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To address the challenges of delayed scheduling information, heavy relianceon manual labour, and low operational efficiency in traditional large-scaleagricultural machinery operations, this study proposes a method formulti-agricultural machinery collaborative task assignment based on an improvedgenetic hybrid optimisation algorithm. The proposed method establishes amulti-agricultural machinery task allocation model by combining the pathpre-planning of a simulated annealing algorithm and the static task allocationof a genetic algorithm. By sequentially fusing these two algorithms, theirrespective shortcomings can be overcome, and their advantages in global andlocal search can be utilised. Consequently, the search capability of thepopulation is enhanced, leading to the discovery of more optimal solutions.Then, an adaptive crossover operator is constructed according to the taskassignment model, considering the capacity, path cost, and time of agriculturalmachinery; two-segment coding and multi-population adaptive mutation are usedto assign tasks to improve the diversity of the population and enhance theexploration ability of the population; and to improve the global optimisationability of the hybrid algorithm, a 2-Opt local optimisation operator and anCircle modification algorithm are introduced. Finally, simulation experimentswere conducted in MATLAB to evaluate the performance of the multi-agriculturalmachinery collaborative task assignment based on the improved genetic hybridalgorithm. The algorithm's capabilities were assessed through comparativeanalysis in the simulation trials. The results demonstrate that the developedhybrid algorithm can effectively reduce path costs, and the efficiency of theassignment outcomes surpasses that of the classical genetic algorithm. Thisapproach proves particularly suitable for addressing large-scale taskallocation problems.

Multi-agricultural Machinery Collaborative Task Assignment Based on Improved Genetic Hybrid Optimization Algorithm