An orthogonal moth flame optimization for global optimization and application to model order reduction problem

Abstract

Moth-Flame optimization is a meta-heuristic algorithm based on the navigation behaviour of moths. Generally, moth’s poses a very effective mechanism called transverse orientation while moving a long distance in night and maintain of fixed angle with respect to the moon. MFO suffers with local optima and stagnation problem, in order to improve the performance and exploration rate of the existing algorithm and for solving the complex real world problems, a new version of MFO algorithms is proposed by adding the concept of orthogonality feature. The modified algorithm is termed as orthogonal Moth-Flame optimization (OMFO) algorithm. The main objective of this OMFO is going to solve the convergence problem to minimization of the search space and avoid the local optima. The proposed method can also be used to maintain the balance between exploration and exploitation. In this work, a set of 28 standard IEEE CEC 2017 benchmark test functions with 10 and 30 dimensions are used to evaluate and compare between the obtained results which prove that the proposed OMFO gives very promising and competitive performance as well as achieve better performance over original MFO algorithm with high stability over searching method. The efficiency of the proposed method is verified by applying in model order reduction problem. The performed analysis such as statistical measure, convergence analysis and complexity measure reveal that the proposed method is reliable and efficient in solving practical optimization problems.