Compared with single-domain unmanned swarms,cross-domain unmanned swarms continue to face new challenges in terms of platform performance and constraints.In this paper,a joint unmanned swarm target assignment and miss...Compared with single-domain unmanned swarms,cross-domain unmanned swarms continue to face new challenges in terms of platform performance and constraints.In this paper,a joint unmanned swarm target assignment and mission trajectory planning method is proposed to meet the requirements of cross-domain unmanned swarm mission planning.Firstly,the different performances of cross-domain heterogeneous platforms and mission requirements of targets are characterised by using a collection of operational resources.Secondly,an algorithmic framework for joint target assignment and mission trajectory planning is proposed,in which the initial planning of the trajectory is performed in the target assignment phase,while the trajectory is further optimised afterwards.Next,the estimation of the distribution algorithms is combined with the genetic algorithm to solve the objective function.Finally,the algorithm is numerically simulated by specific cases.Simulation results indicate that the proposed algorithm can perform effective task assignment and trajectory planning for cross-domain unmanned swarms.Furthermore,the solution performance of the hybrid estimation of distribution algorithm(EDA)-genetic algorithm(GA)algorithm is better than that of GA and EDA.展开更多
The multi-compartment electric vehicle routing problem(EVRP)with soft time window and multiple charging types(MCEVRP-STW&MCT)is studied,in which electric multi-compartment vehicles that are environmentally friendl...The multi-compartment electric vehicle routing problem(EVRP)with soft time window and multiple charging types(MCEVRP-STW&MCT)is studied,in which electric multi-compartment vehicles that are environmentally friendly but need to be recharged in course of transport process,are employed.A mathematical model for this optimization problem is established with the objective of minimizing the function composed of vehicle cost,distribution cost,time window penalty cost and charging service cost.To solve the problem,an estimation of the distribution algorithm based on Lévy flight(EDA-LF)is proposed to perform a local search at each iteration to prevent the algorithm from falling into local optimum.Experimental results demonstrate that the EDA-LF algorithm can find better solutions and has stronger robustness than the basic EDA algorithm.In addition,when comparing with existing algorithms,the result shows that the EDA-LF can often get better solutions in a relatively short time when solving medium and large-scale instances.Further experiments show that using electric multi-compartment vehicles to deliver incompatible products can produce better results than using traditional fuel vehicles.展开更多
针对混合流水车间调度问题(Hybrid flow-shop scheduling problem,HFSP)的特点,设计了基于排列的编码和解码方法,建立了描述问题解空间的概率模型,进而提出了一种有效的分布估计算法(Estimation of distribution algorithm,EDA).该算法...针对混合流水车间调度问题(Hybrid flow-shop scheduling problem,HFSP)的特点,设计了基于排列的编码和解码方法,建立了描述问题解空间的概率模型,进而提出了一种有效的分布估计算法(Estimation of distribution algorithm,EDA).该算法基于概率模型通过采样产生新个体,并基于优势种群更新概率模型的参数.同时,通过实验设计方法对算法参数设置进行了分析并确定了有效的参数组合.最后,通过基于实例的数值仿真以及与已有算法的比较验证了所提算法的有效性和鲁棒性.展开更多
构造了一种基于Alopex(Algorithm of pattern extraction)和分布估计算法(Estimation of distribution algorithm,EDA)相融合的进化算法EDA-Alopex。该算法将分布估计算法嵌入到一种基于Alopex的群智能进化算法(Alopex-based evolutiona...构造了一种基于Alopex(Algorithm of pattern extraction)和分布估计算法(Estimation of distribution algorithm,EDA)相融合的进化算法EDA-Alopex。该算法将分布估计算法嵌入到一种基于Alopex的群智能进化算法(Alopex-based evolutionary algorithm,AEA)中,利用分布估计算法收敛速度快及与传统进化算法进化模式不同的特点来改进AEA算法。新算法综合了AEA算法搜索得到的个体间相关性信息和EDA搜索过程中得到的全局概率信息,能够更好地指导种群向有利的区域进化。仿真结果表明:EDA改进的EDA-Alopex算法搜索性能与AEA算法的搜索性能相比有较大提高,特别是其收敛速度与AEA算法相比有明显提高。展开更多
文摘Compared with single-domain unmanned swarms,cross-domain unmanned swarms continue to face new challenges in terms of platform performance and constraints.In this paper,a joint unmanned swarm target assignment and mission trajectory planning method is proposed to meet the requirements of cross-domain unmanned swarm mission planning.Firstly,the different performances of cross-domain heterogeneous platforms and mission requirements of targets are characterised by using a collection of operational resources.Secondly,an algorithmic framework for joint target assignment and mission trajectory planning is proposed,in which the initial planning of the trajectory is performed in the target assignment phase,while the trajectory is further optimised afterwards.Next,the estimation of the distribution algorithms is combined with the genetic algorithm to solve the objective function.Finally,the algorithm is numerically simulated by specific cases.Simulation results indicate that the proposed algorithm can perform effective task assignment and trajectory planning for cross-domain unmanned swarms.Furthermore,the solution performance of the hybrid estimation of distribution algorithm(EDA)-genetic algorithm(GA)algorithm is better than that of GA and EDA.
基金supported by the National Natural Science Foundation of China(71571076)the National Key R&D Program for the 13th-Five-Year-Plan of China(2018YFF0300301).
文摘The multi-compartment electric vehicle routing problem(EVRP)with soft time window and multiple charging types(MCEVRP-STW&MCT)is studied,in which electric multi-compartment vehicles that are environmentally friendly but need to be recharged in course of transport process,are employed.A mathematical model for this optimization problem is established with the objective of minimizing the function composed of vehicle cost,distribution cost,time window penalty cost and charging service cost.To solve the problem,an estimation of the distribution algorithm based on Lévy flight(EDA-LF)is proposed to perform a local search at each iteration to prevent the algorithm from falling into local optimum.Experimental results demonstrate that the EDA-LF algorithm can find better solutions and has stronger robustness than the basic EDA algorithm.In addition,when comparing with existing algorithms,the result shows that the EDA-LF can often get better solutions in a relatively short time when solving medium and large-scale instances.Further experiments show that using electric multi-compartment vehicles to deliver incompatible products can produce better results than using traditional fuel vehicles.
文摘针对混合流水车间调度问题(Hybrid flow-shop scheduling problem,HFSP)的特点,设计了基于排列的编码和解码方法,建立了描述问题解空间的概率模型,进而提出了一种有效的分布估计算法(Estimation of distribution algorithm,EDA).该算法基于概率模型通过采样产生新个体,并基于优势种群更新概率模型的参数.同时,通过实验设计方法对算法参数设置进行了分析并确定了有效的参数组合.最后,通过基于实例的数值仿真以及与已有算法的比较验证了所提算法的有效性和鲁棒性.
文摘构造了一种基于Alopex(Algorithm of pattern extraction)和分布估计算法(Estimation of distribution algorithm,EDA)相融合的进化算法EDA-Alopex。该算法将分布估计算法嵌入到一种基于Alopex的群智能进化算法(Alopex-based evolutionary algorithm,AEA)中,利用分布估计算法收敛速度快及与传统进化算法进化模式不同的特点来改进AEA算法。新算法综合了AEA算法搜索得到的个体间相关性信息和EDA搜索过程中得到的全局概率信息,能够更好地指导种群向有利的区域进化。仿真结果表明:EDA改进的EDA-Alopex算法搜索性能与AEA算法的搜索性能相比有较大提高,特别是其收敛速度与AEA算法相比有明显提高。
