We have previously proposed a hybrid particle swarm optimisation/ant colony optimisation (PSO/ACO) algorithm for the discovery of classification rules. Unlike a conventional PSO algorithm, this hybrid algorithm can directly cope with nominal attributes, without converting nominal values into binary numbers in a preprocessing phase. PSO/ACO2 also directly deals with both continuous and nominal a...

Network traffic flow prediction model is fundamental to the network performance evaluation and the design of network control scheme which is crucial for the success of high-speed networks. Aiming at shortcoming of the conventional network traffic time series prediction model and the problem that BP training algorithms easily plunge into local solution, a network traffic prediction model based o...

We introduce a high-performing composite particle swarm optimization (PSO) algorithm. In an analogy to the popular character of Mary Shelley’s famous novel, we call our algorithm Frankenstein’s PSO, as it consists of different algorithmic components drawn from other PSO variants. Frankenstein’s PSO constituents were selected after careful evaluation of their impact on speed and reliability. We ...

A new filtering algorithm — PSO-UPF was proposed for nonlinear dynamic systems. Basing on the concept of re-sampling, particles with bigger weights should be re-sampled more time, and in the PSO-UPF, after calculating the weight of particles, some particles will join in the refining process, which means that these particles will move to the region with higher weights. This process can be regard...

This paper presents a comprehensive study on monoand multi-objective approaches for electrical distribution network design using particle swarmoptimization (PSO). Specifically, two distribution network design problems, i.e., static and expansion planning, are solved using PSO. The network planning involves optimization of both network topology and branch conductor sizes. Both the planning probl...

The particle swarm optimizer (PSO) is a stochastic, populationbased optimization technique that can be applied to a wide range of applications. This paper presents a random time variable PSO algorithm, called the PSO-RTVIWAC, introducing random timevarying inertia weight and acceleration coefficients to significantly improve the performance of the original algorithms. The PSO-RTVIWAC method ori...

Pomegranate seed oil (PSO), which is the major source of conjugated linolenic acids such as punicic acid (PuA), exhibits strong anti-inflammatory properties. Necrotizing enterocolitis (NEC) is a devastating disease associated with severe and excessive intestinal inflammation. The aim of this study was to evaluate the effects of orally administered PSO on the development of NEC, intestinal epith...

This paper proposes an improved version of particle swarm optimization (PSO) algorithm for the training of a neural network (NN). An architecture for the NN trained by PSO (standard PSO, improved PSO) is also introduced. This architecture has a data preprocessing mechanism which consists of a normalization module and a data-shuffling module. Experimental results showed that the NN trained by im...

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 Particle Swarm Optimiser (PSO) is a population based stochastic optimisation algorithm, empirically shown to be efficient and robust. This paper provides a proof to show that the original PSO does not have guaranteed convergence to a local optimum. A flaw in the original PSO is identified which causes stagnation of the swarm. Correction of this flaw results in a PSO algorithm with guarantee...