受战斗机协同战术启发，提出新型优化算法，高效解决复杂工程与山地路径规划问题。
Dogfight Search: A Swarm-Based Optimization Algorithm for Complex Engineering Optimization and Mountainous Terrain Path Planning
- 基于运动学位移积分构建搜索机制，摆脱传统类比隐喻
- 在10个真实约束优化问题和山地路径规划中超越7种先进算法
- 代码开源，适合工程优化与复杂地形导航场景使用
战斗机间的协同战术启发了本文提出的新型元启发式算法Dogfight Search（DoS）。与传统算法不同，DoS虽源于战术灵感，但其搜索机制基于运动学中的位移积分方程构建。通过在CEC2017、CEC2022基准测试函数，以及10个真实世界约束优化问题和山地地形路径规划任务上的实验验证，DoS整体性能显著优于7种先进竞争算法，并在Friedman排名中位列第一。进一步对比3种SOTA算法在CEC2017和CEC2022上的表现，结果表明DoS持续保持领先优势，展现出强大竞争力。DoS源代码已公开：https://ww2.mathworks.cn/matlabcentral/fileexchange/183519-dogfight-search。
原文摘要 · Abstract (English)
Dogfight is a tactical behavior of cooperation between fighters. Inspired by this, this paper proposes a novel metaphor-free metaheuristic algorithm called Dogfight Search (DoS). Unlike traditional algorithms, DoS draws algorithmic framework from the inspiration, but its search mechanism is constructed based on the displacement integration equations in kinematics. Through experimental validation on CEC2017 and CEC2022 benchmark test functions, 10 real-world constrained optimization problems and mountainous terrain path planning tasks, DoS significantly outperforms 7 advanced competitors in overall performance and ranks first in the Friedman ranking. Furthermore, this paper compares the performance of DoS with 3 SOTA algorithms on the CEC2017 and CEC2022 benchmark test functions. The results show that DoS continues to maintain its lead, demonstrating strong competitiveness. The source code of DoS is available at https://ww2.mathworks.cn/matlabcentral/fileexchange/183519-dogfight-search.
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