狗群搜索：一种基于群体的优化算法，用于复杂工程优化和山地地形路径规划
Dogfight Search: A Swarm-Based Optimization Algorithm for Complex Engineering Optimization and Mountainous Terrain Path Planning
AI总结：
本文提出了一种名为狗群搜索（DoS）的新型元启发式算法，通过动力学位移积分方程构建搜索机制，在复杂工程优化和山地路径规划中表现出色，优于7种先进算法。
摘要：
狗群是一种战斗机之间的协作战术。受此启发，本文提出了一种无需比喻的新型元启发式算法，称为狗群搜索（DoS）。与传统算法不同，DoS的算法框架源于灵感，但其搜索机制基于动力学位移积分方程。通过在CEC2017和CEC2022基准测试函数、10个现实约束优化问题和山地地形路径规划任务上的实验验证，DoS在整体性能上显著优于7种先进算法，并在Friedman排名中排名第一。此外，本文将DoS与3种最先进的算法在CEC2017和CEC2022基准测试函数上的性能进行比较。结果表明，DoS持续保持领先，显示出强大的竞争力。DoS的源代码可在https://ww2.mathworks.cn/matlabcentral/fileexchange/183519-dogfight-search获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
