A new hybrid optimization method combining moth-flame optimization and teaching-learning-based optimization algorithms for visual tracking

In this paper, a new hybrid algorithm based on moth-flame optimization (MFO) and teaching-learning-based optimization (TLBO) algorithm named as MFO-TLBO is proposed to overwhelm their shortcomings and inherit their advantages using the low-level coevolutionary mixed hybrid. In the best interests of this, we progress the competence of exploitation in TLBO with the ability of exploration in the MFO algorithm to demonstrate the metiers of both methods. The sole inspiration behind integrating modifications in MFO is to benefit the procedure to avoid immature convergence and to steer the search in the direction of the potential search region in a quicker way. The proposed algorithm was tested on the set of best known unimodal and multimodal benchmark functions in various dimensions. The obtained results from basic and nonparametric statistical tests confirmed that this hybrid method dominates in terms of convergence and success rate. Furthermore, MFO-TLBO is applied to visual tracking as a real-life application. All experimental outcomes, illustrations and comparative investigation found that the MFO-TLBO algorithm can vigorously track a random target object in many stimulating circumstances than the other trackers successfully.