Dogfight Search: A Swarm-Based Optimization Algorithm for Complex Engineering Optimization and Mountainous Terrain Path Planning

📰 ArXiv cs.AI

Dogfight Search is a novel swarm-based optimization algorithm inspired by tactical fighter behavior for complex engineering optimization and path planning

advanced Published 31 Mar 2026
Action Steps
  1. Understand the Dogfight Search algorithm's framework and its inspiration from kinematics
  2. Apply the algorithm to complex engineering optimization problems using benchmark test functions like CEC2017 and CEC2022
  3. Evaluate the performance of Dogfight Search against traditional optimization algorithms
  4. Integrate Dogfight Search into path planning systems for mountainous terrain navigation
Who Needs to Know This

AI engineers and researchers can benefit from this algorithm for solving complex optimization problems, while software engineers can apply it to develop more efficient path planning systems

Key Insight

💡 Dogfight Search algorithm draws its framework from kinematics and can be used for complex optimization problems and path planning

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🚀 Dogfight Search: a novel swarm-based optimization algorithm for complex engineering optimization and path planning #AI #optimization

Key Takeaways

Dogfight Search is a novel swarm-based optimization algorithm inspired by tactical fighter behavior for complex engineering optimization and path planning

Full Article

Title: Dogfight Search: A Swarm-Based Optimization Algorithm for Complex Engineering Optimization and Mountainous Terrain Path Planning

Abstract:
arXiv:2603.28046v1 Announce Type: new Abstract: 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 r
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