Currently,the top-rank-k has been widely applied to mine frequent patterns with a rank not exceeding k.In the existing algorithms,although a level-wise-search could fully mine the target patterns,it usually leads to t...Currently,the top-rank-k has been widely applied to mine frequent patterns with a rank not exceeding k.In the existing algorithms,although a level-wise-search could fully mine the target patterns,it usually leads to the delay of high rank patterns generation,resulting in the slow growth of the support threshold and the mining efficiency.Aiming at this problem,a greedy-strategy-based top-rank-k frequent patterns hybrid mining algorithm(GTK)is proposed in this paper.In this algorithm,top-rank-k patterns are stored in a static doubly linked list called RSL,and the patterns are divided into short patterns and long patterns.The short patterns generated by a rank-first-search always joins the two patterns of the highest rank in RSL that have not yet been joined.On the basis of the short patterns satisfying specific conditions,the long patterns are extracted through level-wise-search.To reduce redundancy,GTK improves the generation method of subsume index and designs the new pruning strategies of candidates.This algorithm also takes the use of reasonable pruning strategies to reduce the amount of computation to improve the computational speed.Real datasets and synthetic datasets are adopted in experiments to evaluate the proposed algorithm.The experimental results show the obvious advantages in both time efficiency and space efficiency of GTK.展开更多
Manned combat aerial vehicles （MCAVs）, and un-manned combat aerial vehicles （UCAVs） together form a cooper-ative engagement system to carry out operational mission, whichwill be a new air engagement style in the n...Manned combat aerial vehicles （MCAVs）, and un-manned combat aerial vehicles （UCAVs） together form a cooper-ative engagement system to carry out operational mission, whichwill be a new air engagement style in the near future. On the basisof analyzing the structure of the MCAV/UCAV cooperative engage-ment system, this paper divides the unique system into three hi-erarchical levels, respectively, i.e., mission level, task-cluster leveland task level. To solve the formation and adjustment problem ofthe latter two levels, three corresponding mathematical modelsare established. To solve these models, three algorithms calledquantum artificial bee colony （QABC） algorithm, greedy strategy（GS） and two-stage greedy strategy （TSGS） are proposed. Finally,a series of simulation experiments are designed to verify the effec-tiveness and superiority of the proposed algorithms.展开更多
Bioluminescence tomography(BLT)is a promising imaging modality that can provide noninvasive three-dimensional visualization information on tumor distribution.In BLT reconstruction,the widely used methods based on regu...Bioluminescence tomography(BLT)is a promising imaging modality that can provide noninvasive three-dimensional visualization information on tumor distribution.In BLT reconstruction,the widely used methods based on regularization or greedy strategy face problems such as over-sparsity,over-smoothing,spatial discontinuity,poor robustness,and poor multi-target resolution.To deal with these problems,combining the advantages of the greedy strategies as well as regularization methods,we propose a hybrid reconstruction framework for model-based multispectral BLT using the support set of a greedy strategy as a feasible region and the Alpha-divergence to combine the weighted solutions obtained by L1-norm and L2-norm regularization methods.In numerical simulations with digital mouse and in vivo experiments,the results show that the proposed framework has better localization accuracy,spatial resolution,and multi-target resolution.展开更多
The dung beetle optimizer(DBO)is a metaheuristic algorithm with fast convergence and powerful search capabilities,which has shown excellent performance in solving various optimization problems.However,it suffers from ...The dung beetle optimizer(DBO)is a metaheuristic algorithm with fast convergence and powerful search capabilities,which has shown excellent performance in solving various optimization problems.However,it suffers from the problems of easily falling into local optimal solutions and poor convergence accuracy when dealing with large-scale complex optimization problems.Therefore,we propose an adaptive DBO(ADBO)based on an elastic annealing mechanism to address these issues.First,the convergence factor is adjusted in a nonlinear decreasing manner to balance the requirements of global exploration and local exploitation,thus improving the convergence speed and search quality.Second,a greedy difference optimization strategy is introduced to increase population diversity,improve the global search capability,and avoid premature convergence.Finally,the elastic annealing mechanism is used to perturb the randomly selected individuals,helping the algorithm escape local optima and thereby improve solution quality and algorithm stability.The experimental results on the CEC 2017 and CEC 2022 benchmark function sets and MCNC benchmark circuits verify the effectiveness,superiority,and universality of ADBO.展开更多
