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Based on the current cloud computing resources security distribution model’s problem that the optimization effect is not high and the convergence is not good, this paper puts forward a cloud computing resources security distribution model based on improved artificial firefly algorithm. First of all, according to characteristics of the artificial fireflies swarm algorithm and the complex method, it incorporates the ideas of complex method into the artificial firefly algorithm, uses the complex method to guide the search of artificial fireflies in population, and then introduces local search operator in the firefly mobile mechanism, in order to improve the searching efficiency and convergence precision of algorithm. Simulation results show that, the cloud computing resources security distribution model based on improved artificial firefly algorithm proposed in this paper has good convergence effect and optimum efficiency.

Cloud computing service model can save a lot of investment cost, get rid of resources such as regional restriction and time limitation, and complete the needs of different users for various calculations, storage, etc. [

At present the latest research on the distribution of mobile cloud computing resources is mainly concentrated on the application of cloud computing in mobile terminals. The direction of the present study was to make the application of mobile cloud computing run on resource-constrained mobile terminals, and it can also run on the “cloud” based on the Internet [

This paper, according to the characteristics of cloud computing, puts forward a cloud computing resources security distribution model based on improved artificial firefly algorithm, in order to improve the security of cloud computing resources allocation.

The evolution process of the basic artificial firefly algorithm is completely inspired by nature fireflies group behavior, the group interaction is pushed by the intensity of fluorescence intensity and its influence. In basic artificial firefly algorithm, first we distribute the fireflies which have the same content of fluorescein and same independent decision domain in the target space we need to search by random, they communicate in their own decision domain, thus affecting the individual behavior of the fireflies around.

According to the biological characteristics of nature fireflies and the matching modify rules of artificial firefly algorithm, the basic artificial firefly algorithm in the algorithm can be divided into six stages, details are as follows:

(1) The initial deployment phase

n fireflies were randomly distributed in D dimensional search space, each firefly i has the same initial radius

(2) On the basis of the objective function mathematical expression f to calculate the objective function value F of all fireflies’ locations, and in accordance with the following formula, we transform it into the corresponding fluorescence intensity value:

(3) Determine the neighbor

At this stage, artificial induction fireflies search the distance neighbor

As can be seen from the

(4) Choose the moving objects

According to the surely neighbor set

(5) Move the fireflies

Move the firefly

(6) Adaptive sensing radius

After firefly moving, we will modify the next iteration of the sensing radius

The change of

Increased with the dimensions of the function, the solution space of the function is expanding rapidly, caused the complexity of the function. So in the later stages of artificial firefly algorithm, there will be a lot of bad fireflies around the optimal fireflies, frustrate the optimization of the algorithm precision, so that the algorithm in the final optimization precision will have very big defects.

According to characteristics of the artificial fireflies swarm algorithm and the complex method, incorporate the ideas of complex method into the artificial firefly algorithm, use the complex method to guide the search of artificial fireflies in population. Algorithm thought graph description as shown in

In the iteration, we will use the improved equation in this paper:

On the base of the complex method of population search optimization steps as follows:

(1) Random initialization

(2) Random initialization

(3) Use the formula (8) to get the center

(4) Each firefly within their own decision-making domain

(5) Through the comparison of fluorescein value, find out the worst firefly luciferin values, then according to the formula (9) to reflect;

(6) According to the formula (4) calculating the probability of firefly

(7) According to the formula (7), dynamic update decision domain radius of fireflies.

In basic GSO algorithm, each firefly in its decision-making domain finds the firefly which has brighter luciferin, then they move to it, they use roulette method to move to it based on a certain probability, this will make the firefly mobile position is not the best location in decision domain, it will reduce the optimization efficiency and convergence precision of the algorithm. In this paper, the firefly mobile mechanism is introduced in local search operator, algorithm thought graphics can be described in

The concrete implementation steps of GSO algorithm with local search operator are:

(1) Initializes the fireflies’ number and dimension of search space, the parameters which is needed to initialize;

(2) According to the formula (2) calculate the firefly

(3) Each firefly within their own decision-making domain

(4) According to the formula (4) calculate the probability that firefly

(5) Make the individual

(6) Make sure the new moving direction by local searching, and according to the formula (6) to update the location;

(7) According to the formula (7), dynamic update decision domain radius of fireflies.

In order to verify the effectiveness of the improved algorithm proposed in this paper, we do the simulation experiments on it. We do the cloud computing resources security distribution optimization efficiency and convergence precision test to the standard artificial firefly algorithm and improved artificial firefly algorithm. The result is shown in

From the simulation results showed that the proposed improved artificial firefly algorithm has better convergence and optimization efficiency, and the application in the cloud computing resources security distribution is good.

Cloud computing is a way of calculation which is developed with the development of computing and communications technology in recent years. How to make full use of the existing cloud computing resources, through the reasonable allocation of cloud computing resources between different areas to improve the quality of the cloud computing security service and system returns of the cloud computing network system, so as to improve the ratio of revenue and expenditure of cloud computing network, has become an increasingly prominent research topic in the cloud computing network. The simulation experimental results show that the improved model proposed in this paper has higher optimization efficiency and convergence, and it has good application effect.

This work was supported by the National Natural Science Foundation of China (Grant No. 61170132).