Open Access

ARTICLE

Dung Beetle Optimization Algorithm Based on Bounded Reflection Optimization and Multi-Strategy Fusion for Multi-UAV Trajectory Planning

Weicong Tan^1,#, Qiwu Wu^2,3,#,*, Lingzhi Jiang¹, Tao Tong², Yunchen Su²

1 School of Information Engineering, Engineering University of PAP, Xi’an, 710086, China
2 School of Equipment Management and Support, Engineering University of PAP, Xi’an, 710086, China
3 Key laboratory of CTC&IE (Engineering University of PAP), Ministry of Education, Xi’an, 710086, China

* Corresponding Author: Qiwu Wu. Email:
# These authors contributed equally to this work

(This article belongs to the Special Issue: Advances in Nature-Inspired and Metaheuristic Optimization Algorithms: Theory, Applications, and Emerging Trends)

Computers, Materials & Continua 2025, 85(2), 3621-3652. https://doi.org/10.32604/cmc.2025.068781

Received 06 June 2025; Accepted 28 July 2025; Issue published 23 September 2025

Abstract

This study introduces a novel algorithm known as the dung beetle optimization algorithm based on bounded reflection optimization and multi-strategy fusion (BFDBO), which is designed to tackle the complexities associated with multi-UAV collaborative trajectory planning in intricate battlefield environments. Initially, a collaborative planning cost function for the multi-UAV system is formulated, thereby converting the trajectory planning challenge into an optimization problem. Building on the foundational dung beetle optimization (DBO) algorithm, BFDBO incorporates three significant innovations: a boundary reflection mechanism, an adaptive mixed exploration strategy, and a dynamic multi-scale mutation strategy. These enhancements are intended to optimize the equilibrium between local exploration and global exploitation, facilitating the discovery of globally optimal trajectories that minimize the cost function. Numerical simulations utilizing the CEC2022 benchmark function indicate that all three enhancements of BFDBO positively influence its performance, resulting in accelerated convergence and improved optimization accuracy relative to leading optimization algorithms. In two battlefield scenarios of varying complexities, BFDBO achieved a minimum of a 39% reduction in total trajectory planning costs when compared to DBO and three other high-performance variants, while also demonstrating superior average runtime. This evidence underscores the effectiveness and applicability of BFDBO in practical, real-world contexts.

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Keywords

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