摘要
针对传统的离散粒子群优化算法后期容易陷入局部收敛这一缺点,提出了一种新的离散粒子群算法,使用了新的运动方程来更新粒子的位置,并将贪心算法的思想引入粒子群优化算法中,在粒子的位置初始化的过程中,把采用贪心策略所得出的结果作为一个粒子的初始位置.用改进的算法求解背包问题,通过与其他文献中仿真实例的计算和结果比较,表明该算法在全局寻优能力和收敛性上都优于传统的粒子群算法.
Traditional discrete particle swarm optimization （DPSO） is easy to incur a local convergence in optimization. So we propose an improved DPSO algorithm in which a new motion equation is adopted to update the position of particles. The concept of Greedy algorithm is introduced into the DPSO algorithm, In the course of initialization, we use the results obtained from greedy strategy as the initial position of a particle. The knapsack problem was worked out with the proposed algorithm, showing that it is advantageous in global searching and convergence properties than traditional DPSO.
出处
《应用科技》
CAS
2008年第3期16-19,共4页
Applied Science and Technology
关键词
背包问题
粒子群优化算法
贪心算法
knapsack problem
particle swarm optimization algorithm
greedy algorithm
