摘要
为了提高粒子群算法的性能,针对粒子群算法的早熟收敛和收敛速度问题,提出了一种改进的粒子群优化算法。在分析了粒子群算法不足的基础上,提出了两个提高算法性能的改进途径。该算法对动态惯性权重策略进行了扩展,并引入随机扰动策略,从两个方面同时改进以提高算法的收敛速度和克服局部极值的能力。函数测试的结果表明,该算法能显著提高收敛速度,并能有效克服局部极值。
In order to improve the performance of particle swarm algorithm, aiming at the problems of premature convergence and convergence velocity in particle swarm algorithm, an improved particle swarm optimization algorithm is proposed. On the basis of analysis of demerits of particle swarm algorithm, two of the improving measures are put forward. In the algorithm, the dynamic inertia weight is expanded, and random disturbance strategy is introduced. By combining these two methods, the convergence velocity of the algorithm and the capability of overcoming local extreme value are increased. The test restdts indicate that the algorithm enhances the convergence velocity outstandingly and averting the local extreme values effectively.
出处
《自动化仪表》
CAS
北大核心
2009年第7期28-30,共3页
Process Automation Instrumentation
关键词
粒子群
优化算法
动态惯性权重
随机扰动
收敛速度
Particle swarm Optimization algorithm Dynamic inertia weight Random disturbance Convergence velocity
