摘要
文章针对约束非线性优化问题,将微粒群优化算法(PSO)和序贯二次规划(SQP)算法结合起来,提出了一种解决此类问题的有效算法。PSO可以看作是全局搜索器,而SQP则主要执行局部搜索。对于那些具有多个局部极值点的优化问题,大大增加了获得全局极值点的几率。由于PSO具有快速全局收敛的特点,同时SQP的局部搜索能力很强,所以所提算法可以快速获得全局最优值。将基于PSO的序贯二次规划算法在两个标准优化问题上进行仿真,结果证明与标准的PSO和SQP相比,算法具有明显的优越性。
This paper presents a novel and efficient method for solving the constrained nonlinear optimization problems,by combining the Particle Swarm Optimization（PSO） technique with the Sequential Quadratic Programming（SQP）.PSO can be viewed as the global optimizer while the SQP is employed for the local search.Thus,the possibility of exploring a global minimum in problems with more local optima is increased.Benefit from the fast globally converging characteristics of PSO and the effective local search ability of SQP,the proposed method can obtain the global optimal result quickly.The proposed method is test for two benchmark optimization problems and the improved performance comparing with the standard PSO and SQP techniques testifies its validity.
出处
《计算机工程与应用》
CSCD
北大核心
2006年第23期69-71,共3页
Computer Engineering and Applications
关键词
微粒群优化算法
序贯二次规划
非线性优化
Particle Swarm Optimization, Sequential Quadratic Programming, nonlinear optimization
