Abstract
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.
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