scholarly journals Determination of the Shortest Route on the Distribution System using Ant Colony Optimization (ACO) Algorithm (Case Study: Alfamidi Palu Branch – PT. Midi Utama Indonesia)

2021 ◽  
Vol 1 (2) ◽  
pp. 84-93
Author(s):  
Nabila Dwi Indria ◽  
Junaidi Junaidi ◽  
Iut Tri Utami
Keyword(s):  
Ant Colony Optimization ◽  
Distribution System ◽  
Ant Colony ◽  
Ant Colony System ◽  
Ant Colonies ◽  
Status Transition ◽  
Shortest Route ◽  
Transition Rules

The distribution system of goods is one of the most important parts for every company. The company certainly has many route options to visit, and this is expected to be conducted efficiently in terms of time. In the distribution of goods by Alfamidi company in Palu City which has 51 outlets include into the category of Traveling Salesman Problem (TSP) because of many route options that can be visited. The problem can be solved by employing the Ant Colony Optimization (ACO) method which is one of the algorithms Ant Colony System (ACS). The ACS acquires principles based on the behavior of ant colonies and applies three characteristics to determine the shortest route namely status transition rules, local pheromone renewal and global pheromones. The result showed that the shortest route of the distribution of goods based on the calculation of selected iterations was ant 1 with the shortest total distance obtained 86.98 km.

Ant Colony Optimization with Local Search for Continuous Functions

2011 ◽  
Vol 204-210 ◽  
pp. 1135-1138
Author(s):  
Cheng Ming Qi
Keyword(s):  
Local Search ◽  
Ant Colony Optimization ◽  
Population Based ◽  
Continuous Functions ◽  
Ant Colony ◽  
Benchmark Problems ◽  
Ant Colony System ◽  
Local Optimum ◽  
Ant Colonies ◽  
Ant Algorithms

Ant algorithms are a recently developed, population-based approach which was inspired by the observation of the behavior of ant colonies. Based on the ant colony optimization idea, we present a hybrid ant colony system (ACS) coupled with a pareto local search (PLS) algorithm, named PACS, and apply to the continuous functions optimization. The ACS makes firstly variable range into grid. In local search, we use the PLS to escape local optimum. Computational results for some benchmark problems demonstrate that the proposed approach has the high search superior solution ability.

scholarly journals DESIGNING APPLICATION OF ANT COLONY SYSTEM ALGORITHM FOR THE SHORTEST ROUTE OF BANDA ACEH CITY AND ACEH BESAR REGENCY TOURISM BY USING GRAPHICAL USER INTERFACE MATLAB

2017 ◽  
Vol 17 (2) ◽  
pp. 83
Author(s):  
Durisman Durisman ◽  
Marwan Ramli ◽  
Siti Rusdiana
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Minimum Distance ◽  
Ant Colony ◽  
Ant Colony System ◽  
Ant Colony Optimization Algorithm ◽  
Shortest Route ◽  
Manual Calculation ◽  
Test Result ◽  
Banda Aceh

Banda Aceh city and Aceh Besar Regency are two of the leading tourism areas located in the province of Aceh. For travelling, there are some important things to be considered, such as determining schedule and distance of tourism. Every tourist certainly chooses the shortest route to reach the destination since it can save time, energy, and money. The purpose of this reserach is to develop a method that can be used in calculating the shortest route and applied to the tourism of Banda Aceh city and Aceh Besar regency. In this reserach, Ant Colony Optimization algorithm is used to determine the shortest route to tourism of Banda Aceh city and Aceh Besar regency. From the analysis made by using both manual calculation and  GUI MATLAB program application test, the shortest route can be obtained with a minimum distance of 120.85 km in one travel. Based on the test result, the application for tourism (in Banda Aceh city and Aceh Besar regency) shortest route searching built by utilizing the Ant Colony Optimization algorithm can find optimal route. Keyword: tourism, the shortest route, Ant Colony Optimization

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

The Map-Based Shortest Route Selection by Using Ant Colony Optimization Algorithm

2018 ◽  
Vol 1 (1) ◽  
pp. 15
Author(s):  
Achmad Fanany Onnilita Gaffar ◽  
Agusma Wajiansyah ◽  
Supriadi Supriadi
Keyword(s):  
Ant Colony Optimization ◽  
Shortest Path ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Google Earth ◽  
Ant Colony ◽  
Food Sources ◽  
Shortest Route ◽  
Study Results ◽  
Destination Point

The shortest path problem is one of the optimization problems where the optimization value is a distance. In general, solving the problem of the shortest route search can be done using two methods, namely conventional methods and heuristic methods. The Ant Colony Optimization (ACO) is the one of the optimization algorithm based on heuristic method. ACO is adopted from the behavior of ant colonies which naturally able to find the shortest route on the way from the nest to the food sources. In this study, ACO is used to determine the shortest route from Bumi Senyiur Hotel (origin point) to East Kalimantan Governor's Office (destination point). The selection of the origin and destination points is based on a large number of possible major roads connecting the two points. The data source used is the base map of Samarinda City which is cropped on certain coordinates by using Google Earth app which covers the origin and destination points selected. The data pre-processing is performed on the base map image of the acquisition results to obtain its numerical data. ACO is implemented on the data to obtain the shortest path from the origin and destination point that has been determined. From the study results obtained that the number of ants that have been used has an effect on the increase of possible solutions to optimal. The number of tours effect on the number of pheromones that are left on each edge passed ant. With the global pheromone update on each tour then there is a possibility that the path that has passed the ant will run out of pheromone at the end of the tour. This causes the possibility of inconsistent results when using the number of ants smaller than the number of tours.

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.

An Improved Ant Colony System Based on Negative Biased

2010 ◽  
Vol 439-440 ◽  
pp. 558-562
Author(s):  
Jin Qiu Yang ◽  
Jian Gang Yang ◽  
Gen Lang Chen
Keyword(s):  
Convergence Rate ◽  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Ant Colony System ◽  
Ant System

Ant System (AS) was the first Ant Colony Optimization (ACO) algorithm, which converged too slowly and consumed huge computation. Among the variants of AS, Ant Colony System (ACS) was one of the most successful algorithms. But ACS converged so rapidly that it always was in early stagnation. An improved Ant Colony System based on Negative Biased (NBACS) was introduced in the paper to overcome the early stagnation of the ACS. Experiments for Traveling Salesman Problem (TSP) showed that better solutions were obtained at the same time when the convergence rate accelerated more rapidly.

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

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