The invention provides a shuffled frog-leaping based division method of software and hardware. The method comprises the steps of determining the maximum iterations of the algorithm at the initial; calculating the area and time cost of the hardware in the corresponding software and hardware division scheme corresponding to each frog; descending and sequencing the whole frog group according to the adaptive fitness of individual fog; updating the fog groups in each group at a plurality of times to enable the adaptive fitness of the worst frog in the group to be prior to that of the previous frog as well as meeting the system limitation condition; updating the optimal frog in the group by the random updating method in case of failing to update; shuffling the whole frog groups; finding the global optimal solution as the optimal software and hardware division scheme after the iteration is done. According to the method, the sub-task of one embedded composite system is divided through a processor, and meanwhile, the searching and the optimizing capacity of the system are improved; the optimal software and hardware division scheme can be found, and as a result, the task performing efficiency of the system can be increased.

Shuffled frog-leaping based division method of software and hardware

The invention provides an efficient dynamic division method of software and hardware, and aims at designing an embedded system. The method comprises the steps of 1) building an accurate mathematic model for software and hardware division problem; 2) simplifying the accurate mathematic model into a low-dimensional simple model according to the relevance of coefficients of a coefficient matrix in a limitation condition; 3) solving the simplified model obtained in step 2) through the automatic division algorithm to obtain the optimal software and hardware division scheme, recording the algorithm solving operation time, and observing the simplified mode solving efficiency of the automatic division algorithm; 4) verifying the feasibility of the software and hardware division scheme; 5) outputting the optimal software and hardware division scheme. With the adoption of the method, the complexity of the model can be reduced; the model solving time of the automatic division algorithm can be greatly reduced; the division algorithm solving efficiency can be increased; the large scale software and hardware can be particularly quickly divided; therefore, large scale complex software and hardware can be divided, and the applicable scope of the model is expanded.

Efficient dynamic division method of software and hardware

The invention discloses an agreement inventory material order allocation method based on greedy algorithm, comprising a data import and export unit, an information query and display unit, a material allocation unit and an information maintaining unit. An excel sheet downloaded by ERP (Enterprise Resource Planning) is imported into a software database through a data import and export unit, corresponding planned batches and allocation modes are selected and allocation is executed in the material allocation unit, moreover, allocation result and information of unallocated material can be checked in the information query and display unit. According to the invention, four agreement inventory material allocation modes suitable for the material allocation needs of power supply companies are set, the modes are realized through a software unit, planned allocation of material is carried out automatically through importing ERP and material planning data, and thus, efficiency of material allocation of the power supply companies is improved.

Agreement inventory material order allocation method based on greedy algorithm

The invention discloses a software and hardware partitioning method on the basis of improved brainstorming algorithms. The software and hardware partitioning method includes initializing parameters; initializing cluster centers; starting iteration updating and ranking individuals from small to large according to fitness values; sequentially starting to compute the distance from each individual toeach cluster center from the first ranked individuals; updating optimal individuals in each cluster; randomly selecting an individual from the clusters and generating a new individual; shifting the randomly selected individual towards global optimal individuals by random lengths randomly generating a new individual meeting hardware area constraint conditions and replacing the randomly selected individual with the new individual; completing an iteration updating process; outputting the optimal individuals to be used as optimal software and hardware partitioning schemes. First-rank individuals sorted according to the fitness values are the global optimal individuals. The software and hardware partitioning method has the advantages that cluster modes and individual updating modes are improved, accordingly, the efficiency of each iteration process can be effectively enhanced, premature convergence can be prevented, the global optimization ability can be omitted, the solution quality can beeffectively enhanced, and the convergence speeds can be effectively increased.

Software and hardware partitioning method on basis of improved brainstorming algorithms

The present invention discloses a nested dynamic environment change detection method. The method comprises: extracting characteristic parameters related to a current division task set; setting a sampling number as M; determining whether the current environment division scale is equal to the previous environment division scale or not; evaluating the fitness of M sampling individuals in the current environment, determining whether the fitness is the same with that under the previous environment evaluation or not, so as to determine whether a fitness function of the current environment is equal to that in the previous environment or not; successively determining whether each component in a performance constraint set of the current environment is equal to each corresponding component in a performance constraint set of the previous environment or not; successively determining whether values at M corresponding positions of an adjacency matrix in the current environment are equal to those at M corresponding positions of an adjacency matrix in the previous environment; successively determining whether values at M corresponding positions in node attributes of the current environment are equal to those at M corresponding positions in node attributes of the previous environment; and ending the detection. The nested dynamic environment change detection method disclosed by the present invention enables an algorithm to be able to predict changes of dynamic problems more accurately.

Nested dynamic environment change detection method

A hardware-software partitioning method based on a greedy simulated annealing algorithm comprises the following steps: (1) specifying the hardware-software partitioning problem as 0-1 knapsack problem, initially partitioning a task set by a greedy algorithm with low time complexity, and setting the partitioning result as the initial value of the simulated annealing algorithm; and (2) performing the simulated annealing algorithm, which is mainly composed of two layer loops, namely an inner loop and an outer loop, wherein the inner loop can generate a new partition according to a disturbance model, and determine and accept the new partition according to an acceptance criteria; and the outer loop is used for determining whether or not to exit the loop process according to a temperature threshold and the number of new partitions which are not accepted continuously. The method provided by the invention can reduce algorithm operation time, improve search quality and reduce calculation complexity.

Hardware-software partitioning method based on greedy simulated annealing algorithm

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Hardware-software partitioning method based on greedy simulated annealing algorithm