Discrete Particle Swarm Optimization Algorithm for Solving Independent Task Scheduling

An improved discrete Particle Swarm Optimization(PSO) algorithm is presented to tackle the independent task scheduling problem. In the algorithm, a task based representation is designed, and a new method is used to update the positions and velocity of particles. In order to keep the particle swarm algorithm from premature stagnation, the simulated annealing algorithm, which has local search ability, is combined with the PSO algorithm to make elaborate search near the optimal solution, then the quality of solutions is improved effectively. Experimental results compared with genetic algorithm and basic PSO algorithm show that the hybrid algorithm has good performance.