scholarly journals Multi-Controller Load Balancing Algorithm for Test Network Based on IACO

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1901
Author(s):  
Yanfang Fu ◽  
Yuting Zhu ◽  
Zijian Cao ◽  
Zhiqiang Du ◽  
Guochuang Yan ◽  
...  
Keyword(s):  
Load Balancing ◽  
Ant Colony Algorithm ◽  
Flight Test ◽  
Cooperative Networks ◽  
Ant Colony ◽  
Local Optimum ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Asymmetric Load ◽  
Load Balancing Algorithm

With the rapid increase of volume and complexity in the projectile flight test business, it is becoming increasingly important to improve the quality of the service and efficiency of multi-domain cooperative networks. The key for these improvements is to solve the problem of asymmetric load of multi-controllers in multi-domain networks. However, due to the current reality, it is difficult to meet the demands of future tests, and there is not guarantee of subnet multi-domain test load balancing. Most recent works have used a heuristic approach to seek the optimal dynamic migration path, but they may fall into the local optimum. This paper proposes an improved ant colony algorithm (IACO) that can transform the modeling of the mapping relationship between the switch and the controller into a traveling salesman problem by combining the ant colony algorithm and artificial fish swarm algorithm. The IACO not only ensures the load balancing of multi-controllers but also improves the reliability of the cluster. The simulation results show that compared to other algorithms such as traditional ant colony algorithms and distributed decision mechanisms, this IACO achieves better load balancing, improves the average throughput of multi-controller clusters, and effectively reduces the response time of controller request events.

Logistics Route Optimization Based on Improved Fish Swarm Algorithm

2015 ◽  
Vol 713-715 ◽  
pp. 1761-1764
Author(s):  
Feng Kai Xu
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Ant Colony Algorithm ◽  
Low Cost ◽  
Ant Colony ◽  
Route Optimization ◽  
Artificial Fish Swarm Algorithm ◽  
Logistics Distribution ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

In order to achieve a low cost and low exhaust pollution in logistics distribution path. In view of the shortages of existing genetic algorithm and ant colony algorithm which have the characteristics of some limitations, such as ant colony algorithm's convergence slow, easy going, the characteristics of such as genetic algorithm premature convergence in the process of path optimization, process complex, the paper proposed the improved artificial fish swarm algorithm in order to solve logistics route optimization problem. At last, through simulation experiment, the improved artificial fish swarm algorithm is proved correct and effective.

Approximate Inference Algorithm Based on Adaptive Ant Colony and Artificial Fish Swarm Algorithm for Credal Networks

2011 ◽  
Vol 219-220 ◽  
pp. 1504-1508
Author(s):  
Ying Qu ◽  
Pang Zhou
Keyword(s):  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Directed Acyclic Graphs ◽  
Ant Colony ◽  
Approximate Inference ◽  
Artificial Fish Swarm Algorithm ◽  
Combinatorial Optimization Problems ◽  
Swarm Algorithms ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

This paper presents a new algorithm for approximate inference in credal networks (that is, models based on directed acyclic graphs and interval-valued probabilities). Approximate inference in credal networks can be considered as multistage decision in this paper. It is looked as combinatorial optimization problems that obtaining the extreme posteriors from the combinations of various vertices in credal networks. Based on this, the paper combines two intelligence swarm algorithms (ant colony algorithm and artificial fish swarm algorithm) to obtain interval posterior probabilities of query variable for the states of given evidence variables.

scholarly journals Crow Search based Scheduling Algorithm for Load Balancing in Cloud Environment

2019 ◽  
Vol 8 (9) ◽  
pp. 1058-1064
Keyword(s):  
Comparative Analysis ◽  
Load Balancing ◽  
Task Scheduling ◽  
Ant Colony Algorithm ◽  
Scheduling Algorithm ◽  
Research Paper ◽  
Global Optimum ◽  
Ant Colony ◽  
Load Balancing Algorithm ◽  
Optimum Solution

Task scheduling is a vital aspect in computer science, as it is the essence of how a computer executes various tasks and performs the related activities with accuracy and efficiency. In cloud computing, task scheduling is a typical NP-hard problem and scientists have been attempting to handle this problem for quite a long time. Although it is anything but difficult to accomplish a global optimum solution utilizing the ant colony algorithm and achieve promising outcomes. This research paper proposes a crow search based load balancing algorithm (CSLBA) for multi-objective task scheduling environment which concentrates on allocating best suitable resources for the task to be implemented with the consideration of various parameters like average makespan time (AMT), average waiting time (AWT) and average data center processing time (ADCPT). The present work provides a comparative analysis of proposed algorithm and Ant Colony Optimization based load balancing algorithm (ACOLBA). The experimentation is performed in steps and comparative analysis proved that proposed CSLBA is the optimal technique among the other scheduling technique considered in this research paper.

