摘要
为提高约束优化模型的求解准确度和运算速度,针对粒子群算法及其计算方法进行了改进。引入多样化机制避免算法陷入局部最优的危险:创建多个子群将决策空间划分为多个搜索子空间,多子群独立搜索以保证群间解的多样化;用量子粒子代替普通粒子,为其添加服从球状分布的伴随粒子来提高群内解的多样化。多样化的引入增加了计算量和计算复杂度,利用并行计算提高算法运行速度:分析了改进粒子群算法并行计算的方法,在私有云计算平台上编写了基于MapReduce的并行求解流程。实验结果表明,本文方法具有较高准确度,算法的稳定性也较好,运算速度可成倍提高。
In order to solve constrained optimization problems with higher accuracy and faster computing speed, several improvements are raised on particle swarm optimization（PSO） and its computing method. Solu- tions＇ diversification mechanism is applied in PSO to improve its global optimization ability., decision space is di- vided into multiple searching subspaces, while multi-subswarms are created according to those searching sub- spaces, and multi-subswarms are searched independently to get solutions＇ diversification among subswarms; or- dinary particles is replaced by quantum particles in PSO, while associated particles that follow globular distribu- tion is vested in each quantum particle, which could improve solutions＇ diversification in subswarms. Running speed of the improved PSO is increased via parallel computing： Parallel computing flow of the improved PSO is analyzed based on the private cloud platform and the algorithm for the flow is programmed based on MapRe- duce. The experimental results show that the proposed method has higher accuracy solutions and stability, and the performance and computing speed is exponentially improved.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2016年第5期1086-1092,共7页
Systems Engineering and Electronics
关键词
约束优化
粒子群算法
私有云计算平台
并行求解
多样化
constrained optimization
particle swarm optimization （PSO）
private cloud platform
parallel solving diversification
