Swarm Search Algorithm Based on Chemotactic Behaviors of Caenorhabditis elegans Nematodes

We investigated the chemotactic behaviors of the nematode Caenorhabditis elegans, whose individuals have only 302 neurons but might sense the density of other individuals. As an individual detects areas with high concentration of a target chemical, mimicking their behavior may improve the exploration efficiency of autonomous distributed agents with limited sensing area and no direct communication with others. Inspired by this behavior, we experimentally determined the relationship between the density of individuals and probability of rapid turns to develop a search algorithm. We found a parameter set of “elite” individuals that achieved a high similarity of individual distributions with respect to a chemical gradient. Then, we implemented a motion selection algorithm that reflects the observation results so that an autonomous distributed agent, which has limited sensing range, achieves effective searching in a multi-peak environment. We simulated autonomous agents and applied the parameter sets obtained from elite, inferior, and single individuals. Through verifications using various benchmark potential functions, we concluded that the parameters of the elite group improved the search efficiency.