The study of ant colonies behavior and their self-organizing capabilities is of interest to machine learning community, because it provides models of distributed adaptive organization which are useful to solve difficult optimization and classification problems among others. Social insects like ants, bees deposit pheromone (a type of chemical) in order to communicate between the members of their...

Consider the Ants Nearby Treasure Search (ANTS) problem introduced by Feinerman, Korman, Lotker, and Sereni (PODC 2012), where n mobile agents, initially placed in a single cell of an infinite grid, collaboratively search for an adversarially hidden treasure. In this paper, the model of Feinerman et al. is adapted such that each agent is controlled by an asynchronous (randomized) finite state m...

A new algorithm ABC (Ant based Clustering) inspired from behavior of the real ants is proposed for clustering in data mining. ABC employs some ants clustering in the searching space. Different from some clustering algorithms, instead of picking or dropping data objects, artificial ants in ABC find and merge the similar data points in their vision range, also a following and randomly route choos...

This paper presents a work inspired by the Pachycondyla apicalis ants behavior for the clustering problem. These ants have a simple but efficient prey search strategy: when they capture their prey, they return straight to their nest, drop off the prey and systematically return back to their original position. This behavior has already been applied to optimization, as the API meta-heuristic. API...

With large number of ants, the ant colony algorithm would always take a long time or is rather difficult to find the optimal path from complex chapter path, further more, there exists a contradiction between stagnation, accelerated convergence and precocity. In this paper, we propose a new bionic optimization algorithm. The main idea of the algorithm is to introduce the horizons concept in the ...

Model Checking is a well-known and fully automatic technique for checking software properties, usually given as temporal logic formulas on the program variables. Most of model checkers found in the literature use exact deterministic algorithms to check the properties. These algorithms usually require huge amounts of computational resources if the checked model is large. We propose here the use ...

This paper addresses the problem of multiagent task allocation in extreme teams. An extreme team is composed by a large number of agents with overlapping functionality operating in dynamic environments with possible inter-task constraints. We present an approximate algorithm for task allocation in extreme teams, called eXtreme-Ants. The algorithm is inspired in the division of labor in social i...

In this paper we propose a new approach to solve bi-criterion optimization problems with ant algorithms where several colonies of ants cooperate in nding good solutions. We introduce two methods for cooperation between the colonies and compare them with a multistart ant algorithm that corresponds to the case of no cooperation. Heterogeneous colonies are used in the algorithm, i.e. the ants di e...

The Ant Colony Algorithm is an effective method for solving combinatorial optimization problems. However, in practical applications, there also exist issues such as slow convergence speed and easy to fall into local extremum. This paper proposes an improved Quantum Ant Colony Algorithm based on Bloch coordinates by combining Quantum Evolutionary Algorithm with Ant Colony Algorithm. In this algo...

The ant colony algorithm has been applied to the problem of finding the minimal potential energy configuration of a small physical system (cluster) of atoms interacting via the Lennard–Jones phenomenological potential. The ants were positively motivated if their activity (displacement of atomic positions) leads to a lower total potential energy of the system. Starting from a random spatial dist...