A novel algorithm inspired by plant root growth with self-similarity propagation

Most nature-inspired algorithms simulate intelligent behaviors of animals and insects that can move spontaneously and independently. As another species of biology, the survival wisdom of plants has been neglected to some extent until now. This paper presents a novel plant-inspired algorithm which is called root growth optimizer (RGO). RGO simulates the adaptive growth behaviors of plant roots, e.g. self-similar propagation, to optimize continuous space search. In the process, different roots implement different strategies according to their biological roles, so as to cooperate as a whole. Seven well-known benchmark functions are used to validate its optimization effect. We compared RGO with other existing animal-inspired algorithm including artificial bee colony algorithm and particle swarm optimizer. The experimental results show that RGO outperforms other algorithms on most benchmark functions.