Abstract
Over the last decade, researchers have proposed several ant colony optimisation algorithms to solve combinatorial problems. Ant Colony Optimisation (ACO) was introduced by Dorigo et al. in the early 1990s and is based on the behaviour of natural ant colonies, in particular the foraging behaviour of real ant species. The indirect communication of real ants in the colony uses pheromone trail lying on the path to find the shortest trail between their food source and the nest. Recently, Evolutionary ACO algorithms have been proposed to solve truss optimisation problems (EACO algorithms). This algorithm can solve truss size and topology problems, which makes EACO very attractive to solve non-combinatorial optimisation problems. Computational tests are described to show the effectiveness of the EACO.
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