The multi-point dynamic aggregation(MPDA)problem is a challenging real-world problem.In the MPDA problem,the demands of tasks keep changing with their inherent incremental rates,while a heterogeneous robot fleet is re...The multi-point dynamic aggregation(MPDA)problem is a challenging real-world problem.In the MPDA problem,the demands of tasks keep changing with their inherent incremental rates,while a heterogeneous robot fleet is required to travel between these tasks to change the time-varying state of each task.The robots are allowed to collaborate on the same task or work separately until all tasks are completed.It is challenging to generate an effective task execution plan due to the tight coupling between robots abilities and tasks'incremental rates,and the complexity of robot collaboration.For effectiveness consideration,we use the variable length encoding to avoid redundancy in the solution space.We creatively use the adaptive large neighborhood search(ALNS)framework to solve the MPDA problem.In the proposed algorithm,high-quality initial solutions are generated through multiple problem-specific solution construction heuristics.These heuristics are also used to fix the broken solution in the novel integrated decoding-construction repair process of the ALNS framework.The results of statistical analysis by the Wilcoxon rank-sum test demonstrate that the proposed ALNS can obtain better task execution plans than some state-of-the-art algorithms in most MPDA instances.展开更多
There are defects such as the low convergence rate and premature phenomenon on the performance of simple genetic algorithms （SGA） as the values of crossover probability （Pc） and mutation probability （Pro） are fi...There are defects such as the low convergence rate and premature phenomenon on the performance of simple genetic algorithms （SGA） as the values of crossover probability （Pc） and mutation probability （Pro） are fixed. To solve the problems, the fuzzy control method and the genetic algorithms were systematically integrated to create a kind of improved fuzzy adaptive genetic algorithm （FAGA） based on the auto-regulating fuzzy rules （ARFR-FAGA）. By using the fuzzy control method, the values of Pc and Pm were adjusted according to the evolutional process, and the fuzzy rules were optimized by another genetic algorithm. Experimental results in solving the function optimization problems demonstrate that the convergence rate and solution quality of ARFR-FAGA exceed those of SGA, AGA and fuzzy adaptive genetic algorithm based on expertise （EFAGA） obviously in the global search.展开更多
Meiotic recombination is essential for sexual reproduction and its regulation has been extensively studied in many taxa.However,genome-wide recombination landscape has not been reported in ciliates and it remains unkn...Meiotic recombination is essential for sexual reproduction and its regulation has been extensively studied in many taxa.However,genome-wide recombination landscape has not been reported in ciliates and it remains unknown how it is affected by the unique features of ciliates:the synaptonemal complex(SC)-independent meiosis and the nuclear dimorphism.Here,we show the recombination landscape in the model ciliate Tetrahymena thermophila by analyzing single-nucleotide polymorphism datasets from 38 hybrid progeny.We detect 1021 crossover(CO)events(35.8 per meiosis),corresponding to an overall CO rate of 9.9 cM/Mb.However,gene conversion by non-crossover is rare(1.03 per meiosis)and not biased towards G or C alleles.Consistent with the reported roles of SC in CO interference,we find no obvious sign of CO interference.CO tends to occur within germ-soma common genomic regions and many of the 44 identified CO hotspots localize at the centromeric or subtelomeric regions.Gene ontology analyses show that CO hotspots are strongly associated with genes responding to environmental changes.We discuss these results with respect to how nuclear dimorphism has potentially driven the formation of the observed recombination landscape to facilitate environmental adaptation and the sharing of machinery among meiotic and somatic recombination.展开更多
Single unmanned aerial vehicle(UAV)multitasking plays an important role in multiple UAVs cooperative control,which is as well as the most complicated and hardest part.This paper establishes a threedimensional topograp...Single unmanned aerial vehicle(UAV)multitasking plays an important role in multiple UAVs cooperative control,which is as well as the most complicated and hardest part.This paper establishes a threedimensional topographical map,and an improved adaptive differential evolution(IADE)algorithm is proposed for single UAV multitasking.As an optimized problem,the efficiency of using standard differential evolution to obtain the global optimal solution is very low to avoid this problem.Therefore,the algorithm adopts the mutation factor and crossover factor into dynamic adaptive functions,which makes the crossover factor and variation factor can be adjusted with the number of population iteration and individual fitness value,letting the algorithm exploration and development more reasonable.The experimental results implicate that the IADE algorithm has better performance,higher convergence and efficiency to solve the multitasking problem compared with other algorithms.展开更多
