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

TL;DR

Dogfight Search (DoS) is a novel, metaphor-free metaheuristic inspired by tactical fighter cooperation, demonstrating superior performance in complex optimization and terrain path planning tasks through extensive benchmarking.

Contribution

This paper introduces Dogfight Search, a new optimization algorithm based on displacement equations, outperforming state-of-the-art methods in various benchmark and real-world problems.

Findings

DoS outperforms 7 advanced competitors on benchmark tests.

DoS ranks first in the Friedman ranking for overall performance.

Source code is publicly available at the provided URL.

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 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.