求解独立任务调度的离散粒子群优化算法

Discrete Particle Swarm Optimization Algorithm for Solving Independent Task Scheduling

针对独立任务调度问题,提出一种改进的离散粒子群算法,采用基于任务的编码方式,对粒子的位置和速度更新方法进行重新定义。为防止粒子群算法的早熟收敛,给出利用模拟退火算法的局部搜索能力在最优解附近进行精细搜索,以改善解的质量。仿真结果表明,与遗传算法和基本粒子群算法相比,该混合算法具有较好的优化性能。

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.