To preserve the original signal as much as possible and filter random noises as many as possible in image processing,a threshold optimization-based adaptive template filtering algorithm was proposed.Unlike conventiona...To preserve the original signal as much as possible and filter random noises as many as possible in image processing,a threshold optimization-based adaptive template filtering algorithm was proposed.Unlike conventional filters whose template shapes and coefficients were fixed,multi-templates were defined and the right template for each pixel could be matched adaptively based on local image characteristics in the proposed method.The superiority of this method was verified by former results concerning the matching experiment of actual image with the comparison of conventional filtering methods.The adaptive search ability of immune genetic algorithm with the elitist selection and elitist crossover(IGAE) was used to optimize threshold t of the transformation function,and then combined with wavelet transformation to estimate noise variance.Multi-experiments were performed to test the validity of IGAE.The results show that the filtered result of t obtained by IGAE is superior to that of t obtained by other methods,IGAE has a faster convergence speed and a higher computational efficiency compared with the canonical genetic algorithm with the elitism and the immune algorithm with the information entropy and elitism by multi-experiments.展开更多
The convergence of genetic algorithm is mainly determined by its core operation crossover operation. When the objective function is a multiple hump function, traditional genetic algorithms are easily trapped into loca...The convergence of genetic algorithm is mainly determined by its core operation crossover operation. When the objective function is a multiple hump function, traditional genetic algorithms are easily trapped into local optimum, which is called premature conver- gence. In this paper, we propose a new genetic algorithm with improved arithmetic crossover operation based on gradient method. This crossover operation can generate offspring along quasi-gradient direction which is the Steepest descent direction of the value of objective function. The selection operator is also simplified, every individual in the population is given an opportunity to get evolution to avoid complicated selection algorithm. The adaptive mutation operator and the elitist strategy are also applied in this algorithm. The case 4 indicates this algorithm can faster converge to the global optimum and is more stable than the conventional genetic algorithms.展开更多
Bilevel linear programming, which consists of the objective functions of the upper level and lower level, is a useful tool for modeling decentralized decision problems. Various methods are proposed for solving this pr...Bilevel linear programming, which consists of the objective functions of the upper level and lower level, is a useful tool for modeling decentralized decision problems. Various methods are proposed for solving this problem. Of all the algorithms, the ge- netic algorithm is an alternative to conventional approaches to find the solution of the bilevel linear programming. In this paper, we describe an adaptive genetic algorithm for solving the bilevel linear programming problem to overcome the difficulty of determining the probabilities of crossover and mutation. In addition, some techniques are adopted not only to deal with the difficulty that most of the chromosomes maybe infeasible in solving constrained optimization problem with genetic algorithm but also to improve the efficiency of the algorithm. The performance of this proposed algorithm is illustrated by the examples from references.展开更多
基金supported in part by the National Outstanding Youth Talents Support Program(No.61822304)the Basic Science Center Program of the NSFC(No.62088101)+2 种基金the Project of Major International(Regional)Joint Research Program of NSFC(No.61720106011)the Shanghai Municipal Science and Technology Major Project(No.2021SHZDZX0100)the Shanghai Municipal Commission of Science and Technology Project(No.19511132101).
文摘The multi-point dynamic aggregation(MPDA)problem is a challenging real-world problem.In the MPDA problem,the demands of tasks keep changing with their inherent incremental rates,while a heterogeneous robot fleet is required to travel between these tasks to change the time-varying state of each task.The robots are allowed to collaborate on the same task or work separately until all tasks are completed.It is challenging to generate an effective task execution plan due to the tight coupling between robots abilities and tasks'incremental rates,and the complexity of robot collaboration.For effectiveness consideration,we use the variable length encoding to avoid redundancy in the solution space.We creatively use the adaptive large neighborhood search(ALNS)framework to solve the MPDA problem.In the proposed algorithm,high-quality initial solutions are generated through multiple problem-specific solution construction heuristics.These heuristics are also used to fix the broken solution in the novel integrated decoding-construction repair process of the ALNS framework.The results of statistical analysis by the Wilcoxon rank-sum test demonstrate that the proposed ALNS can obtain better task execution plans than some state-of-the-art algorithms in most MPDA instances.
基金Project(60574030) supported by the National Natural Science Foundation of ChinaKey Project(60634020) supported by the National Natural Science Foundation of China
文摘There are defects such as the low convergence rate and premature phenomenon on the performance of simple genetic algorithms （SGA） as the values of crossover probability （Pc） and mutation probability （Pro） are fixed. To solve the problems, the fuzzy control method and the genetic algorithms were systematically integrated to create a kind of improved fuzzy adaptive genetic algorithm （FAGA） based on the auto-regulating fuzzy rules （ARFR-FAGA）. By using the fuzzy control method, the values of Pc and Pm were adjusted according to the evolutional process, and the fuzzy rules were optimized by another genetic algorithm. Experimental results in solving the function optimization problems demonstrate that the convergence rate and solution quality of ARFR-FAGA exceed those of SGA, AGA and fuzzy adaptive genetic algorithm based on expertise （EFAGA） obviously in the global search.
