Applying parameter adaptation means operating on parameters of an algorithm while it is tackling an instance. For ant colony optimization, several parameter adaptation methods have...
—Ant colony optimization (ACO) is a probabilistic technique used for solving complex computational problems, such as finding optimal routes in networks. It has been proved to pe...
Image feature selection (FS) is an important task which can affect the performance of image classification and recognition. In this paper, we present a feature selection algorithm ...
We present a novel probabilistic approach to fully automated delineation of tree structures in noisy 2D images and 3D image stacks. Unlike earlier methods that rely mostly on local...
Abstract--Ant Colony Optimization (ACO) is a problemsolving technique that was inspired by the related research on the behavior of real-world ant colony. In the domain of Network-o...
Recently we have setup the goal of investigating new truly distributed forms of Ant Colony Optimization. We proposed a new distributed approach for Ant Colony Optimization (ACO) al...
This paper introduces a Combinatory Optimization Problem (COP) which captures the performance in cooperation of a P2P Streaming Network, considered at the buffer level. A new famil...
This work describes a distributed framework for routing path optimization in Optical Burst-Switched (OBS) networks that loosely mimics the foraging behaviour of ants observed in na...
In this paper we present a novel algorithm, named GBAP, that jointly uses automatic programming with ant colony optimization for mining classification rules. GBAP is based on a con...
Ant colony optimization (ACO) has been widely used for different combinatorial optimization problems. In this paper, we investigate ACO algorithms with respect to their runtime beh...