Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the alg...

This paper presents a modification of the particle swarm optimization algorithm (PSO) intended to combat the problem of premature convergence observed in many applications of PSO. In the new algorithm, each particle is attracted towards the best previous positions visited by its neighbors, in addition to the other aspects of particle dynamics in PSO. This is accomplished by using the ratio of t...

Particle swarm optimization (PSO) is a population-based stochastic optimization originating from artificial life and evolutionary computation. PSO is motivated by the social behavior of organisms, such as bird flocking, fish schooling and human social relations. Its properties of low constraint on the continuity of objective function and ability of adapting to the dynamic environment make PSO b...

BACKGROUND In an effort to minimize rod fractures and nonunion in pedicle subtraction osteotomy (PSO) constructs, surgeons have adopted multirod constructs and interbody cages. Anterior column realignment (ACR) with posterior column osteotomies is a minimally invasive alternative to PSO in sagittal balance correction, however, there is a paucity of evidence with respect to rod survival. METHO...

OBJECTIVES To compare obesity among individuals with PsA, psoriasis (PsO), RA and the general population (n), and identify correlates of obesity among individuals with PsO and PsA. METHODS We compared the BMI of patients with PsA (n = 644), PsO (n = 448), RA (n = 350) and the general population using age- and sex-adjusted linear and logistic regression analyses. We conducted multivariate anal...

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...