A Spider Monkey Optimization Based on Beta-Hill Climbing Optimizer for Unmanned Combat Aerial Vehicle (UCAV)
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Date
2023-03-03
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MDPI
Abstract
Unmanned Combat Aerial Vehicle (UCAV) path planning is a challenging optimization
problem that seeks the optimal or near-optimal flight path for military operations. The problem
is further complicated by the need to operate in a complex battlefield environment with minimal
military risk and fewer constraints. To address these challenges, highly sophisticated control methods
are required, and Swarm Intelligence (SI) algorithms have proven to be one of the most effective
approaches. In this context, a study has been conducted to improve the existing Spider Monkey
Optimization (SMO) algorithm by integrating a new explorative local search algorithm called BetaHill Climbing Optimizer (BHC) into the three main phases of SMO. The result is a novel SMO
variant called SMOBHC, which offers improved performance in terms of intensification, exploration,
avoiding local minima, and convergence speed. Specifically, BHC is integrated into the main SMO
algorithmic structure for three purposes: to improve the new Spider Monkey solution generated in
the SMO Local Leader Phase (LLP), to enhance the new Spider Monkey solution produced in the SMO
Global Leader Phase (GLP), and to update the positions of all Local Leader members of each local
group under a specific condition in the SMO Local Leader Decision (LLD) phase. To demonstrate the
effectiveness of the proposed algorithm, SMOBHC is applied to UCAV path planning in 2D space
on three different complex battlefields with ten, thirty, and twenty randomly distributed threats
under various conditions. Experimental results show that SMOBHC outperforms the original SMO
algorithm and a large set of twenty-six powerful and recent evolutionary algorithms. The proposed
method shows better results in terms of the best, worst, mean, and standard deviation outcomes
obtained from twenty independent runs on small-scale (D = 30), medium-scale (D = 60), and largescale (D = 90) battlefields. Statistically, SMOBHC performs better on the three battlefields, except in
the case of SMO, where there is no significant difference between them. Overall, the proposed SMO
variant significantly improves the obstacle avoidance capability of the SMO algorithm and enhances
the stability of the final results. The study provides an effective approach to UCAV path planning
that can be useful in military operations with complex battlefield environments.
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Citation
Allouani, F.; Abboudi, A.; Gao, X.-Z.; Bououden, S.; Boulkaibet, I.; Khezami, N.; Lajmi, F. A Spider Monkey Optimization Based on Beta-Hill Climbing Optimizer for Unmanned Combat Aerial Vehicle (UCAV). Appl. Sci. 2023, 13, 3273. https://doi.org/10.3390/ app13053273