Many swarm intelligence optimisation algorithms have been inspired by the collective behaviour of natural and artificial, decentralised, self-organised systems. Swarm intelligence optimisation algorithms have unique advantages in solving certain complex problems that cannot be easily solved by traditional optimisation algorithms. Inspired by the adaptive phenomena of plants, a novel evolutionary algorithm named the bean optimisation algorithm (BOA) is proposed, which combines natural evolutionary tactics and limited random searches. It demonstrates stable behaviour in experiments and is a promising alternative to existing optimisation methods for engineering applications. A novel distribution model for BOA is built through research and study on the relevant research results of biostatistics. This model is based on a combination of the negative binomial and normal distributions, and the resulting algorithm is called NBOA. To validate NBOA, function optimisation experiments are carried out, which include ten typical benchmark functions. The results indicate that NBOA performs better than particle swarm optimisation (PSO) and BOA. We also investigate the characteristics of NBOA and conduct a contrast analysis to verify our conclusions about the relationship between its parameters and its performance.

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