Multi-objective optimization of task allocation for multiple weeding robots based on grouping strategy
LI Long-xin
SANG Hong-yan
MENG Lei-lei
ZHANG Biao
Abstract：This paper addresses the multiple weeding robots task allocation(MWRTA)problem,aiming to minimize the maximum task completion time,total energy consumption,and the amount of residual pesticides,which are key perfor-mance indicators in sustainable agricultural systems.A mixed integer linear programming(MILP)formulation is proposed,and a novel grouping strategy-based multi-objective discrete artificial bee colony algorithm(GMO-DABC)is developed for solving the MWRTA problem efficiently.Firstly,heuristic methods integrating grouping strategy with load balancing is designed to effectively generate solutions.Secondly,neighborhood operators are designed based on the grouping strategy,dynamically adjusting neighborhood structures by knowledge-guided to reduce the risk of local optimum.Finally,a search strategy combining grouping strategy with non-dominated frontier analysis is proposed to efficiently explore the solution space.Extensive simulation experiments under multiple instance scales validate the superiority of GMO-DABC over several state-of-the-art algorithms in terms of solution quality,convergence speed and robustness,confirms its strong optimization capability and practical value for real-world agricultural applications.
Keywords：multi-robot task allocationartificial bee colony algorithmmulti-objective optimizationgrouping strategy
Publication Date：2025-11-30
Online Publishing Date：2025-12-29(First online date of this platform, not the publication date of the document)
Pages：10( 2322-2331 )