改进DPSO算法求解仿真任务调度问题

Apply Modified Discrete Particle Swarm Optimization Algorithm to Solve Simulation Task Scheduling Problem

  • 摘要: 针对离散粒子群算法在求解雷达分布式仿真系统中的仿真任务调度时,由于其易陷入局部最优的缺陷导致算法受初始种群的影响较大且结果稳定低的问题,提出基于信息素变异策略的改进离散粒子群算法。文中分析了离散粒子群算法容易陷入局部最优的原因,引入基于信息素的变异策略,充分利用种群中所有粒子的寻优经验信息来累计信息素,以信息素的分布和效率矩阵为依据对基本离散粒子群算法每次迭代后得到的粒子进行变异操作。仿真结果表明,改进算法有效地避免了算法陷于局部最优的问题,且结果的稳定性比基本离散粒子群算法更好,调度跨度和负载平衡度相比离散粒子群算法,蚁群算法,Max-Min算法和Min-Min算法都有明显的改善。

     

    Abstract: Aiming at the problems that the algorithm was greatly influenced by initial population and the result was not stable when solving the simulation task scheduling in distributed radar simulation system, due to the defects that the discrete particle swarm optimization (DPSO) algorithm was easy to encounter local optimization, a modified DPSO algorithm based on the mutation strategy of pheromone was proposed. The mutation strategy of pheromone was first introduced in this paper after analysis the cause of high probability to encounter local optimization. Then the optimization empirical information of all particles in swarm was fully used to accumulate pheromone. Finally, mutation operation was conducted on particles obtained from iteration of DPSO according to the distribution of pheromone and efficiency matrix. Simulation results show the effectiveness to avoid the problem of local optimization and the better stability of results than DPSO. Meanwhile, the scheduling span and load balance are both improved greatly compared with DPSO, ACO, Max-Min and Min-Min.

     

/

返回文章
返回