Cooperation-based gene regulatory network for target entrapment

Abstract

Multi-agent systems are applied to a variety of scenarios, in which target entrapment has become a primary research area in recent decades. In order to solve the problem of intelligent swarm behavior control, the hierarchical gene regulation network (H-GRN) is proposed. However, the networks in H-GRN rely solely on target information for behavioral control, and interaction with surrounding partners only involves avoiding physical collisions. To benefit from the cooperation with partners, we design a cooperation-based gene regulatory network (C-GRN) for target entrapment. Following the hierarchical gene regulatory network, we use the agent’s own sensor to get the companion information, and add information to the network by controlling changes in the corresponding protein concentration. In addition, a self-organizing obstacle avoidance control method is also proposed. A series of empirical evaluations index comparison show that C-GRN can cooperate with partners. The experimental results indicate that the total time to complete task and average thickness of the target’s encirclement is obviously optimized in a simulation experiment.