To solve the problem that root branching plasticity cannot be achieved by using sequential model in root branch modeling, a new root branching method based on swarm Parrondo's game was proposed to analyze root branching plasticity in heterogeneous root growth environment. Firstly, root primordial swarm based on individual root primordium was constructed. Secondly, Parrondo's game was used to achieve interaction among root primordial swarm affected by environment. Finally, root branch modeling process was simulated according to auxin that was updated based on the interaction results of root primordial. Prediction of root branching probability was achieved in four different root growth environments. The experimental results show that compared with RootMap, etc, the proposed method can be used to model development process of root primordium into root branch affected by spatial and temporal changes in the growth environment of the root system, and also provides analysis means for root system modeling and simulation research.

Aiming at the problem that serial Particle Swarm Optimization (PSO) algorithms are time-consuming to deal with big tasks, a novel shared parallel PSO (Shared-PSO) algorithm was proposed. The multi-core processing power was used to reduce time to get resolution. In order to facilitate communication of particles, a shared area was set up and a random strategy was applied to switch particles. Several serial PSO algorithms could be permitted to update particle information because of the universality of its algorithm flow. Shared-PSO was applied on the standard optimization test set CEC (Congress on Evolutionary Computation) 2014. The experiment results show that the execution time of Shared-PSO is a quarter of the serial PSO's. The proposed algorithm can effectively improve the execution efficiency of serial PSO, and expand applied range of PSO.