MODEFIED MOTH-FLAME OPTIMIZATION ALGORITHMS FOR TERRORISM PREDICTION

ABSTRACT In this work, new versions of moth-flame optimization algorithm are proposed and used to select optimal feature subset for the classification purposes. Moth-flame Optimization (MFO) algorithm is one of the newest bio inspired optimization techniques in which the main inspiration of this optimizer is the navigation method of moths in nature called transverse orientation. Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling in a straight line for long distances. However, these fancy insects are trapped in a useless/deadly spiral path around artificial lights. This paper presents two modified versions of MFO algorithm which used as a prediction method for terrorist groups. The two modified versions of MFO are compared with the original MFO as well as compared to other well known nature-inspired algorithms as Ant Lion Optimizer (ALO) algorithm, Grey Wolf Optimization (GWO) algorithm, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Two different experiments modes are conducted and two well-known classification algorithms are used in the classification process; Random Forests (RF) ensemble classifier and K-Nearest Neighbor (KNN) algorithm. A set of assessment indicators are used to evaluate and compare between the obtained results which prove that the proposed modified versions of MFO provide very promising and competitive performance as well as achieve an advance over the original MFO algorithm with high stability over other searching methods. Keywords: Moth-Flame Algorithm, Random-Forests, Terrorism Prediction