Array partitioning is an important research problem in array management area,since the partitioning strategies have important influence on storage,query evaluation,and other components in array management systems.Mean...Array partitioning is an important research problem in array management area,since the partitioning strategies have important influence on storage,query evaluation,and other components in array management systems.Meanwhile,compression is highly needed for the array data due to its growing volume.Observing that the array partitioning can affect the compression performance significantly,this paper aims to design the efficient partitioning method for array data to optimize the compression performance.As far as we know,there still lacks research efforts on this problem.In this paper,the problem of array partitioning for optimizing the compression performance(PPCP for short)is firstly proposed.We adopt a popular compression technique which allows to process queries on the compressed data without decompression.Secondly,because the above problem is NP-hard,two essential principles for exploring the partitioning solution are introduced,which can explain the core idea of the partitioning algorithms proposed by us.The first principle shows that the compression performance can be improved if an array can be partitioned into two parts with different sparsities.The second principle introduces a greedy strategy which can well support the selection of the partitioning positions heuristically.Supported by the two principles,two greedy strategy based array partitioning algorithms are designed for the independent case and the dependent case respectively.Observing the expensive cost of the algorithm for the dependent case,a further optimization based on random sampling and dimension grouping is proposed to achieve linear time cost.Finally,the experiments are conducted on both synthetic and real-life data,and the results show that the two proposed partitioning algorithms achieve better performance on both compression and query evaluation.展开更多
Impact statement We have developed the GReedy Accumulated strategy for Protein Engineering(GRAPE)to improve enzyme stability across various applications,combining advanced computational methods with a unique clusterin...Impact statement We have developed the GReedy Accumulated strategy for Protein Engineering(GRAPE)to improve enzyme stability across various applications,combining advanced computational methods with a unique clustering and greedy accumulation approach to efficiently explore epistatic effects with minimal experimental effort.To make this strategy accessible to nonexperts,we introduced GRAPE‐WEB,an automated,user‐friendly web server that allows the design,inspection,and combination of stabilizing mutations without requiring extensive bioinformatics knowledge.展开更多
基金This research was supported in part by the Hunan Province’s Strategic and Emerging Industrial Projects under Grant 2018GK4035in part by the Hunan Province’s Changsha Zhuzhou Xiangtan National Independent Innovation Demonstration Zone projects under Grant 2017XK2058+1 种基金in part by the National Natural Science Foundation of China under Grant 61602171in part by the Scientific Research Fund of Hunan Provincial Education Department under Grant 17C0960 and 18B037.
文摘Currently,the top-rank-k has been widely applied to mine frequent patterns with a rank not exceeding k.In the existing algorithms,although a level-wise-search could fully mine the target patterns,it usually leads to the delay of high rank patterns generation,resulting in the slow growth of the support threshold and the mining efficiency.Aiming at this problem,a greedy-strategy-based top-rank-k frequent patterns hybrid mining algorithm(GTK)is proposed in this paper.In this algorithm,top-rank-k patterns are stored in a static doubly linked list called RSL,and the patterns are divided into short patterns and long patterns.The short patterns generated by a rank-first-search always joins the two patterns of the highest rank in RSL that have not yet been joined.On the basis of the short patterns satisfying specific conditions,the long patterns are extracted through level-wise-search.To reduce redundancy,GTK improves the generation method of subsume index and designs the new pruning strategies of candidates.This algorithm also takes the use of reasonable pruning strategies to reduce the amount of computation to improve the computational speed.Real datasets and synthetic datasets are adopted in experiments to evaluate the proposed algorithm.The experimental results show the obvious advantages in both time efficiency and space efficiency of GTK.
基金supported by the National Natural Science Foundation of China(61573017)the Doctoral Innovation Found of Air Force Engineering University(KGD08101604)
文摘Manned combat aerial vehicles （MCAVs）, and un-manned combat aerial vehicles （UCAVs） together form a cooper-ative engagement system to carry out operational mission, whichwill be a new air engagement style in the near future. On the basisof analyzing the structure of the MCAV/UCAV cooperative engage-ment system, this paper divides the unique system into three hi-erarchical levels, respectively, i.e., mission level, task-cluster leveland task level. To solve the formation and adjustment problem ofthe latter two levels, three corresponding mathematical modelsare established. To solve these models, three algorithms calledquantum artificial bee colony （QABC） algorithm, greedy strategy（GS） and two-stage greedy strategy （TSGS） are proposed. Finally,a series of simulation experiments are designed to verify the effec-tiveness and superiority of the proposed algorithms.
基金funded by the National Natural Science Foundation of China under Grants Nos.11871321,61901374,61906154,and 61971350Postdoctoral Innovative Talents Support Program under Grants No.BX20180254.
