摘要
针对粒子群算法中加速系数的取值问题,对C1和C2的各种取值策略做了充分调查分析,得到参数C1和C2对算法性能的影响规律,并提出了一种基于随机加速系数的粒子群优化算法.该算法在迭代的每一代中,加速系数取一定范围内随机产生的一组C1和C2的组合,通过非对称的、范围更大的C1和C2取值来增加算法的多样性,避免了算法早熟.在典型测试函数上进行对比实验,结果验证了新算法在优化性能和稳定性上高于传统粒子群优化算法.
Research into setting the values of the acceleration coefficients C1 and C2 in particle swarm optimization（PSO） is one of the most significant areas for performance improvement. On the basis of the investigation of C1 and C2, this paper present the parameters investigation conclusion and proposed a novel PSO which C1 and C2 are generated as a couple of random numbers within a certain range in each iteration process. The experimental results show that the new method per： forms better than the methods with fixed values of C1 and C2 for most benchmark problems tested.
出处
《微电子学与计算机》
CSCD
北大核心
2010年第6期114-117,共4页
Microelectronics & Computer
关键词
粒子群优化算法
随机加速系数
单峰函数
多峰函数
particle swarm optimization（PSO）
random acceleration coefficients
unimodal funtion
multimodal funtion
