Inspired by cooperative hunting of lionesses,this paper presents a Lions Group Algorithm for cooperative hunting tasks involving multiple AUVs(autonomous underwater vehicles).The lions group algorithm is developed aro...Inspired by cooperative hunting of lionesses,this paper presents a Lions Group Algorithm for cooperative hunting tasks involving multiple AUVs(autonomous underwater vehicles).The lions group algorithm is developed around two core relationships:the dynamic game relationship between the hunters and the target and the cooperative relationship between hunters.In this paper,the lions group algorithm is divided into three stages.In each stage,the dynamic game model between the hunters and the target is constructed,and the cooperation model between the hunters is constructed.At the same time,in these three phases,a dynamic allocationmechanism for the roles and tasks of hunterswas established.The simulation experiment revealed the hunting effect.The results show that the path planning and obstacle avoidance strategy of the hunters,the target’s escape strategy,the complexity of the environment,and the speed relationship between the hunter and the target affect the hunting effect.展开更多
AIM: To figure out the contributed factors of the hospitalization expenses of senile cataract patients(HECP) and build up an area-specified senile cataract diagnosis related group(DRG) of Shanghai thereby formula...AIM: To figure out the contributed factors of the hospitalization expenses of senile cataract patients(HECP) and build up an area-specified senile cataract diagnosis related group(DRG) of Shanghai thereby formulating the reference range of HECP and providing scientific basis for the fair use and supervision of the health care insurance fund.METHODS: The data was collected from the first page of the medical records of 22 097 hospitalized patients from tertiary hospitals in Shanghai from 2010 to 2012 whose major diagnosis were senile cataract. Firstly, we analyzed the influence factors of HECP using univariate and multivariate analysis. DRG grouping was conducted according to the exhaustive Chi-squared automatic interaction detector(E-CHAID) model, using HECP as target variable. Finally we evaluated the grouping results using non-parametric test such as Kruskal-Wallis H test, RIV, CV, etc.RESULTS: The 6 DRGs were established as well as criterion of HECP, using age, sex, type of surgery and whether complications/comorbidities occurred as the key variables of classification node of senile cataract cases.CONCLUSION: The grouping of senile cataract cases based on E-CHAID algorithm is reasonable. And the criterion of HECP based on DRG can provide a feasible way of management in the fair use and supervision of medical insurance fund.展开更多
To overcome the default of single search tendency, the ants in the colony are divided into several sub-groups. The ants in different subgroups have different trail information and expectation coefficients. The simulat...To overcome the default of single search tendency, the ants in the colony are divided into several sub-groups. The ants in different subgroups have different trail information and expectation coefficients. The simulated annealing method is introduced to the algorithm. Through setting the temperature changing with the iterations, after each turn of tours, the solution set obtained by the ants is taken as the candidate set. The update set is obtained by adding the solutions in the candidate set to the previous update set with the probability determined by the temperature. The solutions in the candidate set are used to update the trail information. In each turn of updating, the current best solution is also used to enhance the trail information on the current best route. The trail information is reset when the algorithm is in stagnation state. The computer experiments demonstrate that the proposed algorithm has higher stability and convergence speed.展开更多
The artificial immune system,an excellent prototype for developingMachine Learning,is inspired by the function of the powerful natural immune system.As one of the prevalent classifiers,the Dendritic Cell Algorithm(DCA...The artificial immune system,an excellent prototype for developingMachine Learning,is inspired by the function of the powerful natural immune system.As one of the prevalent classifiers,the Dendritic Cell Algorithm(DCA)has been widely used to solve binary problems in the real world.The classification of DCA depends on a data preprocessing procedure to generate input signals,where feature selection and signal categorization are themain work.However,the results of these studies also show that the signal generation of DCA is relatively weak,and all of them utilized a filter strategy to remove unimportant attributes.Ignoring filtered features and applying expertise may not produce an optimal classification result.To overcome these limitations,this study models feature selection and signal categorization into feature grouping problems.This study hybridizes Grouping Genetic Algorithm(GGA)with DCA to propose a novel DCA version,GGA-DCA,for accomplishing feature selection and signal categorization in a search process.The GGA-DCA aims to search for the optimal feature grouping scheme without expertise automatically.In this study,the data coding and operators of GGA are redefined for grouping tasks.The experimental results show that the proposed algorithm has significant advantages over the compared DCA expansion algorithms in terms of signal generation.展开更多
