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

Authors: K. Narsimha Reddy; Polaiah Bojja

Addresses: Department of ECE, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur District, Andhra Pradesh 522502, India ' Department of ECE, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur District, Andhra Pradesh 522502, India

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.

Keywords: population-based algorithm; meta-heuristic; differential evolution; DE; global optimisation; visual object tracking.

DOI: 10.1504/IJBIC.2022.128097

International Journal of Bio-Inspired Computation, 2022 Vol.20 No.4, pp.220 - 231

Accepted: 04 Jun 2021
Published online: 05 Jan 2023 *

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