CAJ | 학술논문

由于侦察任务的复杂性和不确定性,无人机对其目标的侦察时间往往是不确定的.将多无人机对观测时间不确定目标的侦察路径规划问题建模为使任务时间、编队总耗时和编队规模同时最小化的多目标优化路径规划问题.对此,在基于ε-占优的稳态多目标进化算法基础上引入多目标局部搜索,给出了混合ε-占优多目标进化算法,提出了一种使用插入最近点方法的启发式遗传操作.实验结果表明,算法能够有效解决所研究的问题,并且其优势随着问题规模的增大而显著.
유우정찰임무적복잡성화불학정성,무인궤대기목표적정찰시간왕왕시불학정적.장다무인궤대관측시간불학정목표적정찰로경규화문제건모위사임무시간、편대총모시화편대규모동시최소화적다목표우화로경규화문제.대차,재기우ε-점우적은태다목표진화산법기출상인입다목표국부수색,급출료혼합ε-점우다목표진화산법,제출료일충사용삽입최근점방법적계발식유전조작.실험결과표명,산법능구유효해결소연구적문제,병차기우세수착문제규모적증대이현저.
The observation time on the target is usually uncertain due to the complexity and uncertainty of reconnaissance missions. The multiple unmanned aerial vehicles (UAVs) reconnaissance problem with a stochastic observation time (MURSOT) is modeled as a multi-objective optimal routing problem including minimizing mission duration, total time and fleet size. For solving this problem, a multi-objective local search is incorporated to a steady-state multi-objective evolutionary algorithm (MOEA) with ε-dominance conception (epsMOEA). Besides, several heuristic genetic operations using the insert-to-nearest method (INM) are proposed. Experimental results show that the proposed method is effective on MURSOT and its superiority is more remarkable with the growth of the size of missions.