Moth Flame Optimization Based on Golden Section Search and its Application for Link Prediction Problem

Abstract

Moth Flame Optimization (MFO) is one of the meta-heuristic algorithms that recently proposed. MFO is inspired from the method of moths' navigation in natural world which is called transverse orientation. This paper presents an improvement of MFO algorithm based on Golden Section Search method (GSS), namely GMFO. GSS is a search method aims at locating the best maximum or minimum point in the problem search space by narrowing the interval that containing this point iteratively until a particular accuracy is reached. In this paper, the GMFO algorithm is tested on fifteen benchmark functions. Then, GMFO is applied for link prediction problem on five datasets and compared with other well-regarded meta- heuristic algorithms. Link prediction problem interests in predicting the possibility of appearing a connection between two nodes of a network, while there is no connection between these nodes in the present state of the network. Based on the experimental results, GMFO algorithm significantly improves the original MFO in solving most of benchmark functions and providing more accurate prediction results for link prediction problem for major datasets.