Such systems often hinge upon the construction of efficient transportation networks connecting the resources. Many biological systems have been refined through millions of years of natural selection to efficiently exploit the ephemeral, often fiercely contested and spatially isolated resources of their environment. Natural systems have proved to be a rich source of inspiration for computer scientists in designing optimisation algorithms ( Bonabeau et al., 2000 Vassiliadis and Dounias, 2009). Our results also suggest that novel optimisation algorithms can benefit from stronger biological mimicry. Contrary to previous studies, our study shows that mass-recruiting ant species such as the Argentine ant can forage effectively in a dynamic environment. The presence of exploration pheromone increased the efficiency of the resulting network and increased the ants' ability to adapt to changing conditions. 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. 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 also tested whether the ants can adapt to dynamic changes in the problem. We used the Towers of Hanoi puzzle to test whether Argentine ants can solve a potentially difficult optimisation problem. 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. Yet most ‘nature-inspired’ algorithms take only superficial inspiration from biology, and little is known about how real biological systems solve difficult problems. Natural systems are a source of inspiration for computer algorithms designed to solve optimisation problems.
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