LEVYEFO-WTMTOA: The hybrid of the multi-tracker optimization algorithm and the electromagnetic field optimization

Abstract

Abstract Many engineering optimization problems can be solved using meta-heuristics. Despite their merits, such algorithms face common challenges of early convergence rate and the imbalance between the exploitation and exploration phases. These algorithms have strengths and weaknesses considering the convergence rate, local search, and global search criteria. This study presents the new algorithm called the LEVYEFO-WTMTOA that combines the Modified Multi-Tracker Optimization Algorithm (MTOA) and the electromagnetic field optimization (EFO) approach. The LEVYEFO-WTMTOA applies the following proposals to escape from local optima: 1) the Morlet wavelet transform is used to determine the Radius of Search (RS) of the MTOA; 2) a mutation phase based on the combination of the best global position in the electromagnetic field optimization algorithm and the levy law is used in the exploration phase to update the new position 3) it has focused on balancing local and global search and escaping from the local optima trap of the MTOA. To evaluate the proposed LEVYEFO-WTMTOA algorithm, the CEC2018 benchmark suite is used, and the results are compared with the original MTOA, EFO, MEFO, MVO Levy, GSA, and COA algorithms in terms of mean error. The results demonstrate that the proposed algorithm performs better than the baseline algorithms. The applications of LEVYEFO-WTMTOA on several classical engineering problems are included as well.