An Efficient Evolution-Based Technique for Moving Target Search with Unmanned Aircraft Vehicle: Analysis and Validation

Mohamed Abdel-Basset, Reda Mohamed, Ibrahim M. Hezam (), Ahmad M. Alshamrani and Karam M. Sallam
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Mohamed Abdel-Basset: Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt
Reda Mohamed: Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt
Ibrahim M. Hezam: Department of Statistics & Operations Research, College of Sciences, King Saud University, Riyadh 11451, Saudi Arabia
Ahmad M. Alshamrani: Department of Statistics & Operations Research, College of Sciences, King Saud University, Riyadh 11451, Saudi Arabia
Karam M. Sallam: Faculty of Science and Technology, School of IT and Systems, University of Canberra, Canberra, ACT 2601, Australia

Mathematics, 2023, vol. 11, issue 12, 1-21

Abstract: Recent advances in technology have led to a surge in interest in unmanned aerial vehicles (UAVs), which are remote-controlled aircraft that rely on cameras or sensors to gather information about their surroundings during flight. A UAV requires a path-planning technique that can swiftly recalculate a viable and quasi-optimal path in flight if a new obstacle or hazard is recognized or if the target is moved during the mission. In brief, the planning of UAV routes might optimize a specific problem determined by the application, such as the moving target problem (MTP), flight time and threats, or multiobjective navigation. The complexity of MTP ranges from NP-hard to NEXP-complete because there are so many probabilistic variables involved. Therefore, it is hard to detect a high-quality solution for this problem using traditional techniques such as differential calculus. Therefore, this paper hybridizes differential evolution (DE) with two newly proposed updating schemes to present a new evolution-based technique named hybrid differential evolution (HDE) for accurately tackling the MTP in a reasonable amount of time. Using Bayesian theory, the MTP can be transformed into an optimization problem by employing the target detection probability as the fitness function. The proposed HDE encodes the search trajectory as a sequence of UAV motion pathways that evolve with increasing the current iteration for finding the near-optimal solution, which could maximize this fitness function. The HDE is extensively compared to the classical DE and several rival optimizers in terms of several performance metrics across four different scenarios with varying degrees of difficulty. This comparison demonstrates the proposal’s superiority in terms of the majority of used performance metrics.

Keywords: evolutionary algorithms; differential evolution; UAV; unmanned aerial vehicles; exploration; target search (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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