An Improved Decision Strategy of Network Routing Based on Ant Colony Algorithm

2014 ◽  
Vol 599-601 ◽  
pp. 1378-1382
Author(s):  
Yu Bo Jia ◽  
Qian Qian Ding ◽  
Dan Li Liu ◽  
Yun Long Zhang ◽  
Jian Feng Zhang
Keyword(s):  
Convergence Rate ◽  
Ant Colony Algorithm ◽  
Memory Device ◽  
Network Routing ◽  
Ant Colony ◽  
Utilization Rate ◽  
Decision Strategy ◽  
Slow Convergence ◽  
Network Utilization ◽  
High Efficient

-Aiming at getting a high efficient network routing decision strategy, to settle problems of slow convergence speed and easily to fall into local optimal, this paper proposes a new decision strategy based on ant colony algorithm. Memory device to record each time pheromone value and pheromone differences value of the adjacent times to decide follow the former route or find a new one are the focus of this paper. The new decision technology accelerates the convergence rate, improves network utilization rate and accuracy of network routing.

Cloud computing demand elasticity algorithm based on ant colony algorithm

2020 ◽  
Vol 13 ◽  
Author(s):  
Chunyu Liu ◽  
Fengrui Mu ◽  
Weilong Zhang
Keyword(s):  
Ant Colony Algorithm ◽  
Theory Of Elasticity ◽  
Ant Colony ◽  
Demand Elasticity ◽  
Network Efficiency ◽  
Elasticity Of Demand ◽  
Operating Profit ◽  
Network Load ◽  
Network Utilization ◽  
The Impact

Background: In recent era of technology, the traditional Ant Colony Algorithm (ACO) is insufficient in solving the problem of network congestion and load balance, and network utilization. Methods: This paper proposes an improved ant colony algorithm, which considers the price factor based on the theory of elasticity of demand. The price factor is denominated in the impact on the network load which means indirect control of network load, congestion or auxiliary solution to calculate the idle resources caused by the low network utilization and reduced profits. Results: Experimental results show that the improved algorithm can balance the overall network load, extend the life of path by nearly 3 hours, greatly reduce the risk of network paralysis, and increase the profit of the manufacturer by 300 million Yuan. Conclusion: Furthermore, results shows that the improved method has a great application value in improving the network efficiency, balancing network load, prolonging network life and increasing network operating profit.

Ant Colony-Based Load Balancing Algorithm for Multi-Source Multicast Networks

2011 ◽  
Vol 204-210 ◽  
pp. 1399-1402
Author(s):  
Ling Xiu Wang ◽  
Ye Wen Cao
Keyword(s):  
Ant Colony Algorithm ◽  
Minimum Spanning Tree ◽  
Ant Colony ◽  
Multicast Networks ◽  
Multicast Protocol ◽  
Multicast Protocols ◽  
Network Utilization ◽  
Multicast Traffic ◽  
Multicast Scheduling ◽  
Load Balancing Algorithm

IP multicast protocols tend to construct a single minimum spanning tree for a multicast source (i.e., group), in which only a few internal nodes supply multicast traffic. In multicast networks especially with multiple multicast sources where bottleneck effects may occur frequently, frequently used multicast service leads to inefficient network utilization problems. This paper presents a new network utilization algorithm for multicasting called load distribution algorithm (LDA). The LDA algorithm uses selecting candidate path based on ant colony algorithm and multicast scheduling to distribute the contention multicast packets onto their corresponding candidate paths. The numerical results show that a multicast protocol with LDA has higher efficiency of resource utilization and meanwhile maintains less end to end delay compared with the original one without LDA.

Research on Application of Ant Colony Optimization

2014 ◽  
Vol 548-549 ◽  
pp. 1213-1216
Author(s):  
Wang Rui ◽  
Zai Tang Wang
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Slow Convergence ◽  
Simulation Results ◽  
Research On Application

We research on application of ant colony optimization. In order to avoid the stagnation and slow convergence speed of ant colony algorithm, this paper propose the multiple ant colony optimization algorithm based on the equilibrium of distribution. The simulation results show that the optimal algorithm can have better balance in reducing stagnation and improving the convergence.

Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining

2012 ◽  
Vol 433-440 ◽  
pp. 3577-3583
Author(s):  
Yan Zhang ◽  
Hao Wang ◽  
Yong Hua Zhang ◽  
Yun Chen ◽  
Xu Li
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Computing Power ◽  
Slow Convergence ◽  
The Impact

To overcome the defect of the classical ant colony algorithm’s slow convergence speed, and its vulnerability to local optimization, the authors propose Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining to solve Traveling Salesman Problem according to the characteristics of natural ant colony multi-group and pheromone updating features of ant colony algorithm, combined with OpenMP parallel programming idea. The new algorithm combines three different pheromone updating methods to make a new declining pheromone updating method. It effectively reduces the impact of pheromone on the non-optimal path in the ants parade loop to subsequent ants and improves the parade quality of subsequent ants. It makes full use of multi-core CPU's computing power and improves the efficiency significantly. The new algorithm is compared with ACO through experiments. The results show that the new algorithm has faster convergence rate and better ability of global optimization than ACO.

