scholarly journals Running time analysis of Ant Colony Optimization for shortest path problems

2012 ◽  
Vol 10 ◽  
pp. 165-180 ◽  
Author(s):  
Dirk Sudholt ◽  
Christian Thyssen
Keyword(s):  
Ant Colony Optimization ◽  
Shortest Path ◽  
Ant Colony ◽  
Time Analysis ◽  
Running Time ◽  
Shortest Path Problems ◽  
Running Time Analysis

scholarly journals A novel ant colony optimization algorithm for the shortest-path problem in traffic networks

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1619-1628 ◽  
Author(s):  
Shuijian Zhang ◽  
Xuejun Liu ◽  
Meizhen Wang
Keyword(s):  
Combinatorial Optimization ◽  
Ant Colony Optimization ◽  
Shortest Path ◽  
Shortest Path Problem ◽  
Ant Colony ◽  
Single Pair ◽  
Traffic Networks ◽  
Shortest Path Problems ◽  
Combinatorial Optimization Problems ◽  
Pheromone Trail

The Ant Colony Optimization (ACO) algorithm is a metaheuristic nature-inspired technique for solving various combinatorial optimization problems. The shortest-path problem is an important combinatorial optimization problem in network optimization. In this paper, a novel algorithm based on ACO to solve the single-pair shortest-path problem in traffic networks is introduced. In this algorithm, a new strategy is developed to find the best solution in a local search, by which the ants seek the shortest path using both a pheromone-trail-following mechanism and an orientation-guidance mechanism. A new method is designed to update the pheromone trail. To demonstrate the good performance of the algorithm, an experiment is conducted on a traffic network. The experimental results show that the proposed algorithm produces good-quality solutions and has high efficiency in finding the shortest path between two nodes; it proves to be a vast improvement in solving shortest-path problems in traffic networks. The algorithm can be used for vehicle navigation in intelligent transportation systems.

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.

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