随着客户定制化需求的增加以及对交货时间的关注,准时化生产成为提高企业竞争力的关键因素之一,面向准时化生产的车间调度值得深入研究。针对作业车间调度中拖期严重、准时化程度低等问题,提出了以最小拖期、最小提前期和最小化最大完...随着客户定制化需求的增加以及对交货时间的关注,准时化生产成为提高企业竞争力的关键因素之一,面向准时化生产的车间调度值得深入研究。针对作业车间调度中拖期严重、准时化程度低等问题,提出了以最小拖期、最小提前期和最小化最大完工时间为目标的车间调度模型;针对该模型的求解,基于冠状病毒群免疫优化(coronavirus herd immunity optimizer,CHIO)算法提出了一种自我学习的混合CHIO算法(hybrid CHIO algorithm based on self-learning,HCHIO)。首先,设计了一种具备得分评价机制的自我学习算子库,使得算法能够针对不同问题进行自我学习从而选择最优算子以提升算法的全局寻优性能;其次,通过对最优解进行邻域搜索,增强了算法的局部搜索能力;最后,在基准测试与实际案例上对HCHIO进行了实验,验证了该算法在解决车间调度问题上良好的寻优能力。实验结果证明了HCHIO在求解准时化作业车间调度问题上的有效性。展开更多
In this article, we have described the Todd-Coxeter algorithm. Indeed, the Todd-Coxeter algorithm is a mathematical tool used in the field of group theory. It makes it possible to determine different possible presenta...In this article, we have described the Todd-Coxeter algorithm. Indeed, the Todd-Coxeter algorithm is a mathematical tool used in the field of group theory. It makes it possible to determine different possible presentations of a group, i.e. different ways of expressing its elements and operations. We have also applied this algorithm to a subgroup generated H by G;where we obtained a table of the subgroup, three tables of relators including: Table of the relator aaaa;Table of the relator abab;Table of the relator bbb and a multiplication table aa'bb'. Once the algorithm is complete, the unit of H in G is 6. We have explicitly obtained a homomorphism of G in the group of permutations of H/G which is isomorphic to G6;where we have noticed that it is injective: in fact, an element of the nucleus belongs to the intersection of the xHx−1for x∈G, in particular, it belongs to H;on the other hand, the image of H in G6 is of order 4, so the nucleus is reduced to the neutral element.展开更多
边缘场景存在大量有依赖调用关系的容器与离线容器,需要对低延迟、实时响应的在线任务与批处理、可延迟的离线任务进行混合部署,这对容器调度提出了全新挑战。针对边缘计算场景中容器间依赖关系缺乏量化评估以及碎片化资源利用率低的问...边缘场景存在大量有依赖调用关系的容器与离线容器,需要对低延迟、实时响应的在线任务与批处理、可延迟的离线任务进行混合部署,这对容器调度提出了全新挑战。针对边缘计算场景中容器间依赖关系缺乏量化评估以及碎片化资源利用率低的问题,提出一种基于DQDG(Dependency Quantification and Grouping Algorithm)与DA-DDPG(Dependency-Aware Deep Deterministic Policy Gradient)的容器分组调度优化策略。首先,通过构建多维指标的容器依赖量化模型,实现对容器间依赖强度的精确度量。在此基础上,提出依赖驱动的容器分组算法(DQDG),将强依赖关系的容器聚合为组调度单位,有效减少跨节点通信开销。进一步设计依赖感知的深度确定性策略梯度算法(DA-DDPG),通过融入依赖强度信息改进经验回放机制和策略网络,实现容器组的长期优化调度。实验结果表明,该方法在保障在线服务质量的同时,显著提升集群负载均衡度和碎片资源利用率,为边缘场景下具有复杂依赖关系的异构任务部署提供了全链路优化方案。展开更多
摘要Inspired by cooperative hunting of lionesses,this paper presents a Lions Group Algorithm for cooperative hunting tasks involving multiple AUVs(autonomous underwater vehicles).The lions group algorithm is developed around two core relationships:the dynamic game relationship between the hunters and the target and the cooperative relationship between hunters.In this paper,the lions group algorithm is divided into three stages.In each stage,the dynamic game model between the hunters and the target is constructed,and the cooperation model between the hunters is constructed.At the same time,in these three phases,a dynamic allocationmechanism for the roles and tasks of hunterswas established.The simulation experiment revealed the hunting effect.The results show that the path planning and obstacle avoidance strategy of the hunters,the target’s escape strategy,the complexity of the environment,and the speed relationship between the hunter and the target affect the hunting effect.
基金Supported by the Key Research and Development Program of Hunan Province(No.2017SK2011)
摘要AIM: To figure out the contributed factors of the hospitalization expenses of senile cataract patients(HECP) and build up an area-specified senile cataract diagnosis related group(DRG) of Shanghai thereby formulating the reference range of HECP and providing scientific basis for the fair use and supervision of the health care insurance fund.METHODS: The data was collected from the first page of the medical records of 22 097 hospitalized patients from tertiary hospitals in Shanghai from 2010 to 2012 whose major diagnosis were senile cataract. Firstly, we analyzed the influence factors of HECP using univariate and multivariate analysis. DRG grouping was conducted according to the exhaustive Chi-squared automatic interaction detector(E-CHAID) model, using HECP as target variable. Finally we evaluated the grouping results using non-parametric test such as Kruskal-Wallis H test, RIV, CV, etc.RESULTS: The 6 DRGs were established as well as criterion of HECP, using age, sex, type of surgery and whether complications/comorbidities occurred as the key variables of classification node of senile cataract cases.CONCLUSION: The grouping of senile cataract cases based on E-CHAID algorithm is reasonable. And the criterion of HECP based on DRG can provide a feasible way of management in the fair use and supervision of medical insurance fund.
