Task scheduling of cloud computing using integrated particle swarm algorithm and ant colony algorithm

2017 ◽  
Vol 22 (S2) ◽  
pp. 2761-2769 ◽  
Author(s):  
Xuan Chen ◽  
Dan Long
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Ant Colony Algorithm ◽  
Particle Swarm ◽  
Ant Colony ◽  
Particle Swarm Algorithm ◽  
Swarm Algorithm

Review of Task Scheduling Algorithm Research in Cloud Computing

2014 ◽  
Vol 926-930 ◽  
pp. 3236-3239 ◽  
Author(s):  
Mei Geng Huang ◽  
Zhi Qi Ou
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Task Scheduling ◽  
Ant Colony Algorithm ◽  
Particle Swarm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Swarm Optimization ◽  
Slow Convergence

The cloud computing task scheduling field representative algorithms was introduced and analyzed : genetic algorithm, particle swarm optimization, ant colony algorithm. Parallelism and global search solution space is the characteristic of genetic algorithm, genetic iterations difficult to proceed when genetic individuals are very similar; Particle swarm optimization in the initial stage is fast, slow convergence speed in the later stage ; Ant colony algorithm optimization ability is good, slow convergence speed in its first stage; Finally, the summary and prospect the future research direction.

Path Optimization for Automatic Guided Vehicle Based on Fusion Algorithm of Particle Swarm and Ant Colony

2012 ◽  
Vol 182-183 ◽  
pp. 1452-1457 ◽  
Author(s):  
Yuan Bin Hou ◽  
Yi Qian Yuan ◽  
Bai Ping Li
Keyword(s):  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Particle Swarm ◽  
Ant Colony ◽  
Path Optimization ◽  
Particle Swarm Algorithm ◽  
Ant System ◽  
Fusion Algorithm ◽  
Automatic Guided Vehicle ◽  
Swarm Algorithm

Aim at search precocity of particle swarm algorithm and slow convergence speed problem for ant colony algorithm, in the automatic guided vehicle path optimization a path optimization algorithm is proposed, which is fused by particle swarm algorithm and ant colony algorithm. Firstly, robot motion space model of the algorithm is created using link figure. After got fixed circulation rapid global, search to get more optimal path by means of improved fastest convergence ant system, then using a particle ants information communication method to update pheromone, finally, optimal path is drew. The simulation experiment shows that, even in the complex environment, this algorithm can also has the advantage of ant colony algorithm to optimize the result accurately and particle swarm algorithm local optimization accurately and rapidly, and a global security obstacle avoidance of optimal path is plot, the route is shorten 8% compare than the general ant colony algorithm.

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