Dogfight Search: A Swarm-Based Optimization Algorithm for Complex Engineering Optimization and Mountainous Terrain Path Planning
Summary: arXiv:2603.28046v1 Announce Type: new
Abstract
The concept of dogfight, characterized by tactical cooperation among fighters, serves as the foundational inspiration for a groundbreaking metaheuristic algorithm known as Dogfight Search (DoS). This innovative algorithm diverges from traditional optimization methods by utilizing a metaphor-free approach, where its search mechanism is derived from the principles of displacement integration equations found in kinematics.
Key Features of Dogfight Search
Dogfight Search stands out among optimization algorithms due to several key features:
- Metaphor-Free Approach: Unlike many algorithms that rely on anthropomorphic inspirations, DoS is built on mathematical principles, allowing for a more systematic exploration of the solution space.
- Performance Validation: Extensive experimental validation has been conducted on CEC2017 and CEC2022 benchmark test functions, demonstrating the algorithm’s effectiveness in various scenarios.
- Real-World Applications: DoS has been tested on ten real-world constrained optimization problems, showcasing its applicability in practical engineering challenges.
- Mountainous Terrain Path Planning: The algorithm excels not only in optimization but also in complex path planning tasks, particularly in challenging mountainous terrains.
Experimental Results
The performance of Dogfight Search has been rigorously compared against seven advanced competitors across various metrics. The results indicate that DoS consistently outperforms its rivals, achieving the top rank in the Friedman ranking. Additionally, a comparison with three state-of-the-art (SOTA) algorithms on the CEC2017 and CEC2022 benchmark test functions further underscores the robustness and competitiveness of DoS.
Conclusions
The findings from the experimental validation highlight the potential of Dogfight Search as a leading algorithm in the field of optimization. Its novel approach, rooted in kinematic principles, not only enhances performance on benchmark tests but also offers significant advantages in real-world applications and complex path planning scenarios.
Availability
For those interested in exploring the capabilities of Dogfight Search further, the source code is available for download at the following link: Dogfight Search Source Code.
Future Directions
As the field of optimization continues to evolve, the Dogfight Search algorithm presents a promising avenue for future research. Potential areas for exploration include:
- Further refinement of the kinematic principles underlying the search mechanism.
- Application of DoS to other complex optimization problems across various domains.
- Integration of DoS with hybrid optimization techniques to enhance performance even further.
In summary, Dogfight Search represents a significant advancement in optimization algorithms, with the potential to impact various engineering fields and applications.