文摘Bioluminescence tomography(BLT)is a promising imaging modality that can provide noninvasive three-dimensional visualization information on tumor distribution.In BLT reconstruction,the widely used methods based on regularization or greedy strategy face problems such as over-sparsity,over-smoothing,spatial discontinuity,poor robustness,and poor multi-target resolution.To deal with these problems,combining the advantages of the greedy strategies as well as regularization methods,we propose a hybrid reconstruction framework for model-based multispectral BLT using the support set of a greedy strategy as a feasible region and the Alpha-divergence to combine the weighted solutions obtained by L1-norm and L2-norm regularization methods.In numerical simulations with digital mouse and in vivo experiments,the results show that the proposed framework has better localization accuracy,spatial resolution,and multi-target resolution.
基金Project supported by the National Natural Science Foundation of China(No.62102130)the Central Government Guides Local Science and Technology Development Fund Project of China(No.226Z0201G)+4 种基金the Natural Science Foundation of Hebei Province of China(Nos.F2020204003 and F2024204001)the Hebei Youth Talents Support Project of China(No.BJ2019008)the Science and Technology Research Projects of Higher Education Institutions in Hebei Province of China(No.QN2024138)the Basic Scientific Research Funds Research Project of Hebei Provincial Colleges and Universities of China(No.KY2022073)the Hebei Province Higher Education Institution Scientific Research Project of China(No.QN2025192)。
文摘The dung beetle optimizer(DBO)is a metaheuristic algorithm with fast convergence and powerful search capabilities,which has shown excellent performance in solving various optimization problems.However,it suffers from the problems of easily falling into local optimal solutions and poor convergence accuracy when dealing with large-scale complex optimization problems.Therefore,we propose an adaptive DBO(ADBO)based on an elastic annealing mechanism to address these issues.First,the convergence factor is adjusted in a nonlinear decreasing manner to balance the requirements of global exploration and local exploitation,thus improving the convergence speed and search quality.Second,a greedy difference optimization strategy is introduced to increase population diversity,improve the global search capability,and avoid premature convergence.Finally,the elastic annealing mechanism is used to perturb the randomly selected individuals,helping the algorithm escape local optima and thereby improve solution quality and algorithm stability.The experimental results on the CEC 2017 and CEC 2022 benchmark function sets and MCNC benchmark circuits verify the effectiveness,superiority,and universality of ADBO.
基金This work was supported by the National Natural Science Foundation of China under Grant Nos.61832003 and U1811461.
文摘Array partitioning is an important research problem in array management area,since the partitioning strategies have important influence on storage,query evaluation,and other components in array management systems.Meanwhile,compression is highly needed for the array data due to its growing volume.Observing that the array partitioning can affect the compression performance significantly,this paper aims to design the efficient partitioning method for array data to optimize the compression performance.As far as we know,there still lacks research efforts on this problem.In this paper,the problem of array partitioning for optimizing the compression performance(PPCP for short)is firstly proposed.We adopt a popular compression technique which allows to process queries on the compressed data without decompression.Secondly,because the above problem is NP-hard,two essential principles for exploring the partitioning solution are introduced,which can explain the core idea of the partitioning algorithms proposed by us.The first principle shows that the compression performance can be improved if an array can be partitioned into two parts with different sparsities.The second principle introduces a greedy strategy which can well support the selection of the partitioning positions heuristically.Supported by the two principles,two greedy strategy based array partitioning algorithms are designed for the independent case and the dependent case respectively.Observing the expensive cost of the algorithm for the dependent case,a further optimization based on random sampling and dimension grouping is proposed to achieve linear time cost.Finally,the experiments are conducted on both synthetic and real-life data,and the results show that the two proposed partitioning algorithms achieve better performance on both compression and query evaluation.
基金supported by the National Key R&D Program of China(grant no.2021YFC2103600)the National Natural Science Foundation of China(31822002,32170033,and 32422001)+2 种基金the Key Research Program of Frontier Sciences(ZDBS‐LY‐SM014)the Biological Resources Program(KFJBRP‐009 and KFJ‐BRP‐017‐58)from the Chinese Academy of Sciences,the Informatization Plan of Chinese Academy of Sciences(CAS‐WX2021SF‐0111)the Youth Innovation Promotion Association CAS(2022086).
文摘Impact statement We have developed the GReedy Accumulated strategy for Protein Engineering(GRAPE)to improve enzyme stability across various applications,combining advanced computational methods with a unique clustering and greedy accumulation approach to efficiently explore epistatic effects with minimal experimental effort.To make this strategy accessible to nonexperts,we introduced GRAPE‐WEB,an automated,user‐friendly web server that allows the design,inspection,and combination of stabilizing mutations without requiring extensive bioinformatics knowledge.
