Shortest Path Problem Solving Based on Ant Colony Optimization Metaheuristic

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.

Empirical Approach in Topology Control of Sensor Networks for Urban Environment

“…Network topology is typically understood to be a model that describes the structure of connections between the elements within a given network [22] and is frequently presented in the form of a graph G = (V , E ), where V denotes the set of vertices (in other words nodes, such as sensors), whereas E is the set of edges (i.e. connections) between vertices [23]. This notation may refer to both physical relations (relative arrangement of nodes and connections between them, directly stemming from the properties of the transmission medium applied) and logical relations (an operational configuration based method for transmitting data via the network from the starting point to the end point, between the elements of the network's infrastructure [24].…”

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

“…During the search, each ant constructs its solution by repeatedly using the transition probability rule to select the nodes to move to until reaching the destination. The transition probability is obtained by formula (8). The artificial ants tend to choose the nodes that are connected by an edge with many pheromones and fast travel speed, and whose orientation inclines to the destination.…”

“…Various ACO algorithms have been used for shortest-path problems. Glabowski et al introduced an AS called the ShortestPathACO algorithm based on ACO for solving the Shortest-Path problem [8]. In that study, several problems about using ACO to find the shortest path were discussed.…”

Energy-Efficient Lightpath Establishment in Backbone Optical Networks Based on Ant Colony Optimization

“…Evaporation takes place on paths that are less traversed. Usually, the path with the highest pheromone concentration (not always the case -that's why this is a heuristic method) is the shortest path [16] (or carries another important property), due to the less amount of pheromone that is able to evaporate because ants deposit to it more frequently. The AS emulates this nature's behavior with satisfying results.…”