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Solving the Obstacle Neutralization Problem Using Swarm Intelligence Algorithms

机译：使用群智能算法解决障碍中和问题

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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.
机译：在这项研究中，我们解决了障碍物中和问题，其中试剂应该在映射的危险场中找到从给定点S到T的最短路径。在这个问题中，代理具有中和能力，但他/她只能中和数量有限数量的光盘（k）。由于特定原因（例如代理或车辆的负载能力），中和数量是有限的。当磁盘中和时，其成本被添加到遍历的路径长度。这个问题是源限制的一种最短问题，它是np-clyp。在这项研究中，应用了三种重要的群体智能技术，即蚂蚁系统，蚁群系统和迁移鸟类优化算法，用于解决障碍中和问题，并进行计算研究，以揭示其性能。我们的实验表明，迁移鸟类优化算法优于蚂蚁系统和蚁群系统，而蚁群系统比蚂蚁系统更好。

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