Ants have always fascinated human beings. What particularly strikes the occasional observer as well as the scientist is the high degree of societal organization that these insects can achieve in spite of very limited individual capabilities. Ants have inspired also a number of optimization algorithms. These algorithms are increasingly successful among researches in computer science and operational research.
A particular successful metaheuristic—Ant Colony Optimization (ACO)—as a common framework for the existing applications and algorithmic variants of a variety of ant algorithms has been proposed in the early nineties by Marco Dorigo. ACO takes inspiration from the foraging behavior of some ant species. These ants deposit pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony. ACO exploits a similar mechanism for solving combinatorial optimization problems.
In recent years ACO algorithms have been applied to more challenging and complex problem domains. One such domain is continuous optimization. However, a direct application of the ACO for solving continuous optimization problem is difficult.
The first algorithm designed for continuous function optimization was continuous ant colony optimization which comprises two levels: global and local; it uses the ant colony framework to perform local searches, whereas global search is handled by a genetic algorithm. Up to now, there are few other adaptations of ACO algorithm to continuous optimization problems: continuous interacting ant colony, ACO for continuous and mixed-variable, aggregation pheromone system, and multilevel ant-stigmergy algorithm.
In this chapter we will present so-called Differential Ant-Stigmergy Algorithm (DASA), a new approach to the continuous optimization problem. We start with the DASA description followed by three case studies which show real-world application of the proposed optimization approach. Finally, we conclude with discussion of the obtained results.