Intelligent traffic cloud computing system based on ant colony algorithm

With the growth of data volume in transportation system, requirements of big data technologies are rapidly increasing. This paper presented an improved ant colony algorithm by using data analysis technologies of cloud computing and data mining. And the influence of different spatio-temporal feature fusion methods on the steering wheel angle value of intelligent vehicles is explored by feature fusion method. Furthermore, time-constrained and space-constrained networks are utilized to extract the key features that affect the steering wheel angle value. Experimental results show that the proposed algorithm improves the efficiency of data processing and information search by 35%, comparing to traditional ant colony algorithm. It is very effective in the shortest path analysis of ITS. Our research shows that the application of real-time information in the logistics distribution system can make the planning process more dynamic and the prediction results closer to reality.

Download Full-text

Optimized Resource Allocation Algorithm for Crowd-Creation Space Computing Based on Cloud Computing Environment

10.54216/jisiot.040101 ◽

2021 ◽

pp. 08-25

Author(s):

Mustafa El .. ◽

◽

Aaras Y Y.Kraidi

Keyword(s):

Resource Allocation ◽

Cloud Computing ◽

Simulated Annealing ◽

Task Allocation ◽

Ant Colony Algorithm ◽

Simulated Annealing Algorithm ◽

Ant Colony ◽

Computing Environment ◽

Cloud Computing Environment ◽

Resource Allocation Algorithm

The crowd-creation space is a manifestation of the development of innovation theory to a certain stage. With the creation of the crowd-creation space, the problem of optimizing the resource allocation of the crowd-creation space has become a research hotspot. The emergence of cloud computing provides a new idea for solving the problem of resource allocation. Common cloud computing resource allocation algorithms include genetic algorithms, simulated annealing algorithms, and ant colony algorithms. These algorithms have their obvious shortcomings, which are not conducive to solving the problem of optimal resource allocation for crowd-creation space computing. Based on this, this paper proposes an In the cloud computing environment, the algorithm for optimizing resource allocation for crowd-creation space computing adopts a combination of genetic algorithm and ant colony algorithm and optimizes it by citing some mechanisms of simulated annealing algorithm. The algorithm in this paper is an improved genetic ant colony algorithm (HGAACO). In this paper, the feasibility of the algorithm is verified through experiments. The experimental results show that with 20 tasks, the ant colony algorithm task allocation time is 93ms, the genetic ant colony algorithm time is 90ms, and the improved algorithm task allocation time proposed in this paper is 74ms, obviously superior. The algorithm proposed in this paper has a certain reference value for solving the creative space computing optimization resource allocation.

Download Full-text