The fruit fly optimization algorithm based on normal cloud model

Abstract

In order to overcome the demerits of fruit fly optimization algorithm (FOA), such as easily relapsing into local optimum and unstable results which are caused by strong dependence on the selection of algorithm parameters, The cloud model theory is introduced into the algorithm improvement, and the algorithm is optimized and improved from two aspects: the optimal step size of the algorithm and the optimal solution generation mechanism. Firstly, the conception of taste concentration introduced and adjusted adaptively for controlling search step to improve the global search ability and local optimization ability of the algorithm. Then, the randomness and fuzziness of smell concentration parameter is described by normal cloud model and adjusted to finish osphresis search operation automatically to improve the searching precision of the algorithm. Finally, The improved algorithm is used to the automatic test, compared and analyzed with the experiment of other FOA in reference literatures. The results of experiment show that the improved algorithm has better advantages of test efficiency and accuracy.