Abstract
This piece of research introduces POOCA (Progressive Optimisation of Organised Colonies of Ants) as an appealing variant of the established ACO (Ant Colony Optimisation) algorithm. The novelty of POOCA lies on the combination of the co-operation inherent in ACO with the spread of activation around the winner node during SOM (Self-Organising Map) training. The principles and operation of POOCA are demonstrated on examples from robot navigation in unknown environments cluttered with obstacles: efficient navigation and obstacle avoidance are demonstrated via the construction of short and – at the same time - smooth paths (i.e. optimal, or near-optimal solutions); furthermore, path convergence is speedily accomplished with restricted numbers of ants in the colony. The aim of this presentation is to put forward the application of POOCA to combinatorial optimisation problems such as the traveling salesman, scheduling etc.
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Tambouratzis, T. (2007). Progressive Optimisation of Organised Colonies of Ants for Robot Navigation: An Inspiration from Nature. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71629-7_73
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DOI: https://doi.org/10.1007/978-3-540-71629-7_73
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