Multi-agent collaborative task pre-allocation based on improved sand cat swarm optimization algorithm

基于改进沙猫算法的多机协同任务预分配

¹ School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China
² AVIC Xi'an Flight Automatic Control Research Institute, Xi'an 710076, China

Received: 21 May 2025

Abstract

To address the challenge of heterogeneous platform collaborative task pre-allocation, this study proposes an enhanced sand cat swarm optimization(SCSO) algorithm incorporating nonlinear convergence factors and an external archive update strategy. This improved algorithm integrates large neighborhood search(LNS) with dedicated destruction and repair operators. Two simulation cases validate the algorithm's efficacy: Case 1 compares its performance against genetic algorithm(GA), lemur algorithm, and whale optimization algorithm(WOA) on vehicle routing problems with time windows(VRPTW). Case 2 demonstrates its application in multi-heterogeneous agent reconnaissance/attack task allocation scenarios.

摘要

针对异构平台协同任务预分配问题, 在沙猫群算法基础上引入了非线性收敛因子和外部存储集更新策略, 并将其与大规模邻域搜索算法相结合, 设计了包含破坏算子和修复算子的改进沙猫算法。为验证该算法的性能, 设计了2个仿真案例。仿真案例1基于带时间窗的车辆路径问题, 与遗传算法、狐猴算法和鲸鱼算法进行对比。仿真案例2设计了一个多异构有人/无人机对地侦察/攻击任务分配场景, 进一步验证了算法的有效性。