The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper coo...The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper cooperative task allocation is superior to over the single UAV.Accordingly,several constraints should be satisfied to realize the efficient cooperation,such as special time-window,variant equipment,specified execution sequence.Hence,a proper task allocation in UAVs is the crucial point for the final success.The task allocation problem of the heterogeneous UAVs can be formulated as a multi-objective optimization problem coupled with the UAV dynamics.To this end,a multi-layer encoding strategy and a constraint scheduling method are designed to handle the critical logical and physical constraints.In addition,four optimization objectives:completion time,target reward,UAV damage,and total range,are introduced to evaluate various allocation plans.Subsequently,to efficiently solve the multi-objective optimization problem,an improved multi-objective quantum-behaved particle swarm optimization(IMOQPSO)algorithm is proposed.During this algorithm,a modified solution evaluation method is designed to guide algorithmic evolution;both the convergence and distribution of particles are considered comprehensively;and boundary solutions which may produce some special allocation plans are preserved.Moreover,adaptive parameter control and mixed update mechanism are also introduced in this algorithm.Finally,both the proposed model and algorithm are verified by simulation experiments.展开更多
为进一步降低北方乡村住宅采暖能耗、改善室内热舒适,依托Rhino-Grasshopper可视化编程平台,构建北方乡村住宅形体参数多目标优化设计框架.结合天津乡村实地调研数据,对北方乡村住宅规划和单体层面的9项形体参数进行了多目标优化,应用TO...为进一步降低北方乡村住宅采暖能耗、改善室内热舒适,依托Rhino-Grasshopper可视化编程平台,构建北方乡村住宅形体参数多目标优化设计框架.结合天津乡村实地调研数据,对北方乡村住宅规划和单体层面的9项形体参数进行了多目标优化,应用TOPSIS(technique for order preference by similarity to an ideal solution)综合评价法对帕累托解集进一步筛选,得到L型及U型乡村住宅形体参数最终设计方案.结果显示:优化后的L型乡村住宅采暖能耗及热舒适表现均优于U型;L型、U型乡村住宅形体优化方案在基准建筑基础上分别节约采暖能耗16.6%~18.0%,16.3%~26.4%,室内热舒适改善16.5%~17.5%,2.1%~19.0%.展开更多
基金Project(61801495)supported by the National Natural Science Foundation of China
文摘The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper cooperative task allocation is superior to over the single UAV.Accordingly,several constraints should be satisfied to realize the efficient cooperation,such as special time-window,variant equipment,specified execution sequence.Hence,a proper task allocation in UAVs is the crucial point for the final success.The task allocation problem of the heterogeneous UAVs can be formulated as a multi-objective optimization problem coupled with the UAV dynamics.To this end,a multi-layer encoding strategy and a constraint scheduling method are designed to handle the critical logical and physical constraints.In addition,four optimization objectives:completion time,target reward,UAV damage,and total range,are introduced to evaluate various allocation plans.Subsequently,to efficiently solve the multi-objective optimization problem,an improved multi-objective quantum-behaved particle swarm optimization(IMOQPSO)algorithm is proposed.During this algorithm,a modified solution evaluation method is designed to guide algorithmic evolution;both the convergence and distribution of particles are considered comprehensively;and boundary solutions which may produce some special allocation plans are preserved.Moreover,adaptive parameter control and mixed update mechanism are also introduced in this algorithm.Finally,both the proposed model and algorithm are verified by simulation experiments.
文摘为进一步降低北方乡村住宅采暖能耗、改善室内热舒适,依托Rhino-Grasshopper可视化编程平台,构建北方乡村住宅形体参数多目标优化设计框架.结合天津乡村实地调研数据,对北方乡村住宅规划和单体层面的9项形体参数进行了多目标优化,应用TOPSIS(technique for order preference by similarity to an ideal solution)综合评价法对帕累托解集进一步筛选,得到L型及U型乡村住宅形体参数最终设计方案.结果显示:优化后的L型乡村住宅采暖能耗及热舒适表现均优于U型;L型、U型乡村住宅形体优化方案在基准建筑基础上分别节约采暖能耗16.6%~18.0%,16.3%~26.4%,室内热舒适改善16.5%~17.5%,2.1%~19.0%.
