Particle swarm optimization algorithm in solving complex functions, such as slow convergence, accuracy is not high, easily falling into local optimum problem. Based on the chaos optimization is introduced into particle swarm optimization algorithm, given the chaotic particle swarm optimization algorithm. In order to improve the image quality of CMOS image sensor, the image of the main noise sou...

PSO is a robust and stochastic optimization technique based on the movement and the intelligence of the swarms. PSO is inspired with social behavior of bird flocking and fish schooling. PSO algorithm is applied to solve the real world complex optimization problems. So many real world optimization problems are solved by using different combinations and variations of PSO algorithms. In this paper...

It is a grand challenge to model the emergence of swarm intelligence and many principles or models had been proposed. However, existing models do not catch the nature of swarm intelligence and they are not generic enough to describe various types of emergence phenomena. In this work, we propose a contradiction-centric model for emergence of swarm intelligence, in which individuals’ contradictio...

Particle swarm optimization (PSO) is an optimization approach from the field of artificial intelligence. A population of so-called particles moves through the parameter space defined by the optimization problem, searching for good solutions. Inspired by natural swarms, the movements of the swarm members depend on own experiences and on the experiences of adjacent particles. PSO algorithms are m...

this work aims to investigate the reliability of software products as an important attribute of computer programs; it helps to decide the degree of trustworthiness a program has in accomplishing its specific functions. This is done using the Software Reliability Growth Models (SRGMs) through the estimation of their parameters. The parameters are estimated in this work based on the available fai...

Particle swarm optimisation (PSO) is a stochastic optimisation algorithm based on swarm intelligence. The algorithm applies the concept of social interaction to find optimal solution. Sina Weibo is one of the most popular Chinese microblog platforms. Microblog users participate in network interaction by publishing tweets and retweets. The influences of microblog users are determined by the user...

This paper proposes a refined version of particle swarm optimization technique for the optimum design of steel structures. Swarm is composed of a number of particles and each particle in the swarm represents a candidate solution of the optimum design problem. Design constraints in accordance with ASD-AISC (Allowable Stress Design Code of American Institute of Steel Institution) are imposed by t...

In this paper, we present a neural-network (NN) based approach to represent a nonlinear Tagagi-Sugeno (T-S) system. A linear differential inclusion (LDI) state-space representation is utilized to deal with the NN models. Taking advantage of the LDI representation, the stability conditions and controller design are derived for a class of nonlinear structural systems. Moreover, the concept of uti...

In this paper, we propose a novel algorithm for articulated object tracking, based on a hierarchical search and particle swarm optimization. Our approach aims to reduce the complexity induced by the high dimensional state space in articulated object tracking by decomposing the search space into subspaces and then using particle swarms to optimize over these subspaces hierarchically. Moreover, t...

Based on the example of constructing a confidence interval for variance, the notion and construction of the shortest confidence interval are put forward. Furthermore, the particle swarm algorithm for this problem is presented to solve this non-linear programming problem. Compared with the confidence interval calculated with traditional method, it has distinct advantage. The optimum results whic...