A multi-objective optimization method for intelligent swarm robotic control model with changeable parameters

In recent years, research into self-organized swarm robotics has received much attention. Within this research, development of a general control model that functions under different conditions has been a hot topic. One significant approach is to use a control model with tunable parameters determined through simulation. In this article, we propose a rule-based swarm robotics control model with tunable parameters. To improve the performance, a modified non-dominated sorting genetic algorithm (NSGA II) is embedded into this control model. Multiple scenarios including convex obstacles, square obstacles, and tunnels are used to test the performances of the proposed control model. Comparison is made with three widely applied population-based multi-objective optimization algorithms. Experimental results show that our control model has good performance and is robust under different scenarios.