Particle Swarm optimization Describes Ant Nest Move at Low Participation Rate

Ants move from an old nest to a new nest within a short period of time. Contrary to popular belief that ants are workaholics, only 58.0% at best and 31.0% at worst of population of an ant colony work in their nest move. This ant nest move that is smoother and swifter than any other animals has attracted researchers and many models and simulations have been introduced into the computational intelligence community. However, researchers have not solved an open problem that is whether such low participation rates of active ants improve or deteriorate ant nest move. A positive answer to the problem would provide a technological inspiration for proposing a promising swarm-based algorithm in the context of computational intelligence and specific aspects of swarm intelligence in focus on participation rates of agents. In this study, we use an algorithm which is based on particle swarm optimization (PSO) and simulate real-world ant nest move. The simulation results of our PSO-based algorithm have shown that performance at the low participation rates 15%, 30%, 35%, 40%, 45%, 55%, and 60%is better and faster than performance at the full active population rate 100%. Our simulation results are supported by three field researches which were carried by external ant experts.