A Mixed Ant Colony Algorithm for Problem of Function Optimization

2011 ◽  
Vol 101-102 ◽  
pp. 216-219
Author(s):  
Yan Jun Luo ◽  
Zhao Yu Bei
Keyword(s):  
Ant Colony Algorithm ◽  
Local Optimization ◽  
Function Optimization ◽  
Ant Colony ◽  
Continuous Space ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

Ant colony algorithm has disadvantages such as long researching time and easily relapsing into local optimization. Artificial fish-swarm algorithm is presented to conquer the disadvantages. The combination of the two algorithms is applied in function optimization to overcome the limitation that the ant colony algorithm does not fit to solve continuous space optimization. The tested function shows the effect of the method.

scholarly journals Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Artificial Fish Swarm Algorithms

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Kuan-Cheng Lin ◽  
Sih-Yang Chen ◽  
Jason C. Hung
Keyword(s):  
Feature Selection ◽  
Parameter Optimization ◽  
Combinatorial Problem ◽  
Combinatorial Problems ◽  
Support Vector ◽  
Local Optimum ◽  
Artificial Fish Swarm Algorithm ◽  
Swarm Algorithms ◽  
Artificial Fish Swarm ◽  
Information And Communication

Rapid advances in information and communication technology have made ubiquitous computing and the Internet of Things popular and practicable. These applications create enormous volumes of data, which are available for analysis and classification as an aid to decision-making. Among the classification methods used to deal with big data, feature selection has proven particularly effective. One common approach involves searching through a subset of the features that are the most relevant to the topic or represent the most accurate description of the dataset. Unfortunately, searching through this kind of subset is a combinatorial problem that can be very time consuming. Meaheuristic algorithms are commonly used to facilitate the selection of features. The artificial fish swarm algorithm (AFSA) employs the intelligence underlying fish swarming behavior as a means to overcome optimization of combinatorial problems. AFSA has proven highly successful in a diversity of applications; however, there remain shortcomings, such as the likelihood of falling into a local optimum and a lack of multiplicity. This study proposes a modified AFSA (MAFSA) to improve feature selection and parameter optimization for support vector machine classifiers. Experiment results demonstrate the superiority of MAFSA in classification accuracy using subsets with fewer features for given UCI datasets, compared to the original FASA.

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.

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.

Cutting trajectory planning of sections with complex composition for roadheader

2018 ◽  
Vol 233 (4) ◽  
pp. 1441-1452 ◽  
Author(s):  
Suyu Wang ◽  
Miao Wu
Keyword(s):  
Trajectory Planning ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Grid Method ◽  
Ant Colony ◽  
Local Optimum ◽  
Complex Composition ◽  
Cutting Head ◽  
Pheromone Concentration ◽  
Simulation Results

In order to realize the autonomous cutting for tunneling robot, the method of cutting trajectory planning of sections with complex composition was proposed. Firstly, based on the multi-sensor parameters, the existence, the location, and size of the dirt band were determined. The roadway section environment was modeled by grid method. Secondly, according to the cutting process and tunneling cutting characteristics, the cutting trajectory ant colony algorithm was proposed. To ensure the operation safety and avoid the cutting head collision, the expanding operation was adopt for dirt band, and the aborting strategy for the ants trapped in the local optimum was put forward to strengthen the pheromone concentration of the found path. The simulation results showed that the proposed method can be used to plan the optimal cutting trajectory. The ant colony algorithm was used to search for the shortest path to avoid collision with the dirt band, and the S-path cutting was used for the left area to fulfill section forming by following complete cover principle. All the ants have found the optimal path within 50 times iteration of the algorithm, and the simulation results were better than particle swarm optimization and basic ant colony optimization.

Overview of Artificial Fish Swarm Algorithm and its Applications in Industrial Problems

2015 ◽  
Vol 815 ◽  
pp. 253-257 ◽  
Author(s):  
Nurezayana Zainal ◽  
Azlan Mohd Zain ◽  
Safian Sharif
Keyword(s):  
Optimization Algorithm ◽  
Social Behaviors ◽  
Global Optimum ◽  
Local Optimum ◽  
Intelligent Optimization ◽  
Artificial Fish Swarm Algorithm ◽  
Individual Fish ◽  
Intelligent Optimization Algorithm ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

Artificial fish swarm algorithm (AFSA) is a class of swarm intelligent optimization algorithm stimulated by the various social behaviors of fish in search of food. AFSA can search for global optimum through local optimum value search of each individual fish effectively based on simulating of fish-swarm behaviors such as searching, swarming, following and bulletin. This paper presents an overview of AFSA algorithm by describing the evolution of the algorithm along with all the improvements and its combinations with various algorithms and methods as well as its applications in solving industrial problems.

scholarly journals Path planning of lunar robot based on dynamic adaptive ant colony algorithm and obstacle avoidance

2020 ◽  
Vol 17 (3) ◽  
pp. 172988141989897 ◽  
Author(s):  
Shinan Zhu ◽  
Weiyi Zhu ◽  
Xueqin Zhang ◽  
Tao Cao
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Ant Colony Algorithm ◽  
Global Search ◽  
Convergence Speed ◽  
Ant Colony ◽  
Local Optimum ◽  
Adaptive Potential ◽  
Avoidance Strategy

Path planning of lunar robots is the guarantee that lunar robots can complete tasks safely and accurately. Aiming at the shortest path and the least energy consumption, an adaptive potential field ant colony algorithm suitable for path planning of lunar robot is proposed to solve the problems of slow convergence speed and easy to fall into local optimum of ant colony algorithm. This algorithm combines the artificial potential field method with ant colony algorithm, introduces the inducement heuristic factor, and adjusts the state transition rule of the ant colony algorithm dynamically, so that the algorithm has higher global search ability and faster convergence speed. After getting the planned path, a dynamic obstacle avoidance strategy is designed according to the predictable and unpredictable obstacles. Especially a geometric method based on moving route is used to detect the unpredictable obstacles and realize the avoidance of dynamic obstacles. The experimental results show that the improved adaptive potential field ant colony algorithm has higher global search ability and faster convergence speed. The designed obstacle avoidance strategy can effectively judge whether there will be collision and take obstacle avoidance measures.

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