These algorithms like ant colony
optimization have been applied to feature selection as no solid heuristic exist to find optimal feature subset, so it is expected that the ants discover good feature combinations as they proceed through the search space.
With the objective of solving optimization problems, several bio inspired algorithms have been proposed in the literature, which include genetic algorithms (GA) and swarm algorithms such as particle swam optimization (PSO), ant colony
optimization (ACO), Clonal Selection Algorithm (CSA), Bat Algorithm and Artificial Bee Colony.
Opinion analysis using ant colony
Design and Optimization of Ant Colony
Algorithm for Graph Coloring
Concept of Fusing a Genetic Algorithm and Ant Colony
Rest of the paper is organized as follows: Section 2 provides the literature survey and related works with various classifications of Ant colony
based routing algorithms.
This program sets pedestrian routing diversions in VISSIM according to the work zone schedules derived from ant colony
The article is presented as following: The second section is the review of literature, third section introduces the fuzzy system construct and fourth section explains the rules for improving the fuzzy rules using the Ant Colony
The basic ant colony
algorithm (ACA) model is a population-based simulates evolutionary algorithm, winch is inspired by process of ant colony
search for food, it not only has the characteristics of positive feedback, distributed computing and heuristic search, but also is a essentially parallel algorithm and high robust.
The results demonstrate that there is high correlation between reduction of foraging activities and reduction on total activities by the all tested homeopathic preparations, suggesting that the homeopathic preparation acts on the behaviour of the entire ant colony
This field of study is known as "swarm intelligence" and has attracted an increasingly number of researchers since the proposal of Particle Swarm Optimization (PSO) algorithm and also of the Ant Colony
Optimization (ACO) Algorithm (DORIGO et al.
The ant colony
optimization (ACO) algorithm proposed by Dorigo and Stutzle  is a population-based heuristic bionics evolutionary algorithm.
A simulation model was developed that projects the long-term dynamics of three important perennial grass species before, during and after the establishment of a red harvester ant colony
7] have suggested an ACO approach in which an ant colony
system for traveling salesman problem and the results were compared and shown to be better than other heuristic approaches like genetic algorithm, evolutionary programming, simulated annealing, and the annealing genetic algorithm, which is a combination of genetic algorithm and simulated annealing.
The most popular algorithms are genetic algorithms and ant colony
algorithm are used to solve NP-Hard Problems [1-4].