Ant Colony Optimization (ACO) is a paradigm for designing metaheuristic algorithms for combinatorial optimization problems. The first algorithm which can be classified within this framework was presented in 1991 [21, 13] and, since then, many diverse variants of the basic principle have been reported in the literature. The essential trait of ACO algorithms is the combination of a priori informa...

The Ant Colony Optimization (ACO) is a metaheuristic inspired by the behavior of real ants in their search for the shortest paths to food sources. It has recently attracted a lot of attention and has been successfully applied to a number of different optimization problems. Due to the importance of the feature selection problem and the potential of ACO, this paper presents a novel method that ut...

proposed to improve the performance of the ACO algorithm. In this paper an ant colony optimization with memory is proposed, which is applied to the classical traveling salesman problem (TSP). In the proposed algorithm, each ant searches the solution not only according to the pheromone and heuristic information but also based on the memory which is from the solution of the last iteration. A larg...

This paper develops the formal framework of ant programming with the goal of gaining a deeper understanding on ant colony optimization and, more in general, on the principles underlying the use of an iterated Monte Carlo approach for the multi-stage solution of combinatorial optimization problems. Ant programming searches for the optimal policy of a multi-stage decision problem to which the ori...

Middle spatial resolution multi-spectral remote sensing image is a kind of color image with low contrast, fuzzy boundaries and informative features. In view of these features, the fuzzy C-means clustering algorithm is an ideal choice for image segmentation. However, fuzzy C-means clustering algorithm requires a pre-specified number of clusters and costs large computation time, which is easy to ...

Economic dispatch problem is an optimization problem where objective function is highly non linear, non-convex, non-differentiable and may have multiple local minima. Therefore, classical optimization methods may not converge or get trapped to any local minima. This paper presents a comparative study of four different evolutionary algorithms i.e. genetic algorithm, bacteria foraging optimizatio...