An Improved Crayfish Optimization Algorithm: Enhanced Search Efficiency and Application to UAV Path Planning

The resolution of the unmanned aerial vehicle (UAV) path-planning problem frequently leverages optimization algorithms as a foundational approach. Among these, the recently proposed crayfish optimization algorithm (COA) has garnered significant attention as a promising and noteworthy alternative. Ne...

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Veröffentlicht in:Symmetry (Basel) Jg. 17; H. 3; S. 356
Hauptverfasser: Huang, Qinyuan, Sun, Yuqi, Kang, Chengyang, Fan, Chen, Liang, Xiuchen, Sun, Fei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.03.2025
Schlagworte:
Algorithms
Competition
Convergence
Drone aircraft
Effectiveness
Efficiency
Entrapment
Foraging behavior
Heuristic
Mathematical optimization
Nonlinear control
Obstacle avoidance
Optimization
Optimization algorithms
Path planning
Performance evaluation
Problem solving
Searching
Strategy
Temperature
Unmanned aerial vehicles
ISSN:2073-8994, 2073-8994
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Zusammenfassung:The resolution of the unmanned aerial vehicle (UAV) path-planning problem frequently leverages optimization algorithms as a foundational approach. Among these, the recently proposed crayfish optimization algorithm (COA) has garnered significant attention as a promising and noteworthy alternative. Nevertheless, COA’s search efficiency tends to diminish in the later stages of the optimization process, making it prone to premature convergence into local optima. To address this limitation, an improved COA (ICOA) is proposed. To enhance the quality of the initial individuals and ensure greater population diversity, the improved algorithm utilizes chaotic mapping in conjunction with a stochastic inverse learning strategy to generate the initial population. This modification aims to broaden the exploration scope into higher-quality search regions, enhancing the algorithm’s resilience against local optima entrapment and significantly boosting its convergence effectiveness. Additionally, a nonlinear control parameter is incorporated to enhance the algorithm’s adaptivity. Simultaneously, a Cauchy variation strategy is applied to the population’s optimal individuals, strengthening the algorithm’s ability to overcome stagnation. ICOA’s performance is evaluated by employing the IEEE CEC2017 benchmark function for testing purposes. Comparison results reveal that ICOA outperforms other algorithms in terms of optimization efficacy, especially when applied to complex spatial configurations and real-world problem-solving scenarios. The proposed algorithm is ultimately employed in UAV path planning, with its performance tested across a range of terrain obstacle models. The findings confirm that ICOA excels in searching for paths that achieve safe obstacle avoidance and lower trajectory costs. Its search accuracy is notably superior to that of the comparative algorithms, underscoring its robustness and efficiency. ICOA ensures the balanced exploration and exploitation of the search space, which are particularly crucial for optimizing UAV path planning in environments with symmetrical and asymmetrical constraints.
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ISSN:2073-8994
2073-8994
DOI:10.3390/sym17030356