Hybrid moth-flame optimisation algorithm with differential evolution for visual object tracking

The improvement of the metaheuristic algorithms is one of the exciting topics to investigators in current years to resolve the optimisation and engineering problems. One of the population-based search methods, i.e., moth-flame optimisation algorithm (MFO), is eminent by easy execution, low limits, and high speed. On the other hand, the MFO process has shortcomings; for instance, the verdict of local minimum as a substitute for global minimum and weakness in global pursuit proficiency. In this paper, to resolve these shortages, the MFO algorithm is integrated with differential evolution (DE) and proposed a new hybrid method called MFO-DE. The exploration ability of the MFO algorithm is improved and existence trapped in the local minimum is prohibited by a mixture of the MFO and DE in the MFO-DE algorithm. The proposed algorithm was tested on the set of best-known unimodal and multimodal benchmark functions in various dimensions. Furthermore, MFO-DE is applied to visual object tracking as a real-life application.