Adaptive search algorithm for SRIO network routing based on chaotic ant colony algorithm

2021 ◽  
Author(s):  
Yi Yang ◽  
Qian Zhang ◽  
Gang Li
Keyword(s):  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Network Routing ◽  
Ant Colony ◽  
Adaptive Search

scholarly journals Path Planning Based on an Improved Ant Colony Algorithm

2018 ◽  
Vol 228 ◽  
pp. 01010
Author(s):  
Miaomiao Wang ◽  
Zhenglin Li ◽  
Qing Zhao ◽  
Fuyuan Si ◽  
Dianfang Huang
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Local Optimum ◽  
Pheromone Concentration ◽  
Suppression Strategy ◽  
Improved Ant Colony Algorithm ◽  
Robot Path

The classical ant colony algorithm has the disadvantages of initial search blindness, slow convergence speed and easy to fall into local optimum when applied to mobile robot path planning. This paper presents an improved ant colony algorithm in order to solve these disadvantages. First, the algorithm use A* search algorithm for initial search to generate uneven initial pheromone distribution to solve the initial search blindness problem. At the same time, the algorithm also limits the pheromone concentration to avoid local optimum. Then, the algorithm optimizes the transfer probability and adopts the pheromone update rule of "incentive and suppression strategy" to accelerate the convergence speed. Finally, the algorithm builds an adaptive model of pheromone coefficient to make the pheromone coefficient adjustment self-adaptive to avoid falling into a local minimum. The results proved that the proposed algorithm is practical and effective.

Effective ACPS-Based Rescheduling of Parallel Batch Processing Machines with MapReduce

2014 ◽  
Vol 575 ◽  
pp. 820-824
Author(s):  
Bin Zhang ◽  
Jia Jin Le ◽  
Mei Wang
Keyword(s):  
Big Data ◽  
Parallel Computing ◽  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Ant Colony ◽  
Search Problem ◽  
Parallel Search ◽  
Good Efficiency ◽  
Heuristic Scheduling ◽  
Low Efficiency

MapReduce is a highly efficient distributed and parallel computing framework, allowing users to readily manage large clusters in parallel computing. For Big data search problem in the distributed computing environment based on MapReduce architecture, in this paper we propose an Ant colony parallel search algorithm (ACPSMR) for Big data. It take advantage of the group intelligence of ant colony algorithm for global parallel search heuristic scheduling capabilities to solve problem of multi-task parallel batch scheduling with low efficiency in the MapReduce. And we extended HDFS design in MapReduce architecture, which make it to achieve effective integration with MapReduce. Then the algorithm can make the best of the scalability, high parallelism of MapReduce. The simulation experiment result shows that, the new algorithm can take advantages of cloud computing to get good efficiency when mining Big data.

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.

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.

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 Multirobot searching method of natural gas leakage sources on offshore platform using ant colony optimization

2020 ◽  
Vol 17 (5) ◽  
pp. 172988142095901
Author(s):  
Tao Ma ◽  
Shuhai Liu ◽  
Huaping Xiao
Keyword(s):  
Natural Gas ◽  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Offshore Platform ◽  
Ant Colony ◽  
Detection Methods ◽  
Offshore Platforms ◽  
Gas Leakage ◽  
Source Positioning

Natural gas leakage on offshore platforms has a great impact on safety production, and effective and reasonable leakage detection methods can prevent the harm caused by natural gas leakage. This article proposes a method based on ant colony optimization (ACO) for multirobots to collaboratively search for leaking natural gas sources on offshore platforms. First, analyze the structure and environment of the offshore platform, use Fluent software to simulate the diffusion process of natural gas leaked from the platform, and establish a diffusion model of natural gas leaked from various aspects, such as the layout of different platforms, the number of leaked gas sources, and the concentration of leaked gas sources. In terms of multirobot cooperative control, we analyzed and improved the ant colony algorithm and proposed a multirobot cooperative search strategy for gas search, gas tracking, and gas source positioning. The multirobot search process was simulated using MATLAB software, and the robot on the detection effect of multirobots was analyzed in many aspects, such as quantity, location of leak source, and a number of leak sources, which verified the feasibility and effectiveness of the multirobot control strategy based on optimized ACO. Finally, we analyze and compare the two control algorithms based on ACO and cuckoo search algorithm (CSA). The results show that the ACO-based multirobot air source positioning effect is significantly better than CSA.

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>

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