Se precisar resolver problemas complexos, chame as especialistas: as formigas

quinta-feira, dezembro 09, 2010

First published online December 8, 2010
Journal of Experimental Biology 214, 50-58 (2011)
Published by The Company of Biologists 2011 
doi: 10.1242/jeb.048173

Optimisation in a natural system: Argentine ants solve the Towers of Hanoi
Chris R. Reid1,*David J. T. Sumpter2 and Madeleine Beekman1
1 Behaviour and Genetics of Social Insects Laboratory and Centre for Mathematical Biology, School of Biological Sciences A12, University of Sydney, NSW 2006, Australia
2 Mathematics Department, Uppsala University, Box 480, 751 06 Uppsala, Sweden

* Author for correspondence (christopher.reid@sydney.edu.au)
Accepted 2 October 2010
Natural systems are a source of inspiration for computer algorithms designed to solve optimisation problems. Yet most ‘nature-inspired’ algorithms take only superficial inspiration from biology, and little is known about how real biological systems solve difficult problems. Moreover, ant algorithms, neural networks and similar methods are usually applied to static problems, whereas most biological systems have evolved to perform under dynamically changing conditions. We used the Towers of Hanoi puzzle to test whether Argentine ants can solve a potentially difficult optimisation problem. We also tested whether the ants can adapt to dynamic changes in the problem. We mapped all possible solutions to the Towers of Hanoi on a single graph and converted this into a maze for the ants to solve. We show that the ants are capable of solving the Towers of Hanoi, and are able to adapt when sections of the maze are blocked off and new sections installed. The presence of exploration pheromone increased the efficiency of the resulting network and increased the ants' ability to adapt to changing conditions. Contrary to previous studies, our studyshows that mass-recruiting ant species such as the Argentine ant can forage effectively in a dynamic environment. Our results also suggest that novel optimisation algorithms can benefit from stronger biological mimicry.
Key words: Argentine ants, Linepithema humile, optimisation problem, nature-inspired algorithms, trail pheromones
Abbreviations: ACO, ant colony optimisation • NP-hard, non-deterministic polynomial-time hard • {eta}2p, partial eta squared
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PERGUNTA E NOTA INDISCRETA DESTE BLOGGER:
Mero acaso? Fortuita necessidade, ou Design Inteligente? 
Eu já li em um livro de sabedoria oriental antigo sobre se considerar estas especialistas na solução de problemas complexos diários e de planejamento administrativo de curto, médio e longo prazo. Ah, que eu li, eu li..., mas dizem que isso não é ciência... Por que a pesquisa acima é???