Particle swarm optimisation (PSO) was developed by Kennedy and Eberhart in 1995 (Kennedy & Eberhart, 1995) inspired by the collective behaviour of natural birds or fish. PSO is a stochastic optimisation technique that uses a behaviour of population composed by many search points called particle. In spite of easy implementation in computer algorithms, it is well known as a powerful numerical opt...

Peony seed oil (PSO) is a novel vegetable oil developed from the seeds of Paeonia suffruticosa Andr. The present study aimed to make an overall investigation on the chemical profile and antioxidant activities of PSO for reasonable development and utilization of this new resource food. Chemical analysis revealed that PSO was characterized by an uncommon high portion of α-linolenic acid (>38%), f...

The Vehicle Routing Problem can be expressed as the problem of designing optimal collection or delivery routes from one or multiple depots to a number of terrestrially scattered customers or cities, subject to side constraints such as time, capacity, mileage etc. The VRP plays a key role in the fields of logistics and transportation. There exist a number of variants of VRPs. Mostly VRPs with fi...

Psoralidin (PSO), a natural furanocoumarin, is isolated from Psoralea corylifolia L. possessing anti-cancer properties. However, the mechanisms of its effects remain unclear. Herein, we investigated its anti-proliferative effect and potential approaches of action on human lung cancer A549 cells. Cell proliferation and death were measured by MTT and LDH assay respectively. Apoptosis was detected...

The effectiveness of wireless sensor networks (WSNs) depends on the coverage and target detection probability provided by dynamic deployment, which is supported by the several methods. Particle Swarm Optimization (PSO) algorithm is one these methods, however computation time required is a big bottleneck. This paper proposes three dynamic PSO-based deployment algorithms that reduce the computati...

1. GPU-BASED PSO PARALLELIZATION In ‘synchronous’ PSO, positions and velocities of all particles are updated in turn in each ‘generation’, after which each particle’s new fitness is evaluated. The value of the social attractor is only updated at the end of each generation, when the fitness values of all particles are known. The ‘asynchronous’ version of PSO, instead, allows the social attractor...

Recently, Particle Swarm Optimization (PSO) has evolved as a promising alternative to the standard backpropagation (BP) algorithm for training Artificial Neural Networks (ANNs). PSO is advantageous due to its high search power, fast convergence rate and capability of providing global optimal solution. In this paper, the authors explore the improvements in forecasting accuracies of feedforward a...

A hybrid meta-heuristic optimization method is introduced to efficiently find the optimal shape of concrete gravity dams including dam-water-foundation rock interaction subjected to earthquake loading. The hybrid meta-heuristic optimization method is based on a hybrid of gravitational search algorithm (GSA) and particle swarm optimization (PSO), which is called GSA-PSO. The operation of GSA-PSO...

Evolutionary computation (EC) techniques such as genetic algorithms (GAs), utilize multiple searching points in the solution space like PSO. Whereas GAs can treat combinatorial optimization problems, PSO was aimed to treat nonlinear optimization problems with continuous variables originally. Moreover, PSO has been expanded to handle combinatorial optimization problems and both discrete and cont...

BACKGROUND Psoriasis (PsO) is a chronic inflammatory disease with predominantly cutaneous manifestations. Approximately one third of patients with PsO develop psoriatic arthritis (PsA), whereas the remaining proportion of patients has isolated cutaneous psoriasis (PsC). These two phenotypes share common immunology, but with different heredity that might in part be explained by genetic variables...