In order to enhance inter-particle cooperation and information sharing capabilities, an improved particle swarm algorithm optimization model is proposed by introducing the centroid of particle swarm in the standard PSO model to improve global optimum efficiency and accuracy of algorithm, then parameter selection guidelines are derived in the convergence of new algorithm. The results of Benchmar...

Particle Swarm Optimisation (PSO) algorithm is a stochastic search technique, which has exhibited good performance across a wide range of applications. However, very often for multimodal problems involving high dimensions, the algorithm tends to suffer from premature convergence. Analysis of the behaviour of the particle swarm model reveals that such premature convergence is mainly due to the d...

Genetic algorithm behavior is determined by the exploration/exploitation balance kept throughout the run. When this balance is disproportionate, the premature convergence problem will probably appear, causing a drop in the genetic algorithm's eecacy. One approach presented for dealing with this problem is the distributed genetic algorithm model. Its basic idea is to keep, in parallel, several s...

Earthquake time history records are required to perform dynamic nonlinear analyses. In order to provide a suitable set of such records, they are scaled to match a target spectrum as introduced in the well-known design codes. Corresponding scaling factors are taken similar in practice however, optimizing them reduces extra-ordinary economic charge for the seismic design. In the present work a ne...

To self-adapt ([Schwefel, 1981], [Fogel et al., 1991]) a search parameter, rather than fixing the parameter globally before search begins the value is encoded in each individual along with the other genes. This is done in the hope that the value will then become adapted on a perindividual basis. While this mechanism is very powerful and in some cases essential to achieving good search performan...

In order to overcome the weakness that particle swarm optimization algorithm is likely to fall into local minimum when the complex optimization problems are solved, a new adaptive dynamic particle swarm optimization algorithm is proposed. The paper introduces the evaluation index of particle swarm premature convergence to judge the state of particle swarm in the population space, for the sake o...

PURPOSE This study investigated whether vergence and accommodation development in preterm infants is preprogrammed or is driven by experience. METHODS Thirty-two healthy infants, born at mean 34 weeks gestation (range, 31.2-36 weeks), were compared with 45 healthy full-term infants (mean 40.0 weeks) over a 6-month period, starting at 4 to 6 weeks postnatally. Simultaneous accommodation and co...

The convergence and divergence are two common phenomena in swarm intelligence. To obtain good search results, the algorithm should have a balance on convergence and divergence. The premature convergence happens partially due to the solutions getting clustered together, and not diverging again. The brain storm optimization (BSO), which is a young and promising algorithm in swarm intelligence, is...

In this paper an improved Particle Swarm Optimization (PSO) scheme is proposed to solve Static Routing and Wavelength Assignment (static RWA) where the movement of the swarm particles is influenced by their personal-best position searched so far and the position of the global-best particle. Simulation results show that the proposed scheme performs better in terms of particle fitness value and a...

Evolutionary algorithms (EA) are general metaheuristic algorithms which have very good characteristics. They are relatively robust and usually generate very good solutions to hard problems [15], [16], [23], [29]. However, they also have many problems with the population converging to a suboptimal solution instead of an optimal one [27], [28]. This occurrence is called premature convergence, and...