Accelerated Opposition-Based Antlion Optimizer with Application to Order Reduction of Linear Time-Invariant Systems

Abstract

This paper proposes a novel variant of antlion optimizer (ALO), namely accelerated opposition-based antlion optimizer (OB-ac-ALO). This modified version is conceptualized with opposition-based learning (OBL) model and integrated with acceleration coefficient (ac). The OBL model approximates the original as well as opposite candidate solutions simultaneously during evolution process. The implementation of OBL technique is collaborated with an exploitation acceleration coefficient which is useful in local searching and tends to find global optimum efficiently. A position update equation is formulated using these strategies. To validate the proposed technique, a broad set of 21 benchmark test suit of extensive variety of features is chosen. To analyse the performance of proposed algorithm, various analysis metrics such as search history, trajectories and average distance between search agents before and after improving the algorithm are performed. A nonparametric Wilcoxon ranksum test is applied to show its statistical significance. It is applied to solve a real-world application for approximating the higher-order linear time-invariant system to its corresponding lower-order invariant system. Three single-input single-output problems have been considered in terms of integral square error.