scholarly journals ANT COLONY OPTIMIZATION PARAMETER SELECTION FOR SHORTEST PATH PROBLEM

2020 ◽  
Vol V-4-2020 ◽  
pp. 147-154
Author(s):  
N. Zarrinpanjeh ◽  
F. Dadrass Javan ◽  
H. Azadi ◽  
P. De Maeyer ◽  
F. Witlox
Keyword(s):  
Ant Colony Optimization ◽  
Shortest Path ◽  
Parameter Selection ◽  
Shortest Path Problem ◽  
Ant Colony ◽  
Transition Rule ◽  
High Q ◽  
Computational Performance ◽  
Selection For ◽  
Parameter Values

Abstract. The shortest path problem has been studied to be solved through diverse deterministic and also stochastic approaches such as Ant Colony Optimization. One of the most challenging issues with the implication of Ant Colony Optimization to solve the shortest path problem is parameter selection and tuning which is found crucial to improve the computational performance of problem-solving. To tune parameters, it is vital to observe the response of each parameter to different values and study their effect on the final results. In this research, two experiments are designed and conducted to study the behavior of parameters in terms of generated results and computational performance. In the first experiment, evaporation, updating, and transition rule parameters are studied by iterative execution of shortest path generation between nodes considering different parameter values. In the second experiment, the number of initial ants is studied. Inspecting the results, it is observed that to avoid premature stagnation decreasing α value is recommended. On the other hand, ρ is observed to be considered for tuning of speed and number of diffusions of the algorithm. Moreover, it is realized that a high Q value would result in more correct results. Inspecting the initial number of ants, a threshold is realized where increasing the number of ants over this threshold would drastically result in more optimized paths.

Shortest Path Problem Solving Based on Ant Colony Optimization Metaheuristic

2012 ◽  
Vol 17 (1-2) ◽  
pp. 7-17 ◽  
Author(s):  
Mariusz Gła̢bowski ◽  
Bartosz Musznicki ◽  
Przemysław Nowak ◽  
Piotr Zwierzykowski
Keyword(s):  
Ant Colony Optimization ◽  
Shortest Path ◽  
Shortest Path Problem ◽  
Combinatorial Problems ◽  
Ant Colony ◽  
Optimization Approach ◽  
Algorithmic Optimization ◽  
Network Utilization ◽  
Shortest Route ◽  
Hard Problems

Abstract The Ant Colony Optimization (ACO) metaheuristic is a versatile algorithmic optimization approach based on the observation of the behaviour of ants. As a result of numerous analyses, ACO has been applied to solving various combinatorial problems. The ant colony metaheuristic proves itself to be efficient in solving NP-hard problems, often generating the best solution in the shortest amount of time. However, not enough attention has been paid to ACO as a means of solving problems that have optimal solutions which can be found using other methods. The shortest path problem is undoubtedly one of the aspects of great significance to navigation and telecommunications. It is used, amongst others, for determining the shortest route between two geographical locations, for routing in packet networks, and to balance and optimize network utilization. Thus, this article introduces ShortestPathACO, an Ant Colony Optimization based algorithm designed to find the shortest path in a graph. The algorithm consists of several subproblems that are presented successively. Each subproblem is discussed from many points of view to enable researchers to find the most suitable solutions to the problems they investigate.

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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