Title :
The Comparative Research of Solving Multi-task Scheduling Problems with GA and PSO
Author :
Zhang, Tianchang ; Fan, Wenbin ; Li, Yanli
Author_Institution :
Dept. of Inf. Technol., Hubei Police Univ., Wuhan, China
Abstract :
Genetic algorithm and particle swarm optimization both belong to the evolutionary algorithms; they have much in common, but also have some differences. The paper set out from multi-task scheduling problem, discussed in detail the method of utilizing GA and PSO to equilibrium and optimize multi-task scheduling problem under the constraints of resources separately. Through the analysis of comparative experiment, two kinds of intelligence-optimizing methods made very good results when solved a same problem, but in most cases, PSO had a faster rate of convergence than GA, but GA had a better convergent result than PSO.
Keywords :
genetic algorithms; particle swarm optimisation; planning; scheduling; GA; PSO; evolutionary algorithms; genetic algorithm; multi-task scheduling problems; particle swarm optimization; Algorithm design and analysis; Constraint optimization; Convergence; Evolutionary computation; Genetic algorithms; Information technology; Particle swarm optimization; Processor scheduling; Resource management; Scheduling algorithm; Genetic algorithm; Multi-task; Particle swarm optimization; Project scheduling problem;
Conference_Titel :
Natural Computation, 2009. ICNC '09. Fifth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3736-8
DOI :
10.1109/ICNC.2009.206
