A Spider Monkey Optimization Based on Beta-Hill Climbing Optimizer for Unmanned Combat Aerial Vehicle (UCAV)
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 co...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/5/3273 |
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| author | Fouad Allouani Abdelaziz Abboudi Xiao-Zhi Gao Sofiane Bououden Ilyes Boulkaibet Nadhira Khezami Fatma Lajmi |
| author_facet | Fouad Allouani Abdelaziz Abboudi Xiao-Zhi Gao Sofiane Bououden Ilyes Boulkaibet Nadhira Khezami Fatma Lajmi |
| author_sort | Fouad Allouani |
| collection | DOAJ |
| description | 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 Beta-Hill 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 large-scale (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. |
| first_indexed | 2024-03-11T07:29:22Z |
| format | Article |
| id | doaj.art-093660eae7b049989b5a717f2208e612 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T07:29:22Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-093660eae7b049989b5a717f2208e6122023-11-17T07:21:35ZengMDPI AGApplied Sciences2076-34172023-03-01135327310.3390/app13053273A Spider Monkey Optimization Based on Beta-Hill Climbing Optimizer for Unmanned Combat Aerial Vehicle (UCAV)Fouad Allouani0Abdelaziz Abboudi1Xiao-Zhi Gao2Sofiane Bououden3Ilyes Boulkaibet4Nadhira Khezami5Fatma Lajmi6Laboratory of SATIT, Department of Industrial Engineering, Abbes Laghrour University, Khenchela 40004, AlgeriaDepartment of Mechanical Engineering, Abbes Laghrour University, Khenchela 40004, AlgeriaSchool of Computing, University of Eastern Finland, 70210 Kuopio, FinlandLaboratory of SATIT, Department of Industrial Engineering, Abbes Laghrour University, Khenchela 40004, AlgeriaCollege of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitCollege of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitENISO Laboratory: Networked Objectives, Control, and Communication Systems, National Engineering School of Sousse, Sousse 4023, TunisiaUnmanned 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 Beta-Hill 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 large-scale (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.https://www.mdpi.com/2076-3417/13/5/3273unmanned combat aerial vehicle (UCAV)path planningspider monkey optimization (SMO)beta-hill climbing optimizer (BHC) |
| spellingShingle | Fouad Allouani Abdelaziz Abboudi Xiao-Zhi Gao Sofiane Bououden Ilyes Boulkaibet Nadhira Khezami Fatma Lajmi A Spider Monkey Optimization Based on Beta-Hill Climbing Optimizer for Unmanned Combat Aerial Vehicle (UCAV) Applied Sciences unmanned combat aerial vehicle (UCAV) path planning spider monkey optimization (SMO) beta-hill climbing optimizer (BHC) |
| title | A Spider Monkey Optimization Based on Beta-Hill Climbing Optimizer for Unmanned Combat Aerial Vehicle (UCAV) |
| title_full | A Spider Monkey Optimization Based on Beta-Hill Climbing Optimizer for Unmanned Combat Aerial Vehicle (UCAV) |
| title_fullStr | A Spider Monkey Optimization Based on Beta-Hill Climbing Optimizer for Unmanned Combat Aerial Vehicle (UCAV) |
| title_full_unstemmed | A Spider Monkey Optimization Based on Beta-Hill Climbing Optimizer for Unmanned Combat Aerial Vehicle (UCAV) |
| title_short | A Spider Monkey Optimization Based on Beta-Hill Climbing Optimizer for Unmanned Combat Aerial Vehicle (UCAV) |
| title_sort | spider monkey optimization based on beta hill climbing optimizer for unmanned combat aerial vehicle ucav |
| topic | unmanned combat aerial vehicle (UCAV) path planning spider monkey optimization (SMO) beta-hill climbing optimizer (BHC) |
| url | https://www.mdpi.com/2076-3417/13/5/3273 |
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