Population Interactive Particle Swarm Optimization Algorithm for UAV Task Allocation in Uncertain Environments

RAO Ling-feng; GENG Na; ZHANG Yong; GUO Guang-song; XIAO Lei

doi:10.12263/DZXB.20250410

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Population Interactive Particle Swarm Optimization Algorithm for UAV Task Allocation in Uncertain Environments

PAPERS | 更新时间：2025-12-27

- Population Interactive Particle Swarm Optimization Algorithm for UAV Task Allocation in Uncertain Environments
- ACTA ELECTRONICA SINICA Vol. 53, Issue 8, Pages: 2678-2690(2025)
- 作者机构：
  
  1.中国矿业大学信息与控制工程学院，江苏徐州 221008
  2.江苏师范大学电气工程及自动化学院，江苏徐州 221116
  3.郑州航空工业管理学院自动化学院，河南郑州 450046
  4.中国轻工业联合会中国自行车协会，北京 100079
- 作者简介：
- 基金信息：
  
  Xuzhou Science and Technology Project of China(KC23008);National Natural Science Foundation of China(62103255);Science and Technology Research Project of Henan Province(242102211095);Science and Technology Project of Jiangsu Provincial Market Supervision Administration(KJ2025018)
- DOI：10.12263/DZXB.20250410
  CLC： TP312;
- Received：23 May 2025，
  
  Accepted：30 July 2025，
  
  Published：25 August 2025
- 稿件说明：
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饶凌风, 耿娜, 张勇, 等. 不确定环境下无人机任务分配的种群交互式粒子群算法[J]. 电子学报, 2025, 53(08): 2678-2690.

RAO Ling-feng, GENG Na, ZHANG Yong, et al. Population Interactive Particle Swarm Optimization Algorithm for UAV Task Allocation in Uncertain Environments[J]. Acta Electronica Sinica, 2025, 53(08): 2678-2690.
饶凌风, 耿娜, 张勇, 等. 不确定环境下无人机任务分配的种群交互式粒子群算法[J]. 电子学报, 2025, 53(08): 2678-2690. DOI：10.12263/DZXB.20250410

RAO Ling-feng, GENG Na, ZHANG Yong, et al. Population Interactive Particle Swarm Optimization Algorithm for UAV Task Allocation in Uncertain Environments[J]. Acta Electronica Sinica, 2025, 53(08): 2678-2690. DOI：10.12263/DZXB.20250410

摘要

针对灾后救援场景中多无人机（Unmanned Aerial Vehicle，UAV）任务分配所面临的任务信息的时空不确定性、复杂约束耦合及动态环境适应等挑战，本文提出了一种基于双种群协作的交互式改进粒子群算法.构建了双目标优化模型，将救援价值量化为时变区间函数，并结合航程约束引入惩罚机制，有效刻画了灾后环境的动态特性与多维度约束条件；在算法设计上，突破传统集中式框架的局限，构建了主-辅种群协同进化架构，采用问题导向的初始化策略生成高质量解集，确保算法初始解的优越性；进一步引入学习库驱动的双模态更新策略，结合种群交互的实例更新机制和局部精细化搜索，有效协调了多样性和算法收敛性.实验结果表明，在48架无人机的大规模场景下，所提算法相较于多种较新算法，在双重超体积评价指标上提升了11.8%~26.1%，且在多变体场景中表现出更强的鲁棒性和适应性.多种场景的测试结果验证了算法在求解效率、解集质量及环境适应性方面的综合优势，展现了显著的理论和应用价值.

Abstract

This paper proposes an improved particle swarm algorithm based on population interaction to tackle spatiotemporal uncertainty

complex constraint coupling

and dynamic adaptation in multi-UAV (Unmanned Aerial Vehicle) task allocation for post-disaster rescue. A bi-objective optimization model quantifies rescue value as a time-varying interval function

integrating a penalty mechanism with range constraints to capture the environment’s dynamic and multidimensional features. The proposed method departs from traditional centralized frameworks by introducing a main-auxiliary population co-evolution architecture. A problem-oriented initialization strategy ensures high-quality initial solutions. Additionally

a dual modal update strategy driven by a learning library

combined with population interaction and local refinement search

balances convergence speed and population diversity. Experiments with 48 UAVs demonstrate a 11.8%~26.1% improvement in the dual hypervolume metric compared to recent algorithms. Results confirms superior robustness

efficiency

and adaptability

underscoring its theoretical and practical significance.

关键词

Keywords

references

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