In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-base...In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.展开更多
In order to solve the non-linear and high-dimensional optimization problems more effectively, an improved self-adaptive membrane computing(ISMC) optimization algorithm was proposed. The proposed ISMC algorithm applied...In order to solve the non-linear and high-dimensional optimization problems more effectively, an improved self-adaptive membrane computing(ISMC) optimization algorithm was proposed. The proposed ISMC algorithm applied improved self-adaptive crossover and mutation formulae that can provide appropriate crossover operator and mutation operator based on different functions of the objects and the number of iterations. The performance of ISMC was tested by the benchmark functions. The simulation results for residue hydrogenating kinetics model parameter estimation show that the proposed method is superior to the traditional intelligent algorithms in terms of convergence accuracy and stability in solving the complex parameter optimization problems.展开更多
Optimization Algorithm was developed for the simula ti on of ceramic grain growth at atomistic scale. Based on the coordination informa tion of different atoms, a structure of trident tree was applied to save large q ...Optimization Algorithm was developed for the simula ti on of ceramic grain growth at atomistic scale. Based on the coordination informa tion of different atoms, a structure of trident tree was applied to save large q uantities data, so as to solve the problems of large data information and long r unning time. For every atom a binary tree was firstly formed according to the X coordination of atom. If the values of X coordination were the same, the middle sub-tree of first layer formed then a binary tree according to the Y coordinati on of atom. If the values of Y coordination were also the same, the middle sub- tree of second layer formed then a binary tree according to the Z coordination o f atom. In this way the speed of whole program is enhanced obviously. In order t o reduce memory, in this structure only need to store the exterior atoms’ infor mation, an integer is used to store the interior atoms’ information. If other a toms take up an atom’s all adjacent positions, this atom will be deleted in the data structure, for all the adjacent positions’ atoms, the integer’s relative bit will be set 1 to denote that there is an atom in this position but not be s tored in the trident tree. When an outside atom is deleted, for all the bits tha t are set 1,an atom will be added to the trident tree as an outside atom for the relative positions. And for this new added atom, the integer’s relative bi t of all the adjacent position’s atoms should be set 0 to denote that there is no interior atom in this position. In this way, if there are n 3 atoms, onl y need to store 6n 2 quantity’s atoms’ information. Large quantity of mem ory space can then be saved.展开更多
This paper discusses the algorithms for achieving global states and self-stabilizationfor communication protocols. It first describes a primary algorithm including its suitability forachieving global states and limita...This paper discusses the algorithms for achieving global states and self-stabilizationfor communication protocols. It first describes a primary algorithm including its suitability forachieving global states and limitation of self-stabilization for communication protocols, and thenpresents an improved algorithm that can be suitable to achieve global states and can be also usedto self-stabilizing communication protocols. Filially, it gives the proof of correctness and analysis ofcomplexity of the improved algorithm, and verifies its availability and efficiency by illustrating anexample protocol.展开更多
To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individua...To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individual has its own symbiotic individual, which consists of control parameters. Differential evolution operator is applied for the original individuals to search the global optimization solution. Alopex algorithm is used to co-evolve the symbiotic individuals during the original individual evolution and enhance the fitness of the original individuals. Thus, control parameters are self-adaptively adjusted by Alopex to obtain the real-time optimum values for the original population. To illustrate the whole performance of Alopex-DE, several varietal DEs were applied to optimize 13 benchmark functions. The results show that the whole performance of Alopex-DE is the best. Further, Alopex-DE was applied to solve 4 typical CPDOPs, and the effect of the discrete time degree on the optimization solution was analyzed. The satisfactory result is obtained.展开更多
Cloud data centers consume a multitude of power leading to the problem of high energy consumption. In order to solve this problem, an energy-efficient virtual machine(VM) consolidation algorithm named PVDE(prediction-...Cloud data centers consume a multitude of power leading to the problem of high energy consumption. In order to solve this problem, an energy-efficient virtual machine(VM) consolidation algorithm named PVDE(prediction-based VM deployment algorithm for energy efficiency) is presented. The proposed algorithm uses linear weighted method to predict the load of a host and classifies the hosts in the data center, based on the predicted host load, into four classes for the purpose of VMs migration. We also propose four types of VM selection algorithms for the purpose of determining potential VMs to be migrated. We performed extensive performance analysis of the proposed algorithms. Experimental results show that, in contrast to other energy-saving algorithms, the algorithm proposed in this work significantly reduces the energy consumption and maintains low service level agreement(SLA) violations.展开更多
The knapsack problem is a well-known combinatorial optimization problem which has been proved to be NP-hard. This paper proposes a new algorithm called quantum-inspired ant algorithm （QAA） to solve the knapsack prob...The knapsack problem is a well-known combinatorial optimization problem which has been proved to be NP-hard. This paper proposes a new algorithm called quantum-inspired ant algorithm （QAA） to solve the knapsack problem. QAA takes the advantage of the principles in quantum computing, such as qubit, quantum gate, and quantum superposition of states, to get more probabilistic-based status with small colonies. By updating the pheromone in the ant algorithm and rotating the quantum gate, the algorithm can finally reach the optimal solution. The detailed steps to use QAA are presented, and by solving series of test cases of classical knapsack problems, the effectiveness and generality of the new algorithm are validated.展开更多
To solve discrete optimization difficulty of the spectrum allocation problem,a membrane-inspired quantum shuffled frog leaping（MQSFL） algorithm is proposed.The proposed MQSFL algorithm applies the theory of membrane...To solve discrete optimization difficulty of the spectrum allocation problem,a membrane-inspired quantum shuffled frog leaping（MQSFL） algorithm is proposed.The proposed MQSFL algorithm applies the theory of membrane computing and quantum computing to the shuffled frog leaping algorithm,which is an effective discrete optimization algorithm.Then the proposed MQSFL algorithm is used to solve the spectrum allocation problem of cognitive radio systems.By hybridizing the quantum frog colony optimization and membrane computing,the quantum state and observation state of the quantum frogs can be well evolved within the membrane structure.The novel spectrum allocation algorithm can search the global optimal solution within a reasonable computation time.Simulation results for three utility functions of a cognitive radio system are provided to show that the MQSFL spectrum allocation method is superior to some previous spectrum allocation algorithms based on intelligence computing.展开更多
基金Shanxi Province Higher Education Science and Technology Innovation Fund Project(2022-676)Shanxi Soft Science Program Research Fund Project(2016041008-6)。
文摘In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.
基金Projects(61203020,61403190)supported by the National Natural Science Foundation of ChinaProject(BK20141461)supported by the Jiangsu Province Natural Science Foundation,China
文摘In order to solve the non-linear and high-dimensional optimization problems more effectively, an improved self-adaptive membrane computing(ISMC) optimization algorithm was proposed. The proposed ISMC algorithm applied improved self-adaptive crossover and mutation formulae that can provide appropriate crossover operator and mutation operator based on different functions of the objects and the number of iterations. The performance of ISMC was tested by the benchmark functions. The simulation results for residue hydrogenating kinetics model parameter estimation show that the proposed method is superior to the traditional intelligent algorithms in terms of convergence accuracy and stability in solving the complex parameter optimization problems.
文摘Optimization Algorithm was developed for the simula ti on of ceramic grain growth at atomistic scale. Based on the coordination informa tion of different atoms, a structure of trident tree was applied to save large q uantities data, so as to solve the problems of large data information and long r unning time. For every atom a binary tree was firstly formed according to the X coordination of atom. If the values of X coordination were the same, the middle sub-tree of first layer formed then a binary tree according to the Y coordinati on of atom. If the values of Y coordination were also the same, the middle sub- tree of second layer formed then a binary tree according to the Z coordination o f atom. In this way the speed of whole program is enhanced obviously. In order t o reduce memory, in this structure only need to store the exterior atoms’ infor mation, an integer is used to store the interior atoms’ information. If other a toms take up an atom’s all adjacent positions, this atom will be deleted in the data structure, for all the adjacent positions’ atoms, the integer’s relative bit will be set 1 to denote that there is an atom in this position but not be s tored in the trident tree. When an outside atom is deleted, for all the bits tha t are set 1,an atom will be added to the trident tree as an outside atom for the relative positions. And for this new added atom, the integer’s relative bi t of all the adjacent position’s atoms should be set 0 to denote that there is no interior atom in this position. In this way, if there are n 3 atoms, onl y need to store 6n 2 quantity’s atoms’ information. Large quantity of mem ory space can then be saved.
文摘This paper discusses the algorithms for achieving global states and self-stabilizationfor communication protocols. It first describes a primary algorithm including its suitability forachieving global states and limitation of self-stabilization for communication protocols, and thenpresents an improved algorithm that can be suitable to achieve global states and can be also usedto self-stabilizing communication protocols. Filially, it gives the proof of correctness and analysis ofcomplexity of the improved algorithm, and verifies its availability and efficiency by illustrating anexample protocol.
基金Project(2013CB733600) supported by the National Basic Research Program of ChinaProject(21176073) supported by the National Natural Science Foundation of China+2 种基金Project(20090074110005) supported by Doctoral Fund of Ministry of Education of ChinaProject(NCET-09-0346) supported by Program for New Century Excellent Talents in University of ChinaProject(09SG29) supported by "Shu Guang", China
文摘To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individual has its own symbiotic individual, which consists of control parameters. Differential evolution operator is applied for the original individuals to search the global optimization solution. Alopex algorithm is used to co-evolve the symbiotic individuals during the original individual evolution and enhance the fitness of the original individuals. Thus, control parameters are self-adaptively adjusted by Alopex to obtain the real-time optimum values for the original population. To illustrate the whole performance of Alopex-DE, several varietal DEs were applied to optimize 13 benchmark functions. The results show that the whole performance of Alopex-DE is the best. Further, Alopex-DE was applied to solve 4 typical CPDOPs, and the effect of the discrete time degree on the optimization solution was analyzed. The satisfactory result is obtained.
基金Projects(61572525,61272148)supported by the National Natural Science Foundation of ChinaProject(20120162110061)supported by the PhD Programs Foundation of Ministry of Education of China+1 种基金Project(CX2014B066)supported by the Hunan Provincial Innovation Foundation for Postgraduate,ChinaProject(2014zzts044)supported by the Fundamental Research Funds for the Central Universities,China
文摘Cloud data centers consume a multitude of power leading to the problem of high energy consumption. In order to solve this problem, an energy-efficient virtual machine(VM) consolidation algorithm named PVDE(prediction-based VM deployment algorithm for energy efficiency) is presented. The proposed algorithm uses linear weighted method to predict the load of a host and classifies the hosts in the data center, based on the predicted host load, into four classes for the purpose of VMs migration. We also propose four types of VM selection algorithms for the purpose of determining potential VMs to be migrated. We performed extensive performance analysis of the proposed algorithms. Experimental results show that, in contrast to other energy-saving algorithms, the algorithm proposed in this work significantly reduces the energy consumption and maintains low service level agreement(SLA) violations.
基金supported by the National Natural Science Foundation of China(70871081)the Shanghai Leading Academic Discipline Project(S30504).
文摘The knapsack problem is a well-known combinatorial optimization problem which has been proved to be NP-hard. This paper proposes a new algorithm called quantum-inspired ant algorithm （QAA） to solve the knapsack problem. QAA takes the advantage of the principles in quantum computing, such as qubit, quantum gate, and quantum superposition of states, to get more probabilistic-based status with small colonies. By updating the pheromone in the ant algorithm and rotating the quantum gate, the algorithm can finally reach the optimal solution. The detailed steps to use QAA are presented, and by solving series of test cases of classical knapsack problems, the effectiveness and generality of the new algorithm are validated.
基金supported by the National Natural Science Foundation of China (61102106,61102105)the Fundamental Research Funds for the Central Universities (HEUCF100801,HEUCFZ1129)
文摘To solve discrete optimization difficulty of the spectrum allocation problem,a membrane-inspired quantum shuffled frog leaping（MQSFL） algorithm is proposed.The proposed MQSFL algorithm applies the theory of membrane computing and quantum computing to the shuffled frog leaping algorithm,which is an effective discrete optimization algorithm.Then the proposed MQSFL algorithm is used to solve the spectrum allocation problem of cognitive radio systems.By hybridizing the quantum frog colony optimization and membrane computing,the quantum state and observation state of the quantum frogs can be well evolved within the membrane structure.The novel spectrum allocation algorithm can search the global optimal solution within a reasonable computation time.Simulation results for three utility functions of a cognitive radio system are provided to show that the MQSFL spectrum allocation method is superior to some previous spectrum allocation algorithms based on intelligence computing.