基金supported by the Wuhan Branch,Supercomputing Center,Chinese Academy of Sciences,Chinasupported by the National Aquatic Biological Resource Center(NABRC)+4 种基金supported by the Bureau of Frontier Sciences and Education,Chinese Academy of Sciences(ZDBS-LY-SM026)the National Natural Science Foundation of China(32370457,32122015,32130011,31900316,and 31900339)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0480000)PJA3 grant of ARC Foundation(ARCPJA2021060003830)Equipes 2022 grant of Foundation Recherche Medicale(EQU202203014651).
文摘Meiotic recombination is essential for sexual reproduction and its regulation has been extensively studied in many taxa.However,genome-wide recombination landscape has not been reported in ciliates and it remains unknown how it is affected by the unique features of ciliates:the synaptonemal complex(SC)-independent meiosis and the nuclear dimorphism.Here,we show the recombination landscape in the model ciliate Tetrahymena thermophila by analyzing single-nucleotide polymorphism datasets from 38 hybrid progeny.We detect 1021 crossover(CO)events(35.8 per meiosis),corresponding to an overall CO rate of 9.9 cM/Mb.However,gene conversion by non-crossover is rare(1.03 per meiosis)and not biased towards G or C alleles.Consistent with the reported roles of SC in CO interference,we find no obvious sign of CO interference.CO tends to occur within germ-soma common genomic regions and many of the 44 identified CO hotspots localize at the centromeric or subtelomeric regions.Gene ontology analyses show that CO hotspots are strongly associated with genes responding to environmental changes.We discuss these results with respect to how nuclear dimorphism has potentially driven the formation of the observed recombination landscape to facilitate environmental adaptation and the sharing of machinery among meiotic and somatic recombination.
文摘Single unmanned aerial vehicle(UAV)multitasking plays an important role in multiple UAVs cooperative control,which is as well as the most complicated and hardest part.This paper establishes a threedimensional topographical map,and an improved adaptive differential evolution(IADE)algorithm is proposed for single UAV multitasking.As an optimized problem,the efficiency of using standard differential evolution to obtain the global optimal solution is very low to avoid this problem.Therefore,the algorithm adopts the mutation factor and crossover factor into dynamic adaptive functions,which makes the crossover factor and variation factor can be adjusted with the number of population iteration and individual fitness value,letting the algorithm exploration and development more reasonable.The experimental results implicate that the IADE algorithm has better performance,higher convergence and efficiency to solve the multitasking problem compared with other algorithms.
基金Project(20040533035) supported by the National Research Foundation for the Doctoral Program of Higher Education of ChinaProject (60874070) supported by the National Natural Science Foundation of China
文摘To preserve the original signal as much as possible and filter random noises as many as possible in image processing,a threshold optimization-based adaptive template filtering algorithm was proposed.Unlike conventional filters whose template shapes and coefficients were fixed,multi-templates were defined and the right template for each pixel could be matched adaptively based on local image characteristics in the proposed method.The superiority of this method was verified by former results concerning the matching experiment of actual image with the comparison of conventional filtering methods.The adaptive search ability of immune genetic algorithm with the elitist selection and elitist crossover(IGAE) was used to optimize threshold t of the transformation function,and then combined with wavelet transformation to estimate noise variance.Multi-experiments were performed to test the validity of IGAE.The results show that the filtered result of t obtained by IGAE is superior to that of t obtained by other methods,IGAE has a faster convergence speed and a higher computational efficiency compared with the canonical genetic algorithm with the elitism and the immune algorithm with the information entropy and elitism by multi-experiments.
文摘The convergence of genetic algorithm is mainly determined by its core operation crossover operation. When the objective function is a multiple hump function, traditional genetic algorithms are easily trapped into local optimum, which is called premature conver- gence. In this paper, we propose a new genetic algorithm with improved arithmetic crossover operation based on gradient method. This crossover operation can generate offspring along quasi-gradient direction which is the Steepest descent direction of the value of objective function. The selection operator is also simplified, every individual in the population is given an opportunity to get evolution to avoid complicated selection algorithm. The adaptive mutation operator and the elitist strategy are also applied in this algorithm. The case 4 indicates this algorithm can faster converge to the global optimum and is more stable than the conventional genetic algorithms.
基金the National Natural Science Foundation of China(Nos.60574071 and70771080)
文摘Bilevel linear programming, which consists of the objective functions of the upper level and lower level, is a useful tool for modeling decentralized decision problems. Various methods are proposed for solving this problem. Of all the algorithms, the ge- netic algorithm is an alternative to conventional approaches to find the solution of the bilevel linear programming. In this paper, we describe an adaptive genetic algorithm for solving the bilevel linear programming problem to overcome the difficulty of determining the probabilities of crossover and mutation. In addition, some techniques are adopted not only to deal with the difficulty that most of the chromosomes maybe infeasible in solving constrained optimization problem with genetic algorithm but also to improve the efficiency of the algorithm. The performance of this proposed algorithm is illustrated by the examples from references.
