A Novel Enhanced Metaheuristic Algorithm for Automobile Cruise Control System

Abstract

The development of a novel enhanced metaheuristic algorithm is considered in this paper. Such a structure was achieved through enhancement of the arithmetic optimization algorithm by employing the opposition-based learning mechanism together with the Nelder-Mead simplex search method. The developed algorithm (ObAOANM) adopts the opposition-based learning scheme to enhance the algorithm in terms explorative behavior, and the Nelder-Mead method in terms of exploitative behavior. The developed ObAOANM was firstly tested against well-known unimodal and multimodal benchmark functions through comparisons with the original arithmetic optimization algorithm, as it was previously shown to be superior to other efficient algorithms. The benchmark functions and related statistical results demonstrated greater capability of the ObAOANM algorithm. Then, the ObAOANM algorithm was utilized to achieve an optimum design of a proportional-integral-derivative controller adopted in an automobile cruise control system. The performance of the ObAOANM algorithm was compared with the arithmetic optimization algorithm algorithm through statistical, transient response, frequency response, and disturbance rejection analyses, which have shown better capability of the enhanced ObAOANM algorithm. Furthermore, the capability of the ObAOANM based proportional-integral-derivative-controlled automobile cruise control system was compared with other available approaches in the literature by performing time domain analysis, which also confirmed the superior capability of the proposed approach for such a task.