Abstract
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.
Highlights
The shortest path problem is briefly defined as finding a path between two vertices in a graph such that the sum of the weights of its constituent edges is minimized
The first one is almost a toolbox for Ant Colony Optimization (ACO), configured to solve the shortest path problem. This toolbox enjoys the capability of performing preprocessing and blending configuration of different variants of ACO to generate new modifications
As it is observable in figure 2, regardless of changes in the start and destination nodes, the results are vividly similar to each other as the length of the constructed path is normalized to the length of the best solution (Detour Ratio)
Summary
The shortest path problem is briefly defined as finding a path between two vertices (or nodes) in a graph such that the sum of the weights of its constituent edges is minimized. Many different solutions for this problem have been introduced such as Dijkstra's algorithm (Dijkstra, 1959), Bellman-Ford algorithm (Bellman, 1958), A* search algorithm (Zeng, Church, 2009), Floyd–Warshall algorithm (Floyd, 1962), Johnson's algorithm (Johnson, 1977) to provide the most optimized solutions with minimum computational costs Along with these deterministic solutions, meta-heuristic algorithms such as Genetic algorithms (Mitchell, 1998) and especially Ant Colony Optimization (ACO) algorithms (Dorigo, 1992) are utilized to propose a hand in providing innovative solutions for shortest path problem through stochastically defined approaches. Through the iterative deployment of ants, the optimized path, and as a matter of fact, the optimized solution is selected
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