scholarly journals Packets routing and bandwidth sensing in a network traffic: Ant colony optimization tactic.

2018 ◽  
Vol 24 (2) ◽  
pp. 223-228
Author(s):  
Felix U. Ogban ◽  
Roy Nentui
Keyword(s):  
Ant Colony Optimization ◽  
Network Traffic ◽  
Priority Queue ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Ant Colonies ◽  
Lower Priority ◽  
Link State ◽  
Discrete Problems ◽  
Network Platform

Packets routing and bandwidth sensing in a network platform remains an integral part of the study of signal flow.The algorithm to route packets in a network link called the AntNet algorithm was inspired by the behavior of real ant colonies. At each node in the network, a forward ant deposits some amount of pheromones at different links that responds to the node’s queue length. In this paper, we propose the inclusion of the computation of paths to adapt with the Depth Search Ant Explorer Network (DS-ANTENet) algorithm for discrete problems as an IP based mechanism. This method is tested and the efficiency is compared to the original AntNet algorithm and the Link-State algorithm to check the transmission of computing traffic flows between the nodes. We then made comparison with the algorithms proposed in the literature. The protocols were sorted out in terms of average number of lost packets ranging from the higher priority queue to the lower priority queue which then resulted to the fact that; First, AntNetBW (loss ratios reduction of 9.6% when compared to the AntNet and the Link-State algorithm respectively. Secondly,  SANTENetBW (loss ratios reduction of 8.3% and 36.7% when compared to the AntNet and the Link-State algorithm respectively. Finally, DS-ANTENet (loss ratios reduction 0.7% and 33.2% when compared to the AntNet and the Link- State algorithm respectively.Keywords: Packets Routing, Bandwidth Sensing, Network Traffic, Ant Colony Optimization Algorithm, AntNet

Omicron ACO. A New Ant Colony Optimization Algorithm

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Benjamın Baran ◽  
Osvaldo Gomez
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Problems ◽  
Population Based ◽  
Analytical Tool ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Ant Colonies ◽  
Combinatorial Optimization Problems ◽  
The Traveling Salesman Problem

Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ant colonies that has been successful in the resolution of hard combinatorial optimization problems like the Traveling Salesman Problem (TSP). This paper proposes the Omicron ACO (OA), a novel population-based ACO alternative originally designed as an analytical tool. To experimentally prove OA advantages, this work compares the behavior between the OA and the MMAS as a function of time in two well-known TSP problems. A simple study of the behavior of OA as a function of its parameters shows its robustness.

scholarly journals Pencarian Jalur Terpendek Menggunakan Metode Algoritma “Ant Colony Optimization” Pada GUI Matlab (Studi Kasus: PT Distriversa Buana Mas cabang Purwokerto)

2019 ◽  
Vol 8 (2) ◽  
pp. 272-284
Author(s):  
Via Risqiyanti ◽  
Hasbi Yasin ◽  
Rukun Santoso
Keyword(s):  
Ant Colony Optimization ◽  
Selection Process ◽  
Travelling Salesman Problem ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Ant Colonies ◽  
Indonesian Archipelago ◽  
Shortest Route ◽  
Distribution Company ◽  
Speed Up

For company, shortest distribution route is an important thing to be developed in order to obtain effectiveness in the distribution of products to consumers. One way of development is to find the shortest route with Ant Colony Optimization algorithm. This algorithm is inspired by the behavior of ant colonies that can find the shortest path from the nest to the food source. One example of a distribution company is PT Distriversa Buana Mas, also known as DBM. DBM is a physical distribution company covering the entire Indonesian archipelago specialized in the distribution of pharmaceuticals and consumer goods such as personal care, cosmetic and food products. DBM uses land transportation in 18 brances spread across Indonesia. One branch of DBM is in the Purwokerto region that distributes products to 29 stores in the Purbalingga region. This research is done with the help of GUI as a computation tool. Based on test results, the GUI system that has been built able to simplify and speed up the selection process of finding the shortest route for distribute product of DBM in the Purbalingga region. Keywords: Travelling Salesman Problem, Distriversa Buana Mas, Algorithm, Ant Colony Optimization, GUI

