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.
Highlights
In order to improve the performance of Moth Flame Optimization algorithm, golden section search (GSS) strategy is utilized to develop a new version of MFO, which is called Golden Moth Flame Optimization (GMFO)
GMFO focuses on enhancing the convergence rate of MFO through supporting the exploration mechanism by increasing the diversification of the search space, facilitating to get more intensification toward the best solution obtained so far in each iteration
In order to test the performance of GMFO in comparison with other algorithms for link prediction problem, GMFO and other well-known meta-heuristic algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are carried out on five datasets and a comparison between them is conducted in term of the prediction accuracy measured by Area under Curve (AUC)
Summary
In order to improve the performance of Moth Flame Optimization algorithm, golden section search (GSS) strategy is utilized to develop a new version of MFO, which is called Golden Moth Flame Optimization (GMFO). GMFO focuses on enhancing the convergence rate of MFO through supporting the exploration mechanism by increasing the diversification of the search space (population), facilitating to get more intensification toward the best solution obtained so far in each iteration. As in Mirjalili (2015), to explore the search space and to keep away from local solutions, MFO's search agents spend a large number of iterations. By this way, the exploitation of the MFO algorithm slows down and prevents the algorithm from locating a much better approximation of the global solution. The number of iterations to reach optimal solution will be reduced significantly and its convergence will be improved
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