scholarly journals Dynamic Load Balancing of Software-Defined Networking Based on Genetic-Ant Colony Optimization

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 311 ◽  
Author(s):  
Hai Xue ◽  
Kyung Kim ◽  
Hee Youn
Keyword(s):  
Load Balancing ◽  
Ant Colony Optimization ◽  
Network Performance ◽  
Optimal Path ◽  
Optimal Solution ◽  
Software Defined Networking ◽  
Ant Colony ◽  
Centralized Control ◽  
Trip Time ◽  
Overall Performance

Load Balancing (LB) is one of the most important tasks required to maximize network performance, scalability and robustness. Nowadays, with the emergence of Software-Defined Networking (SDN), LB for SDN has become a very important issue. SDN decouples the control plane from the data forwarding plane to implement centralized control of the whole network. LB assigns the network traffic to the resources in such a way that no one resource is overloaded and therefore the overall performance is maximized. The Ant Colony Optimization (ACO) algorithm has been recognized to be effective for LB of SDN among several existing optimization algorithms. The convergence latency and searching optimal solution are the key criteria of ACO. In this paper, a novel dynamic LB scheme that integrates genetic algorithm (GA) with ACO for further enhancing the performance of SDN is proposed. It capitalizes the merit of fast global search of GA and efficient search of an optimal solution of ACO. Computer simulation results show that the proposed scheme substantially improves the Round Robin and ACO algorithm in terms of the rate of searching optimal path, round trip time, and packet loss rate.

scholarly journals Neural Network Optimal Routing Algorithm Based on Genetic Ant Colony in IPv6 Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Weichuan Ni ◽  
Zhiming Xu ◽  
Jiajun Zou ◽  
Zhiping Wan ◽  
Xiaolei Zhao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Performance ◽  
Ant Colony Algorithm ◽  
Routing Algorithm ◽  
Optimal Path ◽  
Optimal Solution ◽  
Ant Colony ◽  
Routing Optimization ◽  
Ipv6 Network

The traditional IPv6 routing algorithm has problems such as network congestion, excessive energy consumption of nodes, and shortening the life cycle of the network. In response to this phenomenon, we proposed a routing optimization algorithm based on genetic ant colony in IPv6 environment. The algorithm analyzes and studies the genetic algorithm and the ant colony algorithm systematically. We use neural network to build the initial model and combine the constraints of QoS routing. We effectively integrate the genetic algorithm and ant colony algorithm that maximize their respective advantages and apply them to the IPv6 network. At the same time, in order to avoid the accumulation of a lot of pheromones by the ant colony algorithm in the later stage of the network, we have introduced an anticongestion reward and punishment mechanism. By comparing the search path with the optimal path, rewards and punishments are based on whether the network path is smooth or not. Finally, it is judged whether the result meets the condition, and the optimal solution obtained is passed to the BP neural network for training; otherwise, iterative iterations are required until the optimal solution is satisfied. The experimental results show that the algorithm can effectively adapt to the IPv6 routing requirements and can effectively solve the user’s needs for network service quality, network performance, and other aspects.

An Improved and Realized Volatility Strategy of the Ant Colony Optimization Algorithm

2013 ◽  
Vol 389 ◽  
pp. 849-853
Author(s):  
Fang Song Cui ◽  
Wei Feng ◽  
Da Zhi Pan ◽  
Guo Zhong Cheng ◽  
Shuang Yang
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Optimal Solution ◽  
Ant Colony ◽  
Ant System ◽  
Ant Colony Optimization Algorithm ◽  
Volatility Model ◽  
Local Optimal Solution ◽  
The Impact ◽  
Improved Algorithm

In order to overcome the shortcomings of precocity and stagnation in ant colony optimization algorithm, an improved algorithm is presented. Considering the impact that the distance between cities on volatility coefficient, this study presents an model of adjusting volatility coefficient called Volatility Model based on ant colony optimization (ACO) and Max-Min ant system. There are simulation experiments about TSP cases in TSPLIB, the results show that the improved algorithm effectively overcomes the shortcoming of easily getting an local optimal solution, and the average solutions are superior to ACO and Max-Min ant system.

scholarly journals Mobile Robot Path Planning in an Obstacle-free Static Environment using Multiple Optimization Algorithms

2020 ◽  
Vol 17 (3) ◽  
pp. 165-173
Author(s):  
C.O. Yinka-Banjo ◽  
U. Agwogie
Keyword(s):  
Mobile Robot ◽  
Ant Colony Optimization ◽  
Optimal Path ◽  
Fruit Fly ◽  
Convergence Speed ◽  
Ant Colony ◽  
Computational Time ◽  
Robot Path Planning ◽  
Nature Inspired Algorithms ◽  
Robot Path

This article presents the implementation and comparison of fruit fly optimization (FOA), ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms in solving the mobile robot path planning problem. FOA is one of the newest nature-inspired algorithms while PSO and ACO has been in existence for a long time. PSO has been shown by other studies to have long search time while ACO have fast convergence speed. Therefore there is need to benchmark FOA performance with these older nature-inspired algorithms. The objective is to find an optimal path in an obstacle free static environment from a start point to the goal point using the aforementioned techniques. The performance of these algorithms was measured using three criteria: average path length, average computational time and average convergence speed. The results show that the fruit fly algorithm produced shorter path length (19.5128 m) with faster convergence speed (3149.217 m/secs) than the older swarm intelligence algorithms. The computational time of the algorithms were in close range, with ant colony optimization having the minimum (0.000576 secs). Keywords:  Swarm intelligence, Fruit Fly algorithm, Ant Colony Optimization, Particle Swarm Optimization, optimal path, mobile robot.

