Ant Colony Optimization

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 approach, was recently proposed by Dorigo et al. to solve several discrete optimization problems (Dorigo, 1996, 1997). The general ACO algorithm mimics the way real ants find the shortest route between a food source and their nest. The ants communicate with one another by means of pheromone trails and exchange information indirectly about which path should be followed. Paths with higher pheromone levels will more likely be chosen and thus reinforced later, while the pheromone intensity of paths that are not chosen is decreased by evaporation. This form of indirect communication is known as stigmergy, and provides the ant colony shortest-path finding capabilities. The first algorithm following the principles of the ACO meta-heuristic is the Ant System (AS) (Dorigo,1996), where ants iteratively construct solutions and add pheromone to the paths corresponding to these solutions. Path selection is a stochastic procedure based on two parameters, the pheromone and heuristic values, which will be detailed in the following section in this chapter. The pheromone value gives an indication of the number of ants that chose the trail recently, while the heuristic value is problem-dependent and it has different forms for different cases. Due to the fact that the general ACO can be easily extended to deal with other optimization problems, its several variants has been proposed as well, such as Ant Colony System (Dorigo,1997), rank-based Ant System (Bullnheimer,1999), and Elitist Ant System (Dorigo,1996) . And the above variants of ACO have been applied to a variety of different problems, such as vehicle routing (Montemanni,2005), scheduling (Blum,2005), and travelling salesman problem (Stützle,2000). Recently, ants have also entered the data mining domain, addressing both the clustering (Kanade,2007), and classification task (Martens et al.,2007). This chapter will focus on another application of ACO to track initiation in the target tracking field. To the best of our knowledge, there are few reports on the track initiation using the ACO. But in the real world, it is observed that there is a case in which almost all ants are inclined to gather around the food sources in the form of line or curve. Fig. 1 shows the evolution process of ants searching for foods. Initially, all ants are distributed randomly 14