A collective approach to resolve the segmentation problem was proposed. AntClust is a new ant-based algorithm that uses the self-organizing and autonomous brood sorting behavior observed in real ants. Ants and pixels are scatted on a discrete array of cells represented the ants’ environment. Using simple local rules and without any central control, ants form homogeneous clusters by moving pixel...

Ants algorithm is an evolutionary search method for solving combinatorial optimization problems. In this note we propose a version of the algorithm for coloring a graph with a fixed number of colors. Based on approximate colorings obtained by single ants, edges of maximal evidence are added so that the new ants generation does only attempt to construct colorings with additional restrictions. Pr...

Ant Colony Optimization (ACO) algorithms often suffer from criticism for the local optimum and premature convergence. In order to overcome these inherent shortcomings shared by most ACO algorithms, we divide the ordinary ants into two types: the utilizationoriented ants and the exploration-oriented ants. The utilization-oriented ants focus on constructing solutions based on the learned experien...

Ant Colony Optimization (ACO) is nature inspired algorithm based on foraging behavior of ants. The algorithm is based on the fact how ants deposit pheromone while searching for food. ACO generates a pheromone matrix which gives the edge information present at each pixel position of image, formed by ants dispatched on image. The movement of ants depends on local variance of image’s intensity val...

Swarm Intelligent (SI) algorithms draw their inspiration from the interaction of individuals of social organisms. One such algorithm, Ant Colony Optimization (ACO) [1], utilizes the foraging behavior of ants to solve combinatorial optimization problems. Although ACO performs well in a static environment, it has been pointed out that ACO does not perform as well as other heuristics in dynamic si...

We model and analyze the distributed task allocation problem, which is solved by ant colonies on a daily basis. Ant colonies employ task allocation in which ants are moved from one task to the other in order to meet changing demands introduced by the environment, such as excess or shortage of food, dirtier or cleaner nest, etc. The different tasks are: nursing (overseeing the hatching of newbie...

Wireless sensor networks monitor physical or environmental conditions. One of the key objectives during their deployment is full coverage of the monitoring region with a minimal number of sensors and minimised energy consumption of the network. This problem is hard, from the computational point of view. Thus, the most appropriate approach to solve it is application of some metaheuristics. In th...

Ant Colony Optimization (ACO) has been applied successfully in solving the Traveling Salesman Problem. Marco Dorigo et al. used Ant System (AS) to explore the Symmetric Traveling Salesman Problem and found that the use of a small number of elitist ants can improve algorithm performance. The elitist ants take advantage of global knowledge of the best tour found to date and reinforce this tour wi...

Ant colony algorithm suffers drawbacks such as slow convergence and easy to trap into local optimum, therefore the path planning for mobile robot based on an improved ant colony optimization algorithm is proposed. The workspace for mobile robot is established with grid method. A hybrid ant colony which is composed of common ants and exploratory ants is utilized to avoid trapping into local opti...

Marco Dorigo et al. used Ant System (AS) to explore the Symmetric Traveling Salesman Problem and found that the use of a small number of elitist ants can improve algorithm performance. The elitist ants take advantage of global knowledge of the best tour found to date and reinforce this tour with pheromone in order to focus future searches more effectively. This paper discusses an alternative ap...