带时间窗的多智能体任务分配改进遗传算法

Juan Li, Ning Fang
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引用次数: 2

摘要

任务分配是多智能体系统的一个重要组成部分。在多智能体系统中,将任务分配给其中一个智能体时,需要考虑许多约束条件以获得最优的分配结果。提出了一种改进的遗传算法(GA)来解决具有时间窗约束的多智能体任务分配问题。首先,建立了任务分配的数学模型,分析了时间窗的约束问题。采用罚函数法处理约束条件。其次,在局部搜索中加入改进的大邻域搜索(Large Neighborhood Search, LNS),增加种群的多样性,使算法更容易跳出局部最优;然后利用遗传算法解决了具有时间窗约束的多智能体任务分配问题。最后通过仿真验证了改进算法的优化性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved Genetic Algorithm for Multi-agent Task Allocation with Time Windows
Task allocation is a very important part of multi-agent systems. When assigning tasks to one of the agents in multi-agent systems, many constraints need to be considered to achieve optimal allocation results. In this paper, an improved genetic algorithm (GA) is proposed to solve the multi-agent task allocation with time window constraints. Firstly, the mathematical model of task allocation is established, and the constraint problem of time window is analyzed. The penalty function method is used to deal with the constraint condition. Secondly, the improved Large Neighborhood Search (LNS) is added to the local search to increase the diversity of population, which can make the algorithm easier to jump out of local optimum. Then genetic algorithm is used to solve the multi-agent task allocation problem with time window constraints. Finally, the simulation verifies the optimization performance of the improved algorithm.
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