Intelligent Ant Colony Algorithm Research Articles

Research on Secure Encryption and Transmission of Disaster Backup of Massive Data Based on Cloud Computing

In order to ensure the protection of important data in the face of natural and man-made disasters in data centers in cloud computing services, sufficient backups of the data are required. This paper briefly introduced the security encryption measures for disaster backup of data under cloud computing. The transmission performance during disaster backup was improved by the intelligent ant colony algorithm. Simulation experiments were performed to compare the encryption performance of two encryption algorithms, the Rivest, Shamir, and Adleman (RSA) algorithm and the Advanced Encryption Standard (AES) algorithm, in disaster backup, and the transmission performance of the intelligent ant colony algorithm and the maximum network flow algorithm. The results showed that the asymmetric encryption algorithm and the RSA algorithm had better encryption performance than the AES algorithm in terms of disaster backup; with the increase of backup data volume, the transmission performance of both route assignment algorithms decreased, but the intelligent ant colony algorithm always outperformed the maximum network flow algorithm.

Research on navigation of bidirectional A* algorithm based on ant colony algorithm

To overcome the lengthy search time, massive space occupation, and overlong planned path of the traditional A* algorithm, this paper integrates the bidirectional search with the intelligent ant colony algorithm to obtain the heuristic function selection factor, and uses the factor to improve the evaluation function of the algorithm. The simulation results show that the improved algorithm achieved better dynamic navigation than the traditional A* algorithm both in search time and distance, featuring shorter path searching time and the algorithm running time. Therefore, the result of this research has effectively reduced the search time and enhanced the dynamic search.

Application of improved intelligent ant colony algorithm in protein folding prediction

While the single ant colony algorithm and the fish swarm algorithm have many advantages, they also have various shortcomings. After analyzing the advantages and disadvantages of the ant colony algorithm and the fish swarm algorithm, this paper uses the complementary principle of the two algorithms to effectively fuse the two population intelligent algorithms. The improved swarm intelligence algorithm is applied to the well-considered protein folding prediction problem, and the simplified protein structure Toy model is verified, and the ideal results are obtained. The improved algorithm enhances the search ability, and the computational efficiency is greatly improved, ensuring the accuracy of the operation.

Clustering and Path Planning for Wireless Sensor Networks Based on Improved Ant Colony Algorithm

To make up the deficiency of artificial intelligent ant colony algorithm in solving the clustering and path planning of wireless sensor network (WSN) a new random disturbance factor is proposed. A self-regulated random disturbance ant colony algorithm is obtained. An improved ant colony algorithm is proposed by combining the self-regulated random disturbance ant colony algorithm with chaos. After the algorithm improvement is completed, the improved artificial intelligent ant colony algorithm is applied to the cluster head fixed WSN node cluster and the path optimization process of each cluster head communication with the base station. The convergence speed, energy consumption and the survival time of the node cluster head are analyzed. The results show that the improved ant colony algorithm has good stability characteristics in the application and convergence of WSN. It can be seen that the improved ant colony algorithm is feasible in clustering and path planning of WSN.

An Improved LEACH Multi-hop Routing Protocol Based on Intelligent Ant Colony Algorithm for Wireless Sensor Networks

In view of limited node energy, how to reduce energy consumption and improve the network lifetime is an important issue in wireless sensor networks. In this paper, by integrating the theory of intelligent ant colony algorithm with LEACH, we proposed an improving routing algorithm LEACH-IACA (LEACHIntelligent Ant Colony Algorithm) based on multi-hop routing protocol. The core idea is to evaluate cluster heads’ current residual energy and location information from the perspective of overall network in real-time, and single ant traverses all nodes in one time, forming a dendritic multi-hop path. While the path is rebuilt, low-energy nodes select energy-saving path, high energy nodes increase energy consumption as consideration to prolong the network lifetime. The simulation shows that the new method is superior to tranditional LEACH routing algorithm, since it effectively improves the network lifetime, reduces average energy consumption.

An Improved Intelligent Ant Colony Algorithm for the Reliability Optimization Problem in Cyber-Physical Systems

In this paper the torsion bar optimization problem of reliability is considered. Since this problem is a difficult optimization problem, an improved intelligent ant colony algorithm is proposed to solve the problem. This algorithm comprises five stages. First stage is the initialization of pheromone and the sensor node configuration. The second stage is to select the next sensor node. The third stage is to update the pheromone of the sensor node path. The fourth stage is to acquire the best path by computing the shortest distance. The last stage is to output the global optimal solution. To evaluate performance of the proposed algorithm, it is compared with the ant colony optimization algorithm and the genetic algorithm. The experimental results show that the proposed algorithm performs better than them.

Expert System Aided Multi-Agent Intelligent Ant Colony Optimization System

In the dynamic production environment of supply chain, based on information sharing among enterprises of supply chain, this paper designs an expert system aided multi-agent intelligent ant colony algorithm system to solve the production scheduling optimization model. Where ant colony is constructed with multi-agent and the order decomposition structure and constraint are expressed by expert system. And then it builds a system using JESS and JADE to confirm this algorithm applied in a mass customization supply chain scheduling model