用双层蚁群优化方法求解联盟结构生成问题

ChiaWei Yeh, T. Sugawara
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引用次数: 8

摘要

联盟结构生成问题是当前多智能体系统研究中的一个热点问题。当多个agent在同一环境中工作时,协作可能成为高效完成任务的关键。在传感器网络、多机器人系统甚至电子商务等许多领域也可以发现这个问题。然而,已经证明找到最优解是np完全的。针对面向任务的联盟结构问题,提出了一种称为双层蚁群优化的随机算法。然后,将其与CPLEX算法得到的最优解进行比较,评价解的质量。结果表明,该方法虽然不能保证最优解,但可以在较短的时间内找到一个足够好的联盟结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Solving Coalition Structure Generation Problem with Double-Layered Ant Colony Optimization
The coalition structure generation problem is now a big issue in the field of multi-agent systems. With many agents working in the same environment, cooperation may become a key point to complete a mission efficiently. This problem can also be found in many areas such as sensor networks, multi-robot systems, and even e-commerce. However, it has been proved to be NP-complete to find an optimal solution. In the paper, a stochastic algorithm called double-layered ant colony optimization is proposed to deal with the task-oriented coalition structure problem. Then, we evaluate the solution quality by comparing it with the optimal solutions derived by CPLEX. The results indicate that even though this method cannot guarantee the optimal solution, it can find a coalition structure that is good enough within a reasonably short time.
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