Ant colony optimization (ACO) algorithms are a recently developed, population-based approach which has been successfully applied to optimization problems. However, in the ACO algorithms it is difficult to adjust the balance between intensification and diversification and thus the performance is not always very well. In this work, we propose an improved ACO algorithm in which some of ants can ev...

This paper describes GRAF-ANT (Graphical Framework for Ant Colony Optimization), an objectoriented C# framework for developing ant colony systems that we have developed. While developing this framework, abstractions that are necessary for ant colony optimization algorithms were analyzed, as well as the features that their implementing classes should have. During creation of these classes, sever...

ant colony optimisation (aco) algorithm and adaptive refinement mechanism are used in this paper for solution of optimization problems. many of the real engineering problems are، however، of continuous nature and finding their solution by discrete ant based algorithms requires discretisation of the decision variables in which affected the convergence and performance of the algorithm. in this pa...

A distinctive optimization technique known as Ant Colony Optimization (ACO) has gained huge popularity in these recent years due to its flexibility and the ability to avoid reaching local optima. This optimization approach has become a candidate approach for many optimization problems. Unfortunately, this attractive algorithm suffers several downsides including stagnation and slow convergence t...

The first ant colony optimization (ACO) called ant system was inspired through studying of the behavior of ants in 1991 by Macro Dorigo and co-workers [1]. An ant colony is highly organized, in which one interacting with others through pheromone in perfect harmony. Optimization problems can be solved through simulating ant’s behaviors. Since the first ant system algorithm was proposed, there is...

In this paper we describe the application of an Ant Colony Optimization (ACO) algorithm to optimize the parameters in the design of PI controller and to find the best optimal intelligent controller. The ACO algorithm is a bio-inspired optimization method that has proven its success through various combinatorial optimization problems. The parameters of the PI Controller are evaluated by an ant c...

The purpose of this paper is to discuss the addition of a new operator, called an ACO operator, to a genetic algorithm. The operator is based on an analogy with Ant Colony Optimization. We use the ACO operator in an application of genetic algorithms to engineering design of conduit systems. The conduit optimization problem involves optimizing both the location of components of conduit systems a...

Genetic Algorithm (GA), Ant Colony Optimization (ACO) algorithm and Particle Swarm Optimization (PSO) are proposed for feature selection, and their performance is compared. The Spatial Gray Level Dependence Method (SGLDM) is used for feature extraction. The selected features are fed to a three-layer Backpropagation Network hybrid with Ant Colony Optimization and Particle Swarm Optimization (BPN...

Ant colony optimization (ACO) is a novel and promising meta-heuristic for solving hard combinatorial optimization problems, such as travelling salesman[3] [4] [5] [6] [7] [8], quadratic assignment[9] [10] [11] [12] and set covering problem [13]. Unfortunately, according to systems performance evaluation literature, the methods adopted to experimentally validate the current ACO are not enough ac...

Ant Colony Optimization (ACO) algorithm is a novel metaheuristic algorithm that has been widely used for different combinational optimization problem and inspired by the foraging behavior of real ant colonies. Ant Colony Optimization has strong robustness and easy to combine with other methods in optimization. In this paper, an efficient ant colony optimization algorithm with uniform mutation o...