Ant colony optimization (ACO) is a metaheuristic for solving combinatorial optimization problems that is based on the foraging behavior of biological ant colonies. Starting with the 1996 seminal paper by Dorigo, Maniezzo and Colorni, ACO techniques have been used to solve the traveling salesperson problem (TSP). In this paper, we focus on a particular type of the ACO algorithm, namely, the rank...

In this paper, we present a dynamic ant colony optimisation (ACO) algorithm to solve dynamic traffic routing problem. The main objective of this work is to search out the least-time-cost route in a variable-edge-weight graph. We introduce time-dependent pheromones and electric-field model as two heuristic factors to improve the basic ACO. The simulation results show that the proposed dynamic AC...

Abs t rac t . In this paper we introduce an Ant Colony Optimisation (ACO) algorithm for the Shortest Common Supersequence (SCS) problem, which has applications in production system planning, mechanical engineering and molecular biology. The ACO algorithm is used to find good parameters for a heuristic for the SCS problem. An island model with several populations of ants is used for the ACO algo...

Ant colony optimization (ACO) has been proposed as a promising tool for adaptive routing in telecommunications networks. The algorithm is applied here to a simulation of a satellite telecommunications network with 72 LEO nodes and 121 earth stations. Three variants of ACO are tested in order to assess the relative importance of the different components of the algorithm. The best ACO variant con...

Time constraint is the main factor in real time operating system and it affects the deadline of the process. To achieve deadline, proper scheduling algorithm is required to schedule the task. In this paper an Adaptive scheduling algorithm is developed which is the combination of Earliest Deadline First (EDF) and Ant Colony Optimization (ACO). The EDF algorithm places the process in a priority q...

The Bin Packing Problem and the Cutting Stock Problem are two related classes of NP-hard combinatorial optimisation problems. Exact solution methods can only be used for very small instances, so for real-world problems we have to rely on heuristic methods. In recent years, researchers have started to apply evolutionary approaches to these problems, including Genetic Algorithms and Evolutionary ...

Due to the fact that Swarm Systems algorithms have been determined to be efficient in solving discrete optimization problems with proven applicability into practical world, the computer scientists are continuously and increasingly discovering swarm-inspired algorithms or improving existing ones. The scope of this paper is to present such an improvement brought to the Elitist Ant System (EAS) al...

Feature selection is an important task for data analysis and information retrieval processing, pattern classification systems, and data mining applications. It reduces the number of features by removing noisy, irrelevant and redundant data. In this paper, a novel feature selection algorithm based on Ant Colony Optimization (ACO), called Advanced Binary ACO (ABACO), is presented. Features are tr...

This paper is dedicated to post disaster road network verification and routing using High Resolution Satellite Imagery (HRSI) and Ant Colony Optimization (ACO) algorithms. By determination of damage degree to each road element using satellite information, a modified ACO algorithm is designed and applied to find best routes with respect to each road’s length and damage degree. The mentioned algo...

Ant colony optimization (ACO) algorithms are population-based algorithms where ants communicate via their pheromone trails. Usually, this indirect communication leads the algorithm to a stagnation behaviour, where the ants follow the same path from early stages. This is because high levels of pheromone are generated into a single trail, where all the ants are influenced and follow it. As a resu...