Fleet optimization considering overcapacity and load sharing restrictions using genetic algorithms and ant colony optimization

2020 ◽  
Vol 34 (1) ◽  
pp. 104-113
Author(s):  
Fredy Kristjanpoller ◽  
Kevin Michell ◽  
Werner Kristjanpoller ◽  
Adolfo Crespo
Keyword(s):  
Ant Colony Optimization ◽  
Real World ◽  
Life Cycle Cost ◽  
Optimal Solution ◽  
Load Sharing ◽  
Ant Colony ◽  
Added Value ◽  
Fleet Size ◽  
Combined Use ◽  
Computing Performance

AbstractThis paper presents a fleet model explained through a complex configuration of load sharing that considers overcapacity and is based on a life cycle cost (LCC) approach for cost-related decision-making. By analyzing the variables needed to optimize the fleet size, which must be evaluated in combination with the event space method (ESM), the solution to this problem would normally require high computing performance and long computing times. Considering this, the combined use of an integer genetic algorithm (GA) and the ant colony optimization (ACO) method was proposed in order to determine the optimal solution. In order to analyze and highlight the added value of this proposal, several empirical simulations were performed. The results showed the potential strengths of the proposal related to its flexibility and capacity in solving large problems with a near optimal solution for large fleet size and potential real-world applications. Even larger problems can be solved this way than by using the complete enumeration approach and a non-family fleet approach. Thus, this allows for a more real solution to fleet design that also considers overcapacity, availability, and an LCC approach. The simulations showed that the model can be solved in much less time compared with the base model and allows for the resolution of a fleet of at least 64 trucks using GA and 130 using ACO, respectively. Thus, the proposed framework can solve real-world problems, such as the fleet design of mining companies, by offering a more realistic approach.

An Ant-Colony-Based Meta-Heuristic Approach for Load Balancing in Cloud Computing

2021 ◽  
pp. 873-903
Author(s):  
Santanu Dam ◽  
Gopa Mandal ◽  
Kousik Dasgupta ◽  
Parmartha Dutta
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Computational Intelligence ◽  
Optimal Solution ◽  
Ant Colony ◽  
Multi Agent Systems ◽  
Distributed Environment ◽  
Intelligent Behavior ◽  
Computational Intelligence Technique ◽  
Multi Agent

This book chapter proposes use of Ant Colony Optimization (ACO), a novel computational intelligence technique for balancing loads of virtual machine in cloud computing. Computational intelligence(CI), includes study of designing bio-inspired artificial agents for finding out probable optimal solution. So the central goal of CI can be said as, basic understanding of the principal, which helps to mimic intelligent behavior from the nature for artifact systems. Basic strands of ACO is to design an intelligent multi-agent systems imputed by the collective behavior of ants. From the perspective of operation research, it's a meta-heuristic. Cloud computing is a one of the emerging technology. It's enables applications to run on virtualized resources over the distributed environment. Despite these still some problems need to be take care, which includes load balancing. The proposed algorithm tries to balance loads and optimize the response time by distributing dynamic workload in to the entire system evenly.

Mobile Robot Motion Planning to Avoid Obstacle Using Modified Ant Colony Optimization

2015 ◽  
Vol 776 ◽  
pp. 396-402 ◽  
Author(s):  
Nukman Habib ◽  
Adi Soeprijanto ◽  
Djoko Purwanto ◽  
Mauridhi Hery Purnomo
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Ant Colony Optimization ◽  
Optimal Path ◽  
Initial Position ◽  
Ant Colony ◽  
Robot Motion ◽  
Complex Environments ◽  
Point To Point ◽  
Global Planning

The ability of mobile robot to move about the environment from initial position to the goal position, without colliding the obstacles is needed. This paper presents about motion planning of mobile robot (MR) in obstacles-filled workspace using the modified Ant Colony Optimization (M-ACO) algorithm combined with the point to point (PTP) motion in achieving the static goal. Initially, MR try to plan the path to reach a goal, but since there are obstacles on the path will be passed through so nodes must be placed around the obstacles. Then MR do PTP motion through this nodes chosen by M-ACO, in order to form optimal path from the choice nodes until the last node that is free from obstacles. The proposed approach shows that MR can not only avoid collision with obstacle but also make a global planning path. The simulation result have shown that the proposed algorithm is suitable for MR motion planning in the complex environments with less running time.

Application of Ant Colony Algorithm

2014 ◽  
Vol 548-549 ◽  
pp. 1217-1220
Author(s):  
Rui Wang ◽  
Zai Tang Wang
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Ant Colony ◽  
Pheromone Concentration ◽  
Local Optimal Solution ◽  
Searching Ability ◽  
Excessive Accumulation

This paper mainly considers the application of the ant colony in our life. The principle of ant colony optimization, improves the performance of ant colony algorithm, and the global searching ability of the algorithm. We introduce a new adaptive factor in order to avoid falling into local optimal solution. With the increase the number of interations, this factor will benefit the ant search the edge with lower pheromone concentration and avoid the excessive accumulation of pheromone.

Sign in / Sign up

Export Citation Format

Share Document