Abstract

Ant nest relocation is smoother and swifter than the same process undertaken by any other animal. Within the population of ants, the ratio that participates in nest relocation is only 58.0% at best and 31.0% at worst. Does such a low active ratio improve or deteriorate ant nest relocation? In this study, we use a particle swarm optimization (PSO) algorithm to simulate real-world ant nest relocation. Our PSO-based algorithm duplicates the velocity and position of an inactive particle (representing an inactive ant) with the velocity and position of an active particle (representing an active ant). The number of particles that the algorithm computes is dramatically reduced, and the global best position can be identified at an early stage. In a series of simulations, our algorithm performs significantly better and faster with active ratios of 15%, 30%, 35%, 45%, 55%, 60%, and 75%–95% than with the full 100% active ratio. We confirm the robust and stable performance of our algorithm at active ratios of 60%, 80%, and 85%. Clustering of the simulation results shows that low active ratios improve ant nest relocation. Furthermore, three field studies carried out by biology experts empirically demonstrate that we have successfully modeled and simulated real-world ant nest relocation using our PSO-based algorithm.

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