Benign: An Automatic Optimization Framework for the Logic of Swarm Behaviors

In the field of swarm intelligence, it is usually complicated to express the logic of swarm behaviors. Behavior tree has drawn a lot of attention to be a practical approach to solving this problem in recent years. However, how to automatically design the logic of swarm behaviors according to the target of a task is the focus of swarm intelligence. Hence, we propose an automatic optimizing framework named Benign which is capable of using gene expression programming (GEP) to optimize the logic of swarm behaviors. In Benign, the basic swarm behaviors and the relationships among those behaviors are mapped to nodes of behavior tree by the method named Matt firstly. With these nodes, we design an artificial behavior tree. After that, the artificial behavior tree is transformed into an expression tree in GEP according to the method named Meet. Finally, GEP is used for optimization to generate the expected logic of swarm behaviors. We conduct simulation experiments to validate the efficiency of Benign. The experimental results show the superiority of Benign. Compared with the logic of the artificial behavior tree before optimization, the conduction of the optimized logic of swarm behaviors increases efficiency by more than 50%.