基于改进PSO算法的任务分配研究

为了解决虚拟企业中的任务分配问题，建立了任务分配的多目标决策优化模型。分析了传统的PSO算法，通过设置算法中速度惯性权重和加速度系数的自动调整，以及引入遗传算法中的变异操作，实现了对该算法的改进。基于改进的PSO算法求解任务分配模型，研究了求解问题与粒子的映射以及采用TOPSIS计算粒子位置适应度的方法，进而设计了一种基于改进PSO算法的任务分配算法。通过应用实例及仿真实验，证明了改进的PSO算法应用于任务分配的可行性和有效性。

Research on improved PSO algorithm based task allocation

To solve the task allocation in virtual enterprise, a multi-object decision-making optimization model on task allocation is constructed. The traditional Particle Swarm Optimization（PSO） algorithm is analyzed. It is improved by automatically adjusting the weight of speed inertia and acceleration coefficient, and by introducing the mutation operation in genetic algorithm. In process of solving the task allocation model by the improved PSO algorithm, the mapping between problems and particles and the computing method of particle position fitness value by Technique for Order Preference by Similarity to Ideal Solution（TOPSIS） are researched. Then, a task allocation algorithm based on the improved PSO algorithm is designed. Finally, the feasibility and validity of the method is verified by an application example and a simulation test.