Modeling and optimizing logistics, information, economic and financial intercluster cooperation using the ant colony optimization algorithm

2020 ◽  
Vol 26 (11) ◽  
pp. 2427-2447
Author(s):  
S.N. Yashin ◽  
E.V. Koshelev ◽  
S.A. Borisov
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Population Decline ◽  
Innovation System ◽  
Federal District ◽  
Original Model ◽  
Ant Colony ◽  
Industrial Agglomeration ◽  
Ant Colony Optimization Algorithm

Subject. This article discusses the issues related to the creation of a technology of modeling and optimization of economic, financial, information, and logistics cluster-cluster cooperation within a federal district. Objectives. The article aims to propose a model for determining the optimal center of industrial agglomeration for innovation and industry clusters located in a federal district. Methods. For the study, we used the ant colony optimization algorithm. Results. The article proposes an original model of cluster-cluster cooperation, showing the best version of industrial agglomeration, the cities of Samara, Ulyanovsk, and Dimitrovgrad, for the Volga Federal District as a case study. Conclusions. If the industrial agglomeration center is located in these three cities, the cutting of the overall transportation costs and natural population decline in the Volga Federal District will make it possible to qualitatively improve the foresight of evolution of the large innovation system of the district under study.

Penerapan Algoritma ACO untuk Penjadwalan Kuliah Pengganti pada Perguruan Tinggi (Studi Kasus: Program Studi Informatika, Universitas Multimedia Nusantara)

2019 ◽  
Vol 9 (2) ◽  
pp. 79-85
Author(s):  
Indah Noviasari ◽  
Andre Rusli ◽  
Seng Hansun
Keyword(s):  
Ant Colony Optimization ◽  
Educational Institution ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Ant Colony ◽  
Scheduling Problems ◽  
Perceived Ease Of Use ◽  
Ant Colony Optimization Algorithm ◽  
Scheduling System ◽  
Course Scheduling

Students and scheduling are both essential parts in a higher educational institution. However, after schedules are arranged and students has agreed to them, there are some occasions that can occur beyond the control of the university or lecturer which require the courses to be cancelled and arranged for replacement course schedules. At Universitas Multimedia Nusantara, an agreement between lecturers and students manually every time to establish a replacement course. The agreement consists of a replacement date and time that will be registered to the division of BAAK UMN which then enter the new schedule to the system. In this study, Ant Colony Optimization algorithm is implemented for scheduling replacement courses to make it easier and less time consuming. The Ant Colony Optimization (ACO) algorithm is chosen because it is proven to be effective when implemented to many scheduling problems. Result shows that ACO could enhance the scheduling system in Universitas Multimedia Nusantara, which specifically tested on the Department of Informatics replacement course scheduling system. Furthermore, the newly built system has also been tested by several lecturers of Informatics UMN with a good level of perceived usefulness and perceived ease of use. Keywords—scheduling system, replacement course, Universitas Multimedia Nusantara, Ant Colony Optimization

Application of Improved Multi-Objective Ant Colony Optimization Algorithm in Ship Weather Routing

2021 ◽  
Vol 20 (1) ◽  
pp. 45-55
Author(s):  
Guangyu Zhang ◽  
Hongbo Wang ◽  
Wei Zhao ◽  
Zhiying Guan ◽  
Pengfei Li
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Multi Objective ◽  
Weather Routing

scholarly journals Fast update method of new equipment status in IMS network based on ant colony optimization algorithm

2021 ◽  
Vol 1948 (1) ◽  
pp. 012049
Author(s):  
Lili Sun ◽  
Hongting Zhai ◽  
Qi Zhai ◽  
Liang Li ◽  
Qingrui Zhang
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm
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