摘要
为了提高粒子群优化算法的性能,提出了一种惯性权值调整的改进粒子群优化算法,该算法的惯性权值满足不同粒子对全局和局部搜索能力的不同需求,每次迭代后根据适应度值对惯性权值做相应的调整。对4个典型的测试函数进行仿真表明,该算法比标准粒子群优化算法有更好的收敛性和更快的收敛速度,改善了优化性能。
To enhance the performance of the particle swarm optimization, the individual inertia weight adjustment pertide swarm optimization is proposed. Each particle has an individual inertia weight, which can provide the different global and local searching performances for particles. The inertia weight will be adjusted by the fitness of adaptive degree. Experimental results show that the new algorithm can greatly improve the global convergence ability and enhance the rate of convergence.
出处
《计算机技术与发展》
2008年第11期106-108,共3页
Computer Technology and Development
关键词
粒子群优化算法
适应度
惯性权值
particle swarm optimization
adaptive degree
inertia weight
