一种求解约束优化问题的混合算法  

Hybrid algorithm for solving constrained optimization problems

作　　者：龙文[1,2] 梁昔明[3] 焦建军[1,2] 

机构地区：[1]贵州财经学院贵州省经济系统仿真重点实验室,贵阳550004 [2]贵州财经学院数学与统计学院,贵阳550004 [3]中南大学信息科学与工程学院,长沙410083

出　　处：《计算机工程与应用》2012年第9期9-11,共3页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61074069);贵州财经学院引进人才科研项目

摘　　要：提出一种基于修改增广Lagrange函数和PSO的混合算法用于求解约束优化问题。将约束优化问题转化为界约束优化问题,混合算法由两层迭代结构组成,在内层迭代中,利用改进PSO算法求解界约束优化问题得到下一个迭代点。外层迭代主要修正Lagrange乘子和罚参数,检查收敛准则是否满足,重构下次迭代的界约束优化子问题,检查收敛准则是否满足。数值实验结果表明该混合算法的有效性。A hybrid algorithm based on modified augmented Lagrange function and PSO is proposed for solving constrained optimiza- tion problems. The general constrained optimization problem is converted into a bound constrained optimization problem. The basic steps off the proposed hybrid algorithm comprise an outer iteration and an inner iteration. The inner iteration, in which a nonlinear bound constrained minimization sub-problem of the modified augmented Lagrange multiplier, is solved by improved PSO algorithm. The outer iteration is performed to update the Lagrange multipliers and penalty parameters using a first-order update scheme, check for convergence and accordingly reinitiate another bound constrained minimization or declare convergence. The proposed algorithm is test- ed on 8 well-known benchmark constrained optimization problems, and the results show that it is very suitable and steadier than other algorithms from the literature for different constrained optimization problems.

关 键 词：增广LAGRANGE函数 约束优化问题 粒子群优化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]