用群智能算法求解障碍物中和问题

Ramazan Algin, A. F. Alkaya
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引用次数: 3

摘要

在本研究中,我们解决了障碍中和问题,其中智能体应该在一个映射的危险场中找到从给定点s到t的最短路径,该危险场中有N个潜在的地雷盘。在这个问题中,agent具有中和能力,但他/她只能中和有限数量的圆盘(K)。由于特定的原因,例如agent或车辆的负载能力,限制了中和数量。当一个磁盘被中和时,它的代价被加到路径的遍历长度上。该问题是一类具有源约束的最短问题,属于np困难问题。本研究将蚂蚁系统、蚁群系统和候鸟优化算法这三种重要的群体智能技术应用于解决障碍中和问题,并进行计算研究以揭示它们的性能。我们的实验表明,候鸟优化算法优于蚂蚁系统和蚁群系统,而蚁群系统优于蚂蚁系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Solving the obstacle neutralization problem using swarm intelligence algorithms
In this study, we tackle the obstacle neutralization problem wherein an agent is supposed to find the shortest path from given points s to t in a mapped hazard field where there are N potential mine discs in the field. In this problem agent has neutralization capability but he/she can neutralize only limited number of discs (K). The neutralization number is limited because of a specific reason such as the load capacity of agent or vehicle. When a disk is neutralized its cost is added to the traversal length of path. This problem is a kind of shortest problem with source constraints and it is NP-Hard. In this study, three important swarm intelligence techniques, namely ant system, ant colony system and migrating birds optimization algorithms, are applied to solve the obstacle neutralization problem and computational research is conducted in order to reveal their performance. Our experiments suggest that the migrating birds optimization algorithm outperforms ant system and ant colony system whereas ant colony system is better than ant system.
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