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首页> 外文期刊>Journal of intelligent & fuzzy systems: Applications in Engineering and Technology >Uncertain multiobjective orienteering problem and its application to UAV reconnaissance mission planning
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Uncertain multiobjective orienteering problem and its application to UAV reconnaissance mission planning

机译：不确定的多目标导向事件及其在无人侦察任务规划中的应用

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摘要

Orienteering problem is gradually prevalent in the recent decade, but studies considering both uncertainty and multiobjective are still at low pace. In this paper, the uncertain multiobjective orienteering problem (UMOOP) is modeled based on uncertainty theory, in which objective functions contain uncertain vectors. Firstly, the Kataoka criterion and the 'worst-case-oriented' philosophy are adopted, and the UMOOP is transformed into a deterministic problem alpha-UMOOP with a series of chance constrains. On this basis, concept of efficient solution with belief degree is defined. Secondly, two assumptions are introduced to deal with the uncertain vectors, and a deterministic equivalent form D-UMOOP can be obtained. It is theoretically proved that efficient solutions to D-UMOOP are equivalent to the efficient solutions with belief degrees to the UMOOP. Additionally, since the D-UMOOP is NP-hard, a discrete multiobjective bat algorithm is designed with the discrete updating process and the multiobjective local search strategy. Finally, an application is presented to the unmanned aerial vehicle (UAV) reconnaissance mission planning problem, which is modeled as uncertain biobjective orienteering problems, and tackled by the theoretical result and algorithm in this paper. The studies provide a new way for multiple attribute and uncertain decision-making problems.
机译：定向的问题在近年来逐渐普遍，但考虑到不确定性和多目标的研究仍然处于低位。在本文中，基于不确定性理论建模的不确定的多目标定向问题（UMOOP），其中客观函数包含不确定的矢量。首先，采用了KAATAOKA标准和“最坏情况导向的”哲学，并将UMOOP转换为alpha-UMOOP，一系列机会约束。在此基础上，定义了具有信念度的有效解决方案的概念。其次，引入了两个假设来处理不确定的载体，并且可以获得确定的等效形式D-UMOOP。理论上证明了D-UMOOP的有效解决方案等同于带有信仰度到UMOOP的有效解决方案。另外，由于D-UMOOP是NP - 硬，因此设计了一种离散的多目标BAT算法，利用离散更新过程和多目标本地搜索策略设计。最后，申请呈现给无人驾驶飞行器（UAV）侦察任务规划问题，该计划问题被建模为不确定的生物回取导向事件问题，并通过本文的理论结果和算法来解决。研究为多个属性和不确定的决策问题提供了一种新的方式。

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