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

A Novel Combinatorial Ant Colony Optimization Algorithm with Detection Zone Rule

2014 ◽  
Vol 568-570 ◽  
pp. 1511-1515 ◽  
Author(s):  
Ya Nan Bai ◽  
Yong Chang Shi ◽  
Xiao Yu Shi
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony ◽  
Ant Colony System ◽  
Local Optimum ◽  
Ant System ◽  
Ant Colony Optimization Algorithm ◽  
Detection Zone ◽  
Global Rate ◽  
Artificial Ants

For solving the Ant Colony System own inherent defects, this paper proposes a novel combinatorial ant Colony Optimization algorithm with detection zone rule. In the proposed algorithm, the pheromone and the search path are modified dynamically. By using the detection method, the artificial ants are detected automatically per m iterations during the detection zone. When the ant colony falls into the local optimum, the variable will be adaptive modified by the algorithm. Meanwhile, for improving the search abilities of artificial ants, it changes the global rate of pheromone evaporation and the maximum and minimum of pheromone, respectively. The performance of the novel algorithm is conducted, and the comparison among the original Ant System (AS), Ant Colony System (ACS) and proposed algorithm is shown. The experiment result demonstrated that the CACOD has a better performance than ACS in term of the capability of search and ability of restrain stagnation.

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.

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