Swarm intelligence is a relatively novel approach to problem solving that takes inspiration from the social behaviors of insects and of other animals. In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful one is the ant colony optimization. Ant colony optimization (ACO) algorithm, a novel population-based and meta-heuristic app...

Recently, researchers in various fields have shown interest in the behavior of creatures from the viewpoint of adaptiveness and flexibility. Ants, known as social insects, exhibit collective behavior in performing tasks that can not be carried out by an individual ant. In ant colonies, chemical substances, called pheromones, are used as a way to communicate important information on global behav...

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 the Constraint Satisfaction Ant Algorithm (CSAA) framework is presented. The underlying infrastructure and the ants behavior are described in detail. The CSAA framework is an ant-based system for solving discrete Constraint Satisfaction Problems (CSP) and Partial Constraint Satisfaction Problems (PCSP). CSPs and PCSPs are used among others to design facility layouts and schedule w...

Colonies of the ant Leptothorax albipennis naturally inhabit flat rock crevices. Scouts can determine, before initiating an emigration, if a nest has sufficient area to house their colony. They do so with a rule of thumb: the Buffon’s needle algorithm. Based on a derivation from the classical statistical geometry of Comte George de Buffon in the 18th century, it can be shown that it is possible...

This paper presents an ant system algorithm for the graph coloring problem. This ant system algorithm has several new features compared to previous ant system algorithms for the coloring problem. The main idea of the algorithm is for a colony of ants to color the graph, in which ants use its knowledge about the local neighborhood, recently visited nodes, and information stored at each vertex. F...

To enhance the optimization ability of ant colony optimization, this paper proposes a Bloch sphere-based quantum-inspired ant colony optimization algorithm. In the proposed approach, the positions of ants are encoded by qubits described on Bloch sphere. First, the destinations of ants are obtained by the select probability designed by the pheromone and heuristic information, and then, the movem...

Organization is a central concept in systems. In this paper an ant algorithm for detecting organizations is presented. In a discrete-time context, at each time-step, an organization corresponds to a set of closely interacting entities in a system. This system is mapped to a graph where nodes represent entities and edges represent interrelations. Several colonies of ants compete, and inside each...

Ants algorithm is a universal and flexible solution which was first designed for solving optimization problem such as Traveling Salesman Problem. Analogy between finding the shortest way by ants and finding documents most alike, became a stimulus of ant based text document clustering method. This method consist of two phases, which are finding documents most alike (trial phase) and clusters mak...

Ants are known to be capable of homing to their nest after displacement to a novel location. This is widely assumed to involve some form of retinotopic matching between their current view and previously experienced views. One simple algorithm proposed to explain this behavior is continuous retinotopic alignment, in which the ant constantly adjusts its heading by rotating to minimize the pixel-w...