Multi-objective Evolutionary Algorithm (MOEA) is becoming a hot research area and quite a few aspects of MOEAs have been studied and discussed. However there are still few literatures discussing the roles of search an...Multi-objective Evolutionary Algorithm (MOEA) is becoming a hot research area and quite a few aspects of MOEAs have been studied and discussed. However there are still few literatures discussing the roles of search and selection operators in MOEAs. This paper studied their roles by solving a case of discrete Multi-objective Optimization Problem (MOP): Multi-objective TSP with a new MOEA. In the new MOEA, We adopt an efficient search operator, which has the properties of both crossover and mutation, to generate the new individuals and chose two selection operators: Family Competition and Population Competition with probabilities to realize selection. The simulation experiments showed that this new MOEA could get good uniform solutions representing the Pareto Front and outperformed SPEA in almost every simulation run on this problem. Furthermore, we analyzed its convergence property using finite Markov chain and proved that it could converge to Pareto Front with probability 1. We also find that the convergence property of MOEAs has much relationship with search and selection operators.展开更多
This paper proposes an improved optimal operation planning method for residential PEFC-CGS (Polymer Electrolyte Fuel CellCo-Generation System). Residential PEFC-CGS has recently been gathering attention as one of the ...This paper proposes an improved optimal operation planning method for residential PEFC-CGS (Polymer Electrolyte Fuel CellCo-Generation System). Residential PEFC-CGS has recently been gathering attention as one of the distributed power sources with high efficiency and low environmental impacts. Previous research pointed out that the output variations of PEFC adversely affect the durability. It can be surmised that smaller output variations will be desired to extend durability years. However, in this field, ramping rate have not been sufficiently considered. For local search and tabu search, ramping rate constraint makes our operation planning difficult because it restricts the search for feasible neighborhood solutions. Therefore, the authors proposed a method to deal with typical and harsher ramping rate constraints in comparison with conventional methods. There are two key points for the improvement. One is the reinforcement of the search along the output power axis;the other is to make use of the strategy of tabu search which avoids the local optimal solutions. The simulation results show the effectiveness of the proposed method in the daily operation planning. Furthermore, in the case using typical ramping rate parameter, it is confirmed that tabu search doesn’t contribute the reduction of daily operational cost due to the above stated restriction of the search area.展开更多
To improve the efficiency of operating rooms, reduce the hospital＇ s costs and improve the level of service qualities, a scheduling method is presented based on a filtered-beam-search-based algo- rithm. Firstly, a sc...To improve the efficiency of operating rooms, reduce the hospital＇ s costs and improve the level of service qualities, a scheduling method is presented based on a filtered-beam-search-based algo- rithm. Firstly, a scheduling problem domain is described. Mathematical programming models are al- so set up with an objective function of minimizing related costs of the system. On the basis of the de= scriptions mentioned above, a solving policy of generating feasible scheduling solutions is estab- lished. Combining with the speeific constraints of operation theatres, a filtered-beam-search-based algorithm is put forward to solve scheduling problems. Finally, simulation experiments are designed. The performance of the proposed algorithm is evaluated and compared with that of other approaches through simulations. Results indicate that the proposed algorithm can reduce costs, and are of prac- ticality and effectiveness.展开更多
In this paper, a study and evaluation of the combination of GPS/GNSS techniques and advanced image processing algorithms for distressed human detection, positioning and tracking, from a fully autonomous Unmanned Aeria...In this paper, a study and evaluation of the combination of GPS/GNSS techniques and advanced image processing algorithms for distressed human detection, positioning and tracking, from a fully autonomous Unmanned Aerial Vehicle (UAV)-based rescue support system, </span><span style="font-family:Verdana;">are</span><span style="font-family:Verdana;"> presented. In particular, the issue of human detection both on terrestrial and marine environment under several illumination and background conditions, as the human silhouette in water differs significantly from a terrestrial one</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;"> is addressed. A robust approach, including an adaptive distressed human detection algorithm running every N input image frames combined with a much faster tracking algorithm, is proposed. Real time or near-real-time distressed human detection rates achieved, using a single, low cost day/night NIR camera mounted onboard a fully autonomous UAV for Search and Rescue (SAR) operations. Moreover, the generation of our own dataset, for the image processing algorithms training is also presented. Details about both hardware and software configuration as well as the assessment of the proposed approach performance are fully discussed. Last, a comparison of the proposed approach to other human detection methods used in the literature is presented.展开更多
露天矿无人矿车在装卸载作业区内运输过程中的长时间停车等待是制约露天矿无人运输系统效率提升的瓶颈。为提高无人矿车的运输效率,本文结合作业区内的运输作业流程,提出一种基于动态可行驶距离的多车协同通行决策方法。首先,将决策模...露天矿无人矿车在装卸载作业区内运输过程中的长时间停车等待是制约露天矿无人运输系统效率提升的瓶颈。为提高无人矿车的运输效率,本文结合作业区内的运输作业流程,提出一种基于动态可行驶距离的多车协同通行决策方法。首先,将决策模型建模为混合整数线性规划(Mixed Integer Linear Programming, MILP)模型,表述优化目标和问题约束;其次,考虑到求解MILP模型存在难以满足动态决策实时性的问题,基于蒙特卡洛树搜索(Monte Carlo Tree Search,MCTS)实现多车冲突消解,核心思想是利用搜索树的推演能力进行多车通行前瞻模拟,计算多车的最优通行优先级,动态调整多车的可行驶距离;此外,根据无人矿车在作业区内的作业特征设计不同的MCTS节点价值函数,实现综合考虑运输效率与作业特征的通行优先级排序;最后,设计作业区4,8,12个停车位场景下的多车通行仿真实验,与基于先到先服务(First-Come-FirstServed, FCFS)的方法进行对比,吞吐量提升22.03%~28.00%,平均停车等待时间缩短31.71%~50.79%。同时,搭建微缩智能车辆的6停车位作业区场景实验平台,多车单次运输作业总用时相比FCFS缩短了18.84%。仿真与微缩智能车辆的实验结果表明,本文提出的方法能够提升露天矿作业区多车运输效率。展开更多
基金Supported by the National Natural Science Foundation of China(60133010,70071042,60073043)
文摘Multi-objective Evolutionary Algorithm (MOEA) is becoming a hot research area and quite a few aspects of MOEAs have been studied and discussed. However there are still few literatures discussing the roles of search and selection operators in MOEAs. This paper studied their roles by solving a case of discrete Multi-objective Optimization Problem (MOP): Multi-objective TSP with a new MOEA. In the new MOEA, We adopt an efficient search operator, which has the properties of both crossover and mutation, to generate the new individuals and chose two selection operators: Family Competition and Population Competition with probabilities to realize selection. The simulation experiments showed that this new MOEA could get good uniform solutions representing the Pareto Front and outperformed SPEA in almost every simulation run on this problem. Furthermore, we analyzed its convergence property using finite Markov chain and proved that it could converge to Pareto Front with probability 1. We also find that the convergence property of MOEAs has much relationship with search and selection operators.
