layer perceptron (mlp) and fuzzy artmap are the candidate neural models. the optimized number of nodes in the first and second hidden layers of el...