随机微粒群优化算法  

Stochastic Particle Swarm Optimization Algorithm

作　　者：张燕[1] 汪镭[1] 吴启迪[1] 

机构地区：[1]同济大学电子与信息工程学院,上海200092

出　　处：《计算机工程》2006年第16期9-10,16,共3页Computer Engineering

基　　金：国家自然科学基金资助项目(70271035;60104004);上海市启明星计划资助项目(03QG14053);国家"973"计划基金资助项目(2002CB312202);国际合作子项目(合作方--微软上海公司)"车载系统的导航算法研究"

摘　　要：微粒群优化算法是继蚁群算法之后又一种新的基于群体智能的启发式全局优化算法,其概念简单、易于实现,而且具有良好的优化性能,目前已在许多领域得到应用。但在求解高维多峰函数寻优问题时,算法易陷入局部最优。该文结合模拟退火算法的思想,提出了一种改进的微粒群优化算法——随机微粒群优化算法,该算法在运行初期具有更强的探索能力,可以避免群体过早陷入局部极值点。基于典型高维复杂函数的仿真结果表明,与基本微粒群优化算法相比,该混合算法具有更好的优化性能。Particle swarm optimization （PSO） is a new heuristic global optimization algorithm based on swarm intelligence after ant colony algorithm. The algorithm is simple, easy to implement and has good performance of optimization. Now it has been applied in many fields. However, when optimizing multidimensional and multimodal functions, the basic particle swarm optimization is apt to be trapped in local optima. This paper proposes a modified optimization method——stochastic particle swarm optimization （SPSO）, which combines the standard version with simulated annealing algorithm. This modified version has stronger exploitation ability at the beginning, so it can keep particle swarm from getting into local optima too early. Simulation results on benchmark complex functions with high dimension show that this hybrid algorithm performs better than the basic particle swarm optimization.

关 键 词：微粒群优化算法 群体智能 模拟退火 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]