基金Project supported by the National Natural Science Foundation of China (Grant No.50608069)
摘要To overcome the default of single search tendency, the ants in the colony are divided into several sub-groups. The ants in different subgroups have different trail information and expectation coefficients. The simulated annealing method is introduced to the algorithm. Through setting the temperature changing with the iterations, after each turn of tours, the solution set obtained by the ants is taken as the candidate set. The update set is obtained by adding the solutions in the candidate set to the previous update set with the probability determined by the temperature. The solutions in the candidate set are used to update the trail information. In each turn of updating, the current best solution is also used to enhance the trail information on the current best route. The trail information is reset when the algorithm is in stagnation state. The computer experiments demonstrate that the proposed algorithm has higher stability and convergence speed.
基金NSFC http://gffzzf112c495998e46deh9fnkoqnw009p6f9k.ffgz.tsg.suse.edu.cn/for the support through Grants No.61877045Fundamental Research Project of Shenzhen Science and Technology Program for the support through Grants No.JCYJ2016042815-3956266.
摘要The artificial immune system,an excellent prototype for developingMachine Learning,is inspired by the function of the powerful natural immune system.As one of the prevalent classifiers,the Dendritic Cell Algorithm(DCA)has been widely used to solve binary problems in the real world.The classification of DCA depends on a data preprocessing procedure to generate input signals,where feature selection and signal categorization are themain work.However,the results of these studies also show that the signal generation of DCA is relatively weak,and all of them utilized a filter strategy to remove unimportant attributes.Ignoring filtered features and applying expertise may not produce an optimal classification result.To overcome these limitations,this study models feature selection and signal categorization into feature grouping problems.This study hybridizes Grouping Genetic Algorithm(GGA)with DCA to propose a novel DCA version,GGA-DCA,for accomplishing feature selection and signal categorization in a search process.The GGA-DCA aims to search for the optimal feature grouping scheme without expertise automatically.In this study,the data coding and operators of GGA are redefined for grouping tasks.The experimental results show that the proposed algorithm has significant advantages over the compared DCA expansion algorithms in terms of signal generation.
摘要随着客户定制化需求的增加以及对交货时间的关注,准时化生产成为提高企业竞争力的关键因素之一,面向准时化生产的车间调度值得深入研究。针对作业车间调度中拖期严重、准时化程度低等问题,提出了以最小拖期、最小提前期和最小化最大完工时间为目标的车间调度模型;针对该模型的求解,基于冠状病毒群免疫优化(coronavirus herd immunity optimizer,CHIO)算法提出了一种自我学习的混合CHIO算法(hybrid CHIO algorithm based on self-learning,HCHIO)。首先,设计了一种具备得分评价机制的自我学习算子库,使得算法能够针对不同问题进行自我学习从而选择最优算子以提升算法的全局寻优性能;其次,通过对最优解进行邻域搜索,增强了算法的局部搜索能力;最后,在基准测试与实际案例上对HCHIO进行了实验,验证了该算法在解决车间调度问题上良好的寻优能力。实验结果证明了HCHIO在求解准时化作业车间调度问题上的有效性。
摘要In this article, we have described the Todd-Coxeter algorithm. Indeed, the Todd-Coxeter algorithm is a mathematical tool used in the field of group theory. It makes it possible to determine different possible presentations of a group, i.e. different ways of expressing its elements and operations. We have also applied this algorithm to a subgroup generated H by G;where we obtained a table of the subgroup, three tables of relators including: Table of the relator aaaa;Table of the relator abab;Table of the relator bbb and a multiplication table aa'bb'. Once the algorithm is complete, the unit of H in G is 6. We have explicitly obtained a homomorphism of G in the group of permutations of H/G which is isomorphic to G6;where we have noticed that it is injective: in fact, an element of the nucleus belongs to the intersection of the xHx−1for x∈G, in particular, it belongs to H;on the other hand, the image of H in G6 is of order 4, so the nucleus is reduced to the neutral element.
摘要边缘场景存在大量有依赖调用关系的容器与离线容器,需要对低延迟、实时响应的在线任务与批处理、可延迟的离线任务进行混合部署,这对容器调度提出了全新挑战。针对边缘计算场景中容器间依赖关系缺乏量化评估以及碎片化资源利用率低的问题,提出一种基于DQDG(Dependency Quantification and Grouping Algorithm)与DA-DDPG(Dependency-Aware Deep Deterministic Policy Gradient)的容器分组调度优化策略。首先,通过构建多维指标的容器依赖量化模型,实现对容器间依赖强度的精确度量。在此基础上,提出依赖驱动的容器分组算法(DQDG),将强依赖关系的容器聚合为组调度单位,有效减少跨节点通信开销。进一步设计依赖感知的深度确定性策略梯度算法(DA-DDPG),通过融入依赖强度信息改进经验回放机制和策略网络,实现容器组的长期优化调度。实验结果表明,该方法在保障在线服务质量的同时,显著提升集群负载均衡度和碎片资源利用率,为边缘场景下具有复杂依赖关系的异构任务部署提供了全链路优化方案。