摘要
为了求解单目标线性规划问题,提出了基于混沌优化(COA)算法的仿射尺度搜索(AFS)算法,即混沌AFS算法。使用混沌优化算法以迭代方式从随机初始点中得到优化的初始点;将得到的初始解点作为仿射尺度搜索算法的起始点来提高仿射尺度搜索算法的性能;通过搜索单目标线性规划决策变量域得到可行的近似最优解。实验结果表明,相比传统的AFS算法,在求解单目标线性优化问题时所提混沌AFS算法明显降低了目标值的偏差,同时大大地减少了迭代次数及CPU运行时间。
To address Single Objective Linear Programming（SOLP） problem, Affine Scaling Search（AFS）algorithm based on Chaos Optimization Algorithm（COA）which can be called chaos AFS algorithm is proposed. Firstly, COA is used to get optimal initial points from random initial points by iteration. Then, the initial solution points are considered as initial points of AFS algorithm to improve its performance. Finally, feasible approximate optimal solution is got by searching decision variable domain of SOLP problem. Experimental results show that proposed chaos AFS algorithm has reduced the deviation of the target clearly and has less numbers of iterations and CPU running time comparing with traditional AFS algorithm.
出处
《计算机工程与应用》
CSCD
2014年第7期56-61,共6页
Computer Engineering and Applications
基金
浙江省科技厅高技能人才培养项目(No.2011R30057)
