We propose a novel multiobjective evolutionary algorithm, MEDACO, a shorter acronym for MOEA/D-ACO, combining ant colony optimization (ACO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D). The motivation is to use the online-learning capabilities of ACO, according to the Reactive Search Optimization (RSO) paradigm of ”learning while optimizing”, to further improve the ...

An ant colony capable of solving the traveling salesman problem (TSP). TSP is NP-hard problem. Even though the problem itself is simple, when the number of city is large, the search space will become extremely large and it becomes very difficult to find the optimal solution in a short time. One of the main ideas of ant algorithms is the indirect communication of a colony of agents, called (arti...

Ant colony optimization (ACO) which is inspired by the natural behavior of ants in finding the shortest path to food is appropriate for solving the combinatorial optimization problems. Therefore, it is used to solve the unit commitment problem (UCP) and attain the minimum cost for scheduling thermal units in order to produce the demand load. In this paper modified ACO (MACO) is used to solve th...

This paper uses a modified Ant Colony Optimization (ACO) algorithm to price simple financial derivatives. We use ants to find the optimum time to exercise an option. Our algorithm searches the solution space to find optimum solution under some user defined constraints. In this preliminary study we show that the modified ACO works in predicting an optimum exercise time of a simple vanilla option.

Although individual ants have an extremely basic intelligence, and are completely incapable of surviving on their own, colonies of ants can develop remarkably sophisticated and biologically successful behavior. This paper discusses a set of experiments which attempt to simulate one of these behaviors, namely the ability of ants to place pheromones as a way of communication. These experiments in...

Ant systems are flexible to implement and give possibility to scale because they are based on multi agent cooperation. The aim of this publication is to show the universal character of that solution and potentiality in implementing it in wide areas of applications. The increase of demand for effective methods of large document collections management is a sufficient stimulus to place the researc...

We use a genetic algorithm to evolve neural models of path integration, with particular emphasis on reproducing the homing behaviour of Cataglyphis fortis ants. This is done within the context of a complete model system, including an explicit representation of the animal's movements within its environment. We show that it is possible to produce a neural network without imposing a priori any par...