1 Parallel Ant Colony Algorithms
نویسنده
چکیده
Ant colony algorithms are computational methods for solving problems that are inspired by the behaviour of real ant colonies. One particularly interesting aspect of the behaviour of ant colonies is that relatively simple individuals perform complicated tasks. Examples for such collective behavior are: i) the foraging behaviour that guides ants on short paths to their food sources, ii) the collective transport of food where a group of ants can transport food particles that are heavier than the sum of what all members of the group can transport individually, and iii) the brood sorting behavior of ants to place larvae and eggs into brood chambers of the nest that have the best environmental conditions. In this chapter we concentrate on the Ant Colony Optimization (ACO) metaheuristic for solving combinatorial optimization problems. ACO is inspired by the foraging behaviour of ants. An essential aspect thereby is the indirect communication of the ants via pheromones, i.e., chemical substances which are released into the environment and influence the behavior or the development of other individuals of the same species. In a famous biological experiment called double bridge experiment ([9, 23]) it was shown how trail pheromone lead ants along short paths to their food sources. In this experiment a double bridge with two branches of different lengths connected a nest of the Argentine ant with a food source. It was found that after a few minutes nearly all ants use the shorter branch. This is interesting because Argentine ants can not see very well. The explanation of this behavior has to do with the fact that the ants lay pheromone along their path. It is likely that ants which randomly chose the shorter branch arrive earlier at the food source. When they go back to the nest they smell some pheromone on the shorter branch
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تاریخ انتشار 2004