Parallel Ant Colony Algorithm Based on Award-Punishment Mechanism and its Application on Wireless Sensor Network Routing

2013 ◽  
Vol 397-400 ◽  
pp. 1117-1120
Author(s):  
Hai Yang
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Energy Efficient ◽  
Ant Colony Algorithm ◽  
Network Routing ◽  
Ant Colony ◽  
Wireless Sensor ◽  
Energy Consumption Model ◽  
Punishment Mechanism

As a new method of obtaining information and disposal pattern, wireless sensor network has been a hot issue nowadays. In this paper the network model and energy consumption model of wireless sensor network are introduced firstly. The improved inspiring factor takes energy into account. Then a parallel ant colony algorithm based on award-punishment mechanism is proposed. The experimental results show that the energy consumption and time delay of the improved algorithm are superior to energy efficient ant based routing and basic ant colony algorithm.

Action Decision Strategy of Simulation Robot Fish Based on Ant Colony Algorithm

2013 ◽  
Vol 341-342 ◽  
pp. 949-954
Author(s):  
Xu Qiong Yang ◽  
Xue Le Jia ◽  
Yan Song Deng
Keyword(s):  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Decision Strategy ◽  
Dynamic Algorithm ◽  
Process Adjustment ◽  
2D Simulation ◽  
Movement Strategy ◽  
Robot Fish ◽  
Variable Movement ◽  
Action Decision

In order to achieve the quick and accurate adjustment of the robot fish, there are two plans, which are based on ant-colony-algorithm movement strategy. These plans are aimed at 2D robot fish in the ant colony algorithm, and the key judgment of the robot fish relies on the branches of physic matters, together with the best match of the fish in its current speed. However, according to the dynamic algorithm project, the feedback of dynamic variable movement can process adjustment automatically. In every period, these two examples by which the 2D Simulation results show the robot fish can be adjusted on the policy path. To achieve optimal combination of speed and direction, the shortest time has a strong ability to adapt effectively to meet the simulation of robot fish or action of the action decision-making.

scholarly journals An Improved Ant Colony Optimization Algorithm with Crossover Operator

2014 ◽  
Vol 8 (1) ◽  
pp. 96-100
Author(s):  
Junen Guo ◽  
Wenguang Diao
Keyword(s):  
Integrated Circuit ◽  
Large Scale ◽  
Ant Colony Algorithm ◽  
Network Routing ◽  
Function Optimization ◽  
Ant Colony ◽  
Crossover Operator ◽  
Ant Colony Optimization Algorithm ◽  
Integrated Circuit Design ◽  
Optimization Function

Ant colony algorithm has been widely applied to lots of fields, such as combinatorial optimization, function optimization, system identification, network routing, robot path planning, data mining and large-scale integrated circuit design of integrated wiring, etc. And it achieved good results. But it still has one weak point which is the slowing convergence speed. To aim at the lacks, an improved ACO is presented. This paper studies a kind of improved ant colony algorithm with crossover operator which makes crossover operator among better results at the end of each iteration. The experiment results indicate that the improved ACO is effectual.

scholarly journals An Improved Ant Colony Algorithm in Wireless Sensor Network Routing

2017 ◽  
Vol 13 (05) ◽  
pp. 174 ◽  
Author(s):  
Liping LV
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Ant Colony Algorithm ◽  
Fitness Function ◽  
Optimal Solution ◽  
Network Routing ◽  
Ant Colony ◽  
Wireless Sensor ◽  
Destination Node ◽  
Ant Colony Optimization Algorithm

<p class="0abstract"><span lang="EN-US">In order to make the energy consumption of network nodes relatively balanced, we apply ant colony optimization algorithm to wireless sensor network routing and improve it.</span><span lang="EN-US"> In this paper, we propose a multi-path wireless sensor network routing algorithm based on energy equalization. The algorithm uses forward ants to find the path from the source node to the destination node, and uses backward ants to update the pheromone on the path. In the route selection, we use the energy of the neighboring nodes as the parameter of the heuristic function. At the same time, we construct the fitness function, and take the path length and the node residual energy as its parameters. The simulation results show that the algorithm can not only avoid the problem of local optimal solution, but also prolong the life cycle of the network effectively.</span></p>

Review of Task Scheduling Algorithm Research in Cloud Computing

2014 ◽  
Vol 926-930 ◽  
pp. 3236-3239 ◽  
Author(s):  
Mei Geng Huang ◽  
Zhi Qi Ou
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Task Scheduling ◽  
Ant Colony Algorithm ◽  
Particle Swarm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Swarm Optimization ◽  
Slow Convergence

The cloud computing task scheduling field representative algorithms was introduced and analyzed : genetic algorithm, particle swarm optimization, ant colony algorithm. Parallelism and global search solution space is the characteristic of genetic algorithm, genetic iterations difficult to proceed when genetic individuals are very similar; Particle swarm optimization in the initial stage is fast, slow convergence speed in the later stage ; Ant colony algorithm optimization ability is good, slow convergence speed in its first stage; Finally, the summary and prospect the future research direction.

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