In this paper, a new variant of particle swarm optimisation (PSO) called PSO with improved learning strategy (PSO-ILS) is developed. Specifically, an ILS module is proposed to generate a more effective and efficient exemplar, which could offer a more promising search direction to the PSO-ILS particle. Comparison is made on the PSO-ILS with 6 well-established PSO variants on 10 benchmark functio...

This is a review paper. Review is carried out in three parts. First part covered up the introduction to PSO algorithm, load balancing based on PSO and multi objective job shop scheduling by improved PSO. Second part of this paper focused on workflow scheduling in cloud based on various versions of PSO. Third part explained PSO with cloud model with a little result analysis. This survey is done ...

The paper proposes an approach to optimize the connection weights and network structure of BP neural network (BPNN) which based on improved particle swarm optimization (PSO) algorithm. For each network structure, the algorithm generates a series of particles which consist of connection weights and threshold values, and selects the best network structure according to the improved PSO algorithm. ...

Particle Swarm Optimization (PSO) algorithm is a new optimization approach, which has been widely used to solve various and complex optimization problems. However, there are still some imperfections, such as premature convergence and low accuracy. To address such defects, an improved PSO is proposed in this paper. The improved PSO algorithm introduces a uniform search strategy that makes partic...

In order to maintain the stability and security of the power system, the uncertainty and intermittency of wind power must be taken into account in economic dispatch (ED) problems. In this paper, a dynamic economic dispatch (DED) model based on chance constrained programming is presented and an improved particle swarm optimization (PSO) approach is proposed to solve the problem. Wind power is re...

Particle swarm optimization (PSO) has shown its good search ability in many optimization problems. However, PSO often easily fall into local minima because the particles could quickly converge to a position by the attraction of the best particles. Under this circumstance, all the particles could hardly be improved. This paper presents a hybrid PSO, namely LSPSO, to solve this problem by employi...

In order to improve the convergence speed and the drawback of easily converging to the local optimum of the standard particle swarm optimization (PSO), two improved PSO algorithms are presented based on the simple particle swarm optimization algorithm without speed attribute. One is introducing differential mutation technology of differential evolution algorithm into the simple PSO algorithm fo...

This paper presents an improved Particle Swarm Optimization (PSO) algorithm for solving Transient Stability Constrained Optimal Power Flow (TSCOPF) problem through the application of Gaussian and Cauchy probability distributions. The modified PSO approach introduces new diversification and intensification strategy into the particles thus preventing PSO algorithm from premature convergence. The ...

Loan default evaluation and discrimination is a complicated issue because of its nonlinearity and uncertainty. Least square support vector machine (LS-SVM) has been successfully employed to solve regression and time series problem. This paper proposes a novel PSO-LS-SVM model based on the improved PSO algorithm to optimize parameters of LS-SVM, which is a new improved form by synthesizing the e...

The paper discusses an improved modeling of transformer windings based on particle swarm optimization algorithm (PSO) and frequency response analysis (FRA). With the purpose to accurately identify transformer windings parameters a model-based identification approach is introduced using a well-known lumped parameter model. It includes search space estimation using analytical calculations, which ...