scholarly journals Scheduling multi–mode resource–constrained tasks of automated guided vehicles with an improved particle swarm optimization algorithm

2021 ◽  
Author(s):  
Xiangjie Xiao ◽  
Yaohui Pan ◽  
Lingling Lv ◽  
Yanjun Shi
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Automated Guided Vehicles ◽  
Resource Constrained ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Multi Mode

An Improved Particle Swarm Optimization Algorithm for Integrated Scheduling Model in AGV-Served Manufacturing Systems

2018 ◽  
Vol 17 (03) ◽  
pp. 375-390 ◽  
Author(s):  
Fuqiang Zhang ◽  
Jingjing Li
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Manufacturing Systems ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Automated Guided Vehicles ◽  
Raw Material ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Scheduling Model

To address the resources optimization problem of AGV-served manufacturing systems driven by multi-varieties and small-batch production orders, a scheduling model integrating machines and automated guided vehicles (AGVs) is proposed. In this model, the makespan of jobs from raw material storage to finished parts storage through multi-stage processes has been used as the objective function, and the utilization ratios of machines and AGVs have been used as the comprehensive evaluation functions. An improved particle swarm optimization algorithm with the characteristics of main particles and nested particles is developed to solve a reasonable scheduling scheme. Compared with the basic particle swarm optimization algorithm and genetic algorithm, the numerical result suggests that the nested particle swarm optimization algorithm has more advantages in convergence and solving efficiency. It is expected that this study can provide a useful reference for the flexible adjustment of AGV-served manufacturing systems.

Compressed Sensing Image Reconstruction Based on Improved Particle Swarm Optimization Algorithm

2014 ◽  
Vol 599-601 ◽  
pp. 1453-1456
Author(s):  
Ju Wang ◽  
Yin Liu ◽  
Wei Juan Zhang ◽  
Kun Li
Keyword(s):  
Particle Swarm Optimization ◽  
Image Reconstruction ◽  
Compressed Sensing ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Matching Pursuit ◽  
Particle Swarm ◽  
Premature Convergence ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The reconstruction algorithm has a hot research in compressed sensing. Matching pursuit algorithm has a huge computational task, when particle swarm optimization has been put forth to find the best atom, but it due to the easy convergence to local minima, so the paper proposed a algorithm ,which based on improved particle swarm optimization. The algorithm referred above combines K-mean and particle swarm optimization algorithm. The algorithm not only effectively prevents the premature convergence, but also improves the K-mean’s local. These findings indicated that the algorithm overcomes premature convergence of particle swarm optimization, and improves the quality of image reconstruction.

An improved particle swarm optimization algorithm for reliability problems

2011 ◽  
Vol 50 (1) ◽  
pp. 71-81 ◽  
Author(s):  
Peifeng Wu ◽  
Liqun Gao ◽  
Dexuan Zou ◽  
Steven Li
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization
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