The invention provides a cloud data center task scheduling method based on an improved ant colony algorithm, and relates to the field of cloud computing. The method comprises the following steps that (1) a to-be-scheduled workflow task set submitted by a user and a virtual machine set rent by the user are input, (2) a scheduling problem that tasks are allocated to virtual machines to be executed is represented as a standard minimum value solving problem, and (3) the virtual machine task scheduling problem in a cloud computing environment is solved through the ant colony algorithm based on information element updating. The method can adapt to the dynamic nature of the cloud environment, time for task scheduling of a user is shortened, and virtual machine load in a cloud data center can be maintained in a relative balance state.

Cloud data center task scheduling method based on improved ant colony algorithm

The invention discloses a method for optimizing a dynamic cloud workflow by using a genetic algorithm. The algorithm aims at minimizing periodic average expense of the cloud workflow in iteration execution, and limiting the total execution time that the workflow is executed for on period in a dynamic environment each time within a maximum execution deadline defined by a user. As the execution mode of the workflow is dynamic and variable in the cloud realization environment, the method carries out overall modeling on all flow topology results which may occur, then the characteristics of dynamic time-variation of the cloud workflow are comprehensively considered, the average performance of the execution of the cloud workflow in the dynamic environment is optimized by using the genetic algorithm, and the execution efficiency of the cloud workflow is improved.

Dynamic cloud workflow scheduling method based on genetic algorithm

The invention relates to a communication resource allocating method, which comprises the following steps: establishing an ant colony algorithm node diagram for each cell in a communication system, and defining and initializing pheromones and heuristic information of each node and each path; determining an evaluation function of the communication system based on a preset optimization objective of the communication system; in each iteration, respectively allocating communication resources in parallel to users in each cell; after communication resources in all the cells are allocated, calculating an evaluation result based on the evaluation function and carrying out global pheromone update; and if a termination condition is met, stopping the allocating process and carrying out practical allocation on the communication resources based on the obtained allocating scheme, otherwise carrying out the next iteration. In the embodiment of the invention, the ant colony optimization algorithm is used to allocate the communication resources to the users in each cell in the communication system, and higher-quality solutions for optimizing problems are quickly obtained by probabilistic search of the ant colony optimization algorithm so as to guide the practical allocating process of the communication resources.

Communication resource allocating method

The invention discloses a fractional-order self-adjusting control method for steam temperature of a coal-fired boiler, particularly relates to a self-adjusting control method based on a fractional-order model, and belongs to the field of automatic control. The fractional-order self-adjusting control method for the steam temperature of the coal-fired boiler comprises the steps that a steam temperature signal and an adjusting variable signal at the moment t are sampled with a sampling interval T and are recorded into a historical database, the performance index of a control system is calculated, whether parameters of a controller need to be updated is determined, a data sequence for modeling is obtained from the historical database, the fractional-order mathematical model of the controlled system is obtained through optimization by means of a mature swarm intelligence algorithm, the parameters of the controller are optimized by means of the mature swarm intelligence algorithm, and finally output of the controller is calculated and is applied to a controlled object. The fractional-order self-adjusting control method for the steam temperature of the coal-fired boiler is suitable for digital control of a coal-fired boiler steam temperature system and has the advantages that the steps are explicit, the concept is clear, the robustness is high, a specific disturbance test does not need to be carried out on the controlled object, a pseudorandom sequence does not need to be added into a control signal, and the steam temperature adjusting quality can be improved obviously.

Fractional-order self-adjusting control method for steam temperature of coal-fired boiler

Method for selecting decoupling condenser based on particle swarm algorithm

The invention discloses a method for solving dynamic grid workflow dispatch problem through utilizing ACO (ant colony optimization). The ACO carries out valuation to the performance of a dispatcher through tracing the topological structure of a workflow in a period. In the iterative search process of the ACO, the ants dispatch the workflow according to pheromone and the value of heuristic information. In the process of solution construction, the ants can change the pheromone in a chosen path, which is called local pheromone renewal. After the completion of all solution construction, the ACO carries out the renewal of all pheromone, thereby strengthening the pheromone on the optimized solution. In addition, the invention designs complete heuristic information used for balancing the cost and the time limit of the workflow. At last, the method provided by the invention is proved to be effective through the optimization to a nine-task and four-topology grid workflow.

Method for optimizing dynamic grid work flow by employing ant colony algorithm

The demand for automation design of power electronic circuits becomes higher and higher with the development of the power electronic technology. In the invention, a particle swarm algorithm is applied to the design and optimization of the power electronic circuits, and the invention mainly relates to the power electronics field and the intelligent computation field. In the optimization method, an optimization process is divided into two parts by a decoupling technology to respectively optimize the power transmission of the power electronic circuit and a feedback network. Meanwhile, a mutation operator is introduced into the particle swarm algorithm to increase diversity of the swarm and improve the optimization efficiency of the algorithm. The optimization design of a buck converter is taken as an example for testing, which proves that the optimization method is very effective.

Power electronic circuit optimization method based on particle swarm algorithm

The invention discloses a method for optimizing dynamic grid workflow by using particle swarm algorithm. The algorithm aims to minimize total expense of the workflow in a period, and to make execution time of the workflow in each unit time not exceed a Deadline defined by a user. Because the dynamic workflow has various different topological structures in the period, and adaptive values of a dispatching proposal are provided according to performance of the dynamic workflow in the period, wherein the adaptive values comprise a deadline part and an expense part. Moreover, a repair operation is added to the method based on the prior article swarm algorithm, so that the article swarm algorithm can effectively solve dispatching problem of the dynamic workflow.

Yuan Shi

Papers

Method for optimizing dynamic grid work flow by employing ant colony algorithm

Power electronic circuit optimization method based on particle swarm algorithm

Method for optimizing dynamic lattice work stream by employing particle cluster algorithm