Self-organized and distributed control methods are increasingly important as they allow multi-agent systems to scale more readily than centralized control techniques. Furthermore, these methods increase system robustness and flexibility. In the online multi-object k-coverage domain studied here, a collective of autonomous agents must dynamically form sub-teams to accomplish two concurrent tasks: target discovery and coverage. Once a target is discovered, the collective of agents must create a sub-team of k-agents to cover the target. The work presented here introduces a novel, entropy-based task selection technique that incorporates signal suppression behaviors found in bee colonies. We test the technique in the online multi-object k-coverage domain while exploring three team properties: heterogeneity, team size, and sensor ranges, and their impact on multi-task accomplishment. Results show that signal suppression helps avoid over-provisioning of team resources to individual targets, dynamically creating sub-teams that simultaneously accomplish target discovery and coverage tasks.