The Ant Colony Optimization (ACO) algorithms have been well‐studied by the Operations
Research community for solving combinatorial optimization problems. A handful of …

Maintaining accuracy in load balancing using metaheuristics is a difficult task even with the
help of recent hybrid approaches. In the existing literature, various optimized metaheuristic …

Classification is a data mining technique which is utilized to predict the future by using
available data and aims to discover hidden relationships between variables and classes …

Ant colony optimization (ACO) has been successfully applied to classification, where the aim
is to build a model that captures the relationships between the input attributes and the target …

Ant colony optimization (ACO) algorithms have been successfully applied in data
classification, which aim at discovering a list of classification rules. However, due to the …

Pheromones are chemical substances essential for communication among social insects. In
the application of swarm intelligence to real micro mobile robots, the deployment of a single …

A re-occurring challenge in applying feed-forward neural networks to a new dataset is the
need to manually tune the neural network topology. If one's attention is restricted to fully …

The last years, UAVs have been developed to address a variety of applications ranging from
searching and tracking to the surveillance of an area. However, using a single UAV limits the …

ACO R is a well-established ant colony optimization algorithm for continuous-domain
optimization. We present an approach for the dynamic adaptation of the ACOR algorithm's …

Classification rule discovery using ant colony optimization (ACO) imitates the foraging
behavior of real ant colonies. It is considered as one of the successful swarm intelligence …