Swarm Intelligence (SI) is a popular multi-agent framework that has been originally inspired by swarm behaviors observed in natural systems, such as ant and bee colonies. In a system designed after swarm intelligence, each agent acts autonomously, reacts on dynamic inputs, and, implicitly or explicitly, works collaboratively with other swarm members without a central control. The system as a whole is expected to exhibit global patterns and behaviors. Although well-designed swarms can show advantages in adaptability, robustness, and scalability, it must be noted that SI system have not really found their way from lab demonstrations to real-world applications, so far.

This is particularly true for embodied SI, where the agents are physical entities, such as in swarm robotics scenarios. In this paper, we start from these observations, outline different definitions and characterizations, and then discuss present challenges in the perspective of future use of swarm intelligence. These include application ideas, research topics, and new sources of inspiration from biology, physics, and human cognition. To motivate future applications of swarms, we make use of the notion of cