Cellular manufacturing system, an application of group technology, has been considered as an effective method to obtain productivity in a factory. For design of manufacturing cells, several mathematical models and various algorithms have been proposed in literature. In the present research, we propose an improved version of discrete particle swarm optimization (PSO) to solve manufacturing cell ...

We describe in this paper a new hybrid approach for optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic to integrate the results. The new evolutionary method combines the advantages of PSO and GA to give us an improved FPSO+FGA hybrid method. Fuzzy Logic is used to combine the results of the PSO and GA in the best way possible. The new hybrid ...

—The implementation methods of the tasks assignment and tasks scheduling for Wireless Sensor and Actuator Network (WSAN) are proposed in this paper. Firstly, the distributed auction algorithm was used to assign tasks to the optimal actuators. Secondly, the Ant Colony Optimization (ACO) algorithm whose parameters were optimized by Particle Swarm Optimization algorithm (PSO) was proposed for the...

The job scheduling technology is an effective way to achieve resource sharing and to improve computational efficiency. Scheduling problem has been proved to be NP-complete problems, Particle Swarm Optimization (PSO) algorithm has demonstrated outstanding performance in solving such issues. In cognizance of the characteristics of cluster scheduling problem, a schedule strategy based on PSO was d...

Particle swarm optimization (PSO), as a novel evolutionary algorithm involved in social interaction for global space search, was firstly used in kinetic parameter estimation. Based on three developed nonlinear kinetic equations for bacterial cell growth, total sugar utilization and -mannanase production by Bacillus licheniformis under the support of a batch fermentation process, various PSO alg...

This investigate proposed a innovative Improved Hybrid PSO-GA (IHPG) algorithm which it combined the advantages of the PSO algorithm and GA algorithm. The IHPG algorithm uses the velocity and position update rules of the PSO algorithm and the GA algorithm in selection, crossover and mutation thought. This study explores the quality monitoring experiment by three existing neural network approach...

A novel neural network algorithm optimized by particle swarm optimization (PSO) for function approximation is proposed in this paper. The prior information extracted from the upper and lower bound of the approximated function is coupled into PSO. Since the prior information narrows the search space and guides the movement direction of the particles, the convergence rate and the approximation ac...

In this chapter we present a technique that helps Particle Swarm Optimisers (PSOs) locate an optimum more quickly, through fitness approximation using regression. A least-squares regression is used to estimate the shape of the local fitness landscape. From this shape, the expected location of the peak is calculated and the information given to the PSO. By guiding the PSO to the optimum, the loc...

This paper deals with the m-machine permutation flowshop scheduling problem to minimize the total flowtime, an NP-complete problem, and proposes an improved particle swarm optimization (PSO) algorithm. To enhance the exploitation ability of PSO, a stochastic iterated local search is incorporated. To improve the exploration ability of PSO, a population update method is applied to replace non-pro...