A New Fusion of ASO with SA Algorithm and Its Applications to MLP Training and DC Motor Speed Control

Abstract

An improved version of atom search optimization (ASO) algorithm is proposed in this paper. The search capability of ASO was improved by using simulated annealing (SA) algorithm as an embedded part of it. The proposed hybrid algorithm was named as hASO-SA and used for optimizing nonlinear and linearized problems such as training multilayer perceptron (MLP) and proportional-integral-derivative controller design for DC motor speed regulation as well as testing benchmark functions of unimodal, multimodal, hybrid and composition types. The obtained results on classical and CEC2014 benchmark functions were compared with other metaheuristic algorithms, including two other SA-based hybrid versions, which showed the greater capability of the proposed approach. In addition, nonparametric statistical test was performed for further verification of the superior performance of hASO-SA. In terms of MLP training, several datasets were used and the obtained results were compared with respective competitive algorithms. The results clearly indicated the performance of the proposed algorithm to be better. For the case of controller design, the performance evaluation was performed by comparing it with the recent studies adopting the same controller parameters and limits as well as objective function. The transient, frequency and robustness analysis demonstrated the superior ability of the proposed approach. In brief, the comparative analyses indicated the proposed algorithm to be successful for optimization problems with different nature.