An efficient quantum multiverse optimization algorithm for solving optimization problems

Abstract

Due to the recent trend of technologies to use the network-based systems, detecting them from threats become a crucial issue. Detecting unknown or modified attacks is one of the recent challenges in the field of intrusion detection system (IDS). In this research, a new algorithm called quantum multiverse optimization (QMVO) is investigated and combined with an artificial neural network (ANN) to develop advanced detection approaches for an IDS. QMVO algorithm depends on adopting a quantum representation of the quantum interference and operators in the multiverse optimization to obtain the optimal solution. The QMVO algorithm determining the neural network weights based on the kernel function, which can improve the accuracy and then optimize the training part of the artificial neural network. It is demonstrated 99.98% accuracy with experimental results that the proposed QMVO is significantly improved optimization compared with multiverse optimizer (MVO) algorithms.