Ill-conditioned Problems Robust Solution of Improved Fruit Fly Optimization Algorithm Combining with Tikhonov Regularization Method

Acta Geodaetica et Cartographica Sinica

ZHANG Ning¹  FAN Qian² 

[1] ;College of Civil Engineering, Fuzhou University, Fuzhou 350108, China;

关键词: fruit fly optimization;    random search direction;    Tikhonov regularization method;    ill-conditioned problems solution;    gross error;   

DOI  :  10.11947/j.AGCS.2016.20150606

来源: DOAJ

【 摘 要 】

Based on deeply analysis for optimization process of basic fruit fly optimization algorithm, an improved fruit fly optimization (IFOA) algorithm is proposed via changing random search direction and adding to a tuning coefficient of search radius. Moreover, through introducing the regularization term of objective function in IFOA algorithm, a new method that IFOA algorithm is combined with Tikhonov regularization method is put forward in order to resolving ill-conditioned problems. Analysis results of practical example show that solution accuracy of new method is superior to genetic algorithm and single Tikhonov regularization method. When observation contains gross errors, the deviation between the results and the true value will increase rapidly using least square method to solve ill-conditioned problems. At this time, the new method has strong robustness. Compared with intelligent search method represented by genetic algorithm, new method has the characteristics of less parameter, fast calculation speed, simple optimization process. It is more practical in ill-conditioned problems solution.

【 授权许可】

Unknown