Quantum-behaved particle swarm optimization (QPSO), motivated by concepts from quantum mechanics and particle swarm optimization (PSO), is a probabilistic optimization algorithm belonging to the bare-bones PSO family. Although it has been shown to perform well in finding the optimal solutions for many optimization problems, there has so far been little analysis on how it works in detail. This p...

This paper presents a new mathematical model to solve cell formation problem in cellular manufacturing systems, where inter-arrival time, processing time, and machine breakdown time are probabilistic. The objective function maximizes the number of operations of each part with more arrival rate within one cell. Because a queue behind each machine; queuing theory is used to formulate the model. T...

This paper presents a new optimization model – EPSO, Evolutionary Particle Swarm Optimization, inspired in both Evolutionary Algorithms and in Particle Swarm Optimization algorithms. The fundamentals of the method are described, and an application to the problem of Loss minimization and Voltage control is presented, with very good results.

In this paper an extensive empirical analysis of recently introduced Particle Swarm Optimization algorithm with Convergence Related parameters (CR-PSO) is presented. The algorithm is tested on extended set of benchmarks and the results are compared to the PSO with time-varying acceleration coefficients (TVAC-PSO) and the standard genetic algorithm (GA). Key-Words: Global Optimization, Particle ...

In this work, we propose a Hybrid particle swarm optimization-Simulated annealing algorithm and present a comparison with i) Simulated annealing algorithm and ii) Back propagation algorithm for training neural networks. These neural networks were then tested on a classification task. In particle swarm optimization behaviour of a particle is influenced by the experiential knowledge of the partic...

this paper investigates a discrete-time impatient customer queue with bernoulli-schedule vacation interruption. the  vacation times and the service times during regular busy period and during working vacation period are assumed to follow geometric distribution. we obtain the steady-state probabilities at arbitrary and outside observer's observation epochs using recursive technique. cost an...

the objective of this paper is to develop an artificial neural network (ann) model which can beused to predict temperature rise due to climate change in regional scale. in the present work data recorded overyears 1985-2008 have been used at training and testing steps for ann model. the multilayer perceptron(mlp) network architecture is used for this purpose. three applied optimization methods a...

in this paper, the optimal design of a three phase surface mounted permanent magnet synchronous motor has been done by particle swarm optimization and bees algorithm. this machine has been designed for high speed applications, and an epoxy and glass fiber bandage is used for permanent magnet protection against centrifugal forces. the optimization has been done by new design equations to improve...

in this paper, we have proposed a new algorithm which combines pso and ga in such a way that the new algorithm is more effective and efficient.the particle swarm optimization (pso) algorithm has shown rapid convergence during the initial stages of a global search but around global optimum, the search process will become very slow. on the other hand, genetic algorithm is very sensitive to the in...

PSO algorithm is an intelligent optimization algorithm based on swarm intelligence. Particle swarm optimization algorithm is simple, easy to implement, and it has a wide application prospect in scientific research and engineering applications. In real life, most of the optimization problem is the optimization problem of some nonlinear discrete with the existence of local. PSO algorithm also has...