Multi-UAV Path Planning Based on Improved Fireworks Algorithm

doi:10.3969/j.issn.1006-2475.2025.06.008

Abstract

Abstract: Abstract: In this paper， the path planning of multiple UAVs in complex environment is studied， and the objective function satisfying the constraints is designed. At the same time， aiming at the shortcomings of fireworks algorithm in global convergence and local convergence， an improved fireworks algorithm is proposed. In the process of generating variation spark， Levy variation is used instead of Gaussian variation to generate variation spark， and the problem that the algorithm is easy to fall into local optimal at the origin is avoided. In addition， in order to improve the locality of the fireworks algorithm and the information exchange between individuals， a deep information exchange strategy is introduced to select the next generation of sparks. By comparing the improved fireworks algorithm with other intelligent optimization algorithms， the simulation results show that the improved fireworks algorithm has significant advantages in convergence speed and stability， and can better carry out multi-UAV path planning.

Key words: Key words: path planning, fireworks algorithm, deep information exchange strategy, Levy distribution

CLC Number:

O221

YANG Jin, CHEN Buqian. Multi-UAV Path Planning Based on Improved Fireworks Algorithm [J]. Computer and Modernization, 2025, 0(06): 51-55.

References

［1］ CHAI X Y， ZHENG Z S， XIAO J M， et al. Multi-strategy fusion differential evolution algorithm for UAV path planning in complex environment［J］. Aerospace Science and Technology， 2022， 121. DOI: 10.1016/J.AST2021.107287.
［2］郑涛，孔祥骏. 无人机系统的仿真建模研究［J］. 飞行力学， 2002（3）:25-28.
［3］王雅琳，杨依然，王彤，等. 2019年无人系统领域发展综述［J］. 无人系统技术， 2019，2（6）:53-57.
［4］魏岳江. 太阳能无人机及其军事应用［J］. 国防科技， 2012，33（6）:45-49.
［5］贾广芝. 基于遗传算法和稀疏A*算法的无人机三维航迹规划研究［D］. 南京:南京邮电大学， 2017.
［6］ WOEGINGER G J. Exact algorithms for NP-hard problems: A survey［C］// Combinatorial Optimization—Eureka， You Shrink! Papers Dedicated to Jack Edmonds 5th International Workshop Aussois， France. Springer， 2003:185-207.
［7］ EBERHART R， KENNEDY J. A new optimizer using particle swarm theory［C］// MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science， 1995:39-43.
［8］ PEHLIVANOGLU Y V. A new vibrational genetic algorithm enhanced with a Voronoi diagram for path planning of autonomous UAV［J］. Aerospace Science and Technology， 2012，16（1）:47-55.
［9］ WILHELM J， ROJAS J， EBERHART G， et al. Heterogeneous aerial platform adaptive mission planning using genetic algorithms［J］. Unmanned Systems， 2017，5（1）:19-30.
［10］吴虎胜，张凤鸣，战仁军，等. 求解0-1背包问题的二进制狼群算法［J］. 系统工程与电子技术， 2014，36（8）:1660-1667.
［11］ WANG G G， CHU H E， MIRJALILI S. Three-dimensional path planning for UCAV using an improved bat algorithm［J］. Aerospace Science and Technology， 2016，49:231-238.
［12］宋志强，夏庆锋，陈少博，等. 基于改进球面向量粒子群优化的UAV航迹规划［J］. 电光与控制， 2023，30（4）:56-60.
［13］ SHAO S K， PENG Y， HE C L， et al. Efficient path planning for UAV formation via comprehensively improved particle swarm optimization［J］. ISA Transactions， 2020，97:415-430.
［14］ TAN Y， ZHU Y C. Fireworks algorithm for optimization［C］// Advances in Swarm Intelligence: First International Conference. Springer， 2010:355-364.
［15］ QI Y Y， LIU J C， YU J Z. Dynamic modeling and hybrid fireworks algorithm-based path planning of an amphibious robot［J］. Guidance， Navigation and Control， 2022，2（1）. DOI: 10.1142/S2737480722500029.
［16］ LANA I， DEL SER J， VÉLEZ M. A novel fireworks algorithm with wind inertia dynamics and its application to traffic forecasting［C］// 2017 IEEE Congress on Evolutionary Computation. IEEE， 2017:706-713.
［17］ XUE J J， XIAO J J， ZHU J. Binary fireworks algorithm for 0-1 knapsack problem［C］// 2019 International Conference on Artificial Intelligence and Advanced Manufacturing， 2019:218-222.
［18］薛俊杰，王瑛，祝捷，等. 美国无人机分布式处理系统研究现状综述［J］. 飞航导弹， 2015（10）:30-34
［19］ CHEN B Q， YANG J Z， ZHANG H， et al. An improved spherical vector and truncated mean stabilization based bat algorithm for uav path planning［J］. IEEE Access， 2023，11:2396-2409.
［20］ YAO P， WANG H L， SU Z K. UAV feasible path planning based on disturbed fluid and trajectory propagation［J］. Chinese Journal of Aeronautics， 2015，28（4）:1163-1177.
［21］ SHAO S K，WANG Z F， TIAN B L， et al. Efficient path planning for UAV formation via comprehensively improved particle swarm optimization［J］. ISA Transactions， 2025，362（5）. DOI: 10.1016/j.jfranklin.2025.107573.
［22］余绍权. 广义高斯分布及其在稀疏表示中的应用研究［D］. 武汉：中国地质大学， 2016.
［23］马洁莹. 基于轮盘赌策略的混沌萤火虫算法研究［D］. 西安:西安电子科技大学， 2019.
［24］ MANTEGNA R N， STANLEY H E. Stochastic process with ultraslow convergence to a Gaussian: The truncated Lévy flight［J］. Physical Review Letters， 1994，73（22）. DOI: 10.1103/PhYsRevLett.74.4959.
［25］ MIRJALILI S， MIRJALILI S M， LEWIS A. Grey wolf optimizer［J］. Advances in Engineering Software， 2014，69:46-61.
［26］ MIRJALILI S， LEWIS A. The whale optimization algorithm［J］. Advances in Engineering Software， 2016，95:51-67.
［27］ QIN Y Q， SUN D B， LI N， et al. Path planning for mobile robot using the particle swarm optimization with mutation operator［C］// Proceedings of 2004 International Conference on Machine Learning and Cybernetics. IEEE， 2004:2473-2478.