Putting Simple Hierarchy into Ant Foraging: Cluster-Based Soft-Bots

This paper revisits a traditional Ant Foraging algorithm and proposes a Cluster-based Softbots algorithm to address the performance issues caused by constraints of random autonomous search featured in most swarm intelligence-based algorithms. A simple hierarchy is introduced to regulate the unfolding of dynamically changing swarm-like behaviors. Comparative experiments for Ant Foraging and the proposed Cluster-based Softbots are described. The results demonstrate that Softbots have significant comparative advantages over a traditional Ant Foraging algorithm on the benchmark criteria in the presented experimental settings. It is shown that Softbots are more suitable for resource-lean search circumstances whereas not many individual agents can be allocated.