Let G be a graph of order n, and let a and b be integers, such that 1 ≤ a b. Let H be a subgraph of G with m(≤b) edges, and δ(G) be the minimum degree. We prove that G has a [a,b]-factor containing all edges of H i...Let G be a graph of order n, and let a and b be integers, such that 1 ≤ a b. Let H be a subgraph of G with m(≤b) edges, and δ(G) be the minimum degree. We prove that G has a [a,b]-factor containing all edges of H if , , and when a ≤ 2, .展开更多
Artificial bee colony(ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencie...Artificial bee colony(ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencies in ABC regarding its local search ability and global search efficiency. Aiming at these deficiencies,an ABC variant named hybrid ABC(HABC) algorithm is proposed.Firstly, the variable neighborhood search factor is added to the solution search equation, which can enhance the local search ability and increase the population diversity. Secondly, inspired by the neuroscience investigation of real honeybees, the memory mechanism is put forward, which assumes the artificial bees can remember their past successful experiences and further guide the subsequent foraging behavior. The proposed memory mechanism is used to improve the global search efficiency. Finally, the results of comparison on a set of ten benchmark functions demonstrate the superiority of HABC.展开更多
Aiming at the intervention decision-making problem in manned/unmanned aerial vehicle（MAV/UAV） cooperative engagement, this paper carries out a research on allocation strategy of emergency discretion based on human f...Aiming at the intervention decision-making problem in manned/unmanned aerial vehicle（MAV/UAV） cooperative engagement, this paper carries out a research on allocation strategy of emergency discretion based on human factors engineering（HFE）.Firstly, based on the brief review of research status of HFE, it gives structural description to emergency in the process of cooperative engagement and analyzes intervention of commanders. After that,constraint conditions of intervention decision-making of commanders based on HFE（IDMCBHFE） are given, and the mathematical model, which takes the overall efficiency value of handling emergencies as the objective function, is established. Then, through combining K-best and variable neighborhood search（VNS） algorithm, a K-best optimization variable neighborhood search mixed algorithm（KBOVNSMA） is designed to solve the model. Finally,through three groups of simulation experiments, effectiveness and superiority of the proposed algorithm are verified.展开更多
近年来,混合型数据的聚类问题受到广泛关注。作为处理混合型数据的一种有效方法,K-prototype聚类算法在初始化聚类中心时通常采用随机选取的策略,然而这种策略在很多实际应用中难以保证聚类结果的质量。针对上述问题,采用基于离群点检...近年来,混合型数据的聚类问题受到广泛关注。作为处理混合型数据的一种有效方法,K-prototype聚类算法在初始化聚类中心时通常采用随机选取的策略,然而这种策略在很多实际应用中难以保证聚类结果的质量。针对上述问题,采用基于离群点检测的策略来为K-prototype算法选择初始中心,并提出一种新的混合型数据聚类初始化算法(initialization of K-prototype clustering based on outlier detection and density,IKP-ODD)。给定一个候选对象,IKP-ODD通过计算其距离离群因子、加权密度以及与已有初始中心之间的加权距离来判断候选对象是否是一个初始中心。IKP-ODD通过采用距离离群因子和加权密度,防止选择离群点作为初始中心。在计算对象的加权密度以及对象之间的加权距离时,采用邻域粗糙集中的粒度邻域熵来计算每一个属性的重要性,并根据属性重要性的大小为不同属性赋予不同的权重,有效地反映不同属性之间的差异性。在多个UCI数据集上的实验表明,相对于现有的初始化方法,IKP-ODD能够更好地解决K-prototype聚类的初始化问题。展开更多
文摘Let G be a graph of order n, and let a and b be integers, such that 1 ≤ a b. Let H be a subgraph of G with m(≤b) edges, and δ(G) be the minimum degree. We prove that G has a [a,b]-factor containing all edges of H if , , and when a ≤ 2, .
基金supported by the National Natural Science Foundation of China(7177121671701209)
文摘Artificial bee colony(ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencies in ABC regarding its local search ability and global search efficiency. Aiming at these deficiencies,an ABC variant named hybrid ABC(HABC) algorithm is proposed.Firstly, the variable neighborhood search factor is added to the solution search equation, which can enhance the local search ability and increase the population diversity. Secondly, inspired by the neuroscience investigation of real honeybees, the memory mechanism is put forward, which assumes the artificial bees can remember their past successful experiences and further guide the subsequent foraging behavior. The proposed memory mechanism is used to improve the global search efficiency. Finally, the results of comparison on a set of ten benchmark functions demonstrate the superiority of HABC.
基金supported by the National Natural Science Foundation of China(61573017)the Doctoral Foundation of Air Force Engineering University(KGD08101604)
文摘Aiming at the intervention decision-making problem in manned/unmanned aerial vehicle（MAV/UAV） cooperative engagement, this paper carries out a research on allocation strategy of emergency discretion based on human factors engineering（HFE）.Firstly, based on the brief review of research status of HFE, it gives structural description to emergency in the process of cooperative engagement and analyzes intervention of commanders. After that,constraint conditions of intervention decision-making of commanders based on HFE（IDMCBHFE） are given, and the mathematical model, which takes the overall efficiency value of handling emergencies as the objective function, is established. Then, through combining K-best and variable neighborhood search（VNS） algorithm, a K-best optimization variable neighborhood search mixed algorithm（KBOVNSMA） is designed to solve the model. Finally,through three groups of simulation experiments, effectiveness and superiority of the proposed algorithm are verified.
文摘近年来,混合型数据的聚类问题受到广泛关注。作为处理混合型数据的一种有效方法,K-prototype聚类算法在初始化聚类中心时通常采用随机选取的策略,然而这种策略在很多实际应用中难以保证聚类结果的质量。针对上述问题,采用基于离群点检测的策略来为K-prototype算法选择初始中心,并提出一种新的混合型数据聚类初始化算法(initialization of K-prototype clustering based on outlier detection and density,IKP-ODD)。给定一个候选对象,IKP-ODD通过计算其距离离群因子、加权密度以及与已有初始中心之间的加权距离来判断候选对象是否是一个初始中心。IKP-ODD通过采用距离离群因子和加权密度,防止选择离群点作为初始中心。在计算对象的加权密度以及对象之间的加权距离时,采用邻域粗糙集中的粒度邻域熵来计算每一个属性的重要性,并根据属性重要性的大小为不同属性赋予不同的权重,有效地反映不同属性之间的差异性。在多个UCI数据集上的实验表明,相对于现有的初始化方法,IKP-ODD能够更好地解决K-prototype聚类的初始化问题。