文摘This paper proposes an improved optimal operation planning method for residential PEFC-CGS (Polymer Electrolyte Fuel CellCo-Generation System). Residential PEFC-CGS has recently been gathering attention as one of the distributed power sources with high efficiency and low environmental impacts. Previous research pointed out that the output variations of PEFC adversely affect the durability. It can be surmised that smaller output variations will be desired to extend durability years. However, in this field, ramping rate have not been sufficiently considered. For local search and tabu search, ramping rate constraint makes our operation planning difficult because it restricts the search for feasible neighborhood solutions. Therefore, the authors proposed a method to deal with typical and harsher ramping rate constraints in comparison with conventional methods. There are two key points for the improvement. One is the reinforcement of the search along the output power axis;the other is to make use of the strategy of tabu search which avoids the local optimal solutions. The simulation results show the effectiveness of the proposed method in the daily operation planning. Furthermore, in the case using typical ramping rate parameter, it is confirmed that tabu search doesn’t contribute the reduction of daily operational cost due to the above stated restriction of the search area.
基金Supported by the National Natural Science Foundation of China(No.61273035,71471135)
文摘To improve the efficiency of operating rooms, reduce the hospital＇ s costs and improve the level of service qualities, a scheduling method is presented based on a filtered-beam-search-based algo- rithm. Firstly, a scheduling problem domain is described. Mathematical programming models are al- so set up with an objective function of minimizing related costs of the system. On the basis of the de= scriptions mentioned above, a solving policy of generating feasible scheduling solutions is estab- lished. Combining with the speeific constraints of operation theatres, a filtered-beam-search-based algorithm is put forward to solve scheduling problems. Finally, simulation experiments are designed. The performance of the proposed algorithm is evaluated and compared with that of other approaches through simulations. Results indicate that the proposed algorithm can reduce costs, and are of prac- ticality and effectiveness.
文摘In this paper, a study and evaluation of the combination of GPS/GNSS techniques and advanced image processing algorithms for distressed human detection, positioning and tracking, from a fully autonomous Unmanned Aerial Vehicle (UAV)-based rescue support system, </span><span style="font-family:Verdana;">are</span><span style="font-family:Verdana;"> presented. In particular, the issue of human detection both on terrestrial and marine environment under several illumination and background conditions, as the human silhouette in water differs significantly from a terrestrial one</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;"> is addressed. A robust approach, including an adaptive distressed human detection algorithm running every N input image frames combined with a much faster tracking algorithm, is proposed. Real time or near-real-time distressed human detection rates achieved, using a single, low cost day/night NIR camera mounted onboard a fully autonomous UAV for Search and Rescue (SAR) operations. Moreover, the generation of our own dataset, for the image processing algorithms training is also presented. Details about both hardware and software configuration as well as the assessment of the proposed approach performance are fully discussed. Last, a comparison of the proposed approach to other human detection methods used in the literature is presented.
文摘露天矿无人矿车在装卸载作业区内运输过程中的长时间停车等待是制约露天矿无人运输系统效率提升的瓶颈。为提高无人矿车的运输效率,本文结合作业区内的运输作业流程,提出一种基于动态可行驶距离的多车协同通行决策方法。首先,将决策模型建模为混合整数线性规划(Mixed Integer Linear Programming, MILP)模型,表述优化目标和问题约束;其次,考虑到求解MILP模型存在难以满足动态决策实时性的问题,基于蒙特卡洛树搜索(Monte Carlo Tree Search,MCTS)实现多车冲突消解,核心思想是利用搜索树的推演能力进行多车通行前瞻模拟,计算多车的最优通行优先级,动态调整多车的可行驶距离;此外,根据无人矿车在作业区内的作业特征设计不同的MCTS节点价值函数,实现综合考虑运输效率与作业特征的通行优先级排序;最后,设计作业区4,8,12个停车位场景下的多车通行仿真实验,与基于先到先服务(First-Come-FirstServed, FCFS)的方法进行对比,吞吐量提升22.03%~28.00%,平均停车等待时间缩短31.71%~50.79%。同时,搭建微缩智能车辆的6停车位作业区场景实验平台,多车单次运输作业总用时相比FCFS缩短了18.84%。仿真与微缩智能车辆的实验结果表明,本文提出的方法能够提升露天矿作业区多车运输效率。
