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Ant Colony Search Essay

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2.5 Ant Colony Search Algorithm
Ant Colony Search Algorithm (ACSA) is a population-based approach for solving combinatorial optimization problems that is inspired by the foraging behaviour of real ants and their inherent ability to find the shortest path from a food source to their nest (Dorigo and Stutzle 2004). ACSA is the result of research on computational intelligence approaches to combinatorial optimization originally conducted by Dr. Marco Dorigo, in collaboration with Alberto Colorni and Vittorio Maniezzo. The fundamental approach underlying ACSA is an iterative process in which a population of simple ant agents repeatedly construct candidate solutions; this construction process is probabilistically guided by heuristic information on …show more content…

Initially, ants move randomly and chose between shorter and longer path with equal probability. While walking, the ants deposited pheromone. When choosing a path, ants chose with higher probability the path with the highest pheromone concentration. Ants choosing the short path will be first back with food and trail on shorter path grows more quickly Figure 2.10: Double Bridge Experiment

2.5.2 Double Bridge Experiments
The foraging behaviour of many ant species (Prabakaran, Senthilkumar, & Baskar, 2005), is based on indirect communication mediated by pheromones. While walking from food sources to the nest and vice versa, ants deposit pheromones on the ground, forming in their way a pheromone trail. Ants can smell the pheromone and choose probabilistically paths marked by strong pheromone concentrations. The pheromone trail-laying and following behaviour of some ant species have been investigated in controlled experiments by several researchers. One particularly brilliant experiment was designed and run by Deneubourg and colleagues (Deneubourg, Aron, Goss, & Pasteels, 1990; Goss et al., 1989), who used a double bridge connecting a nest of ants of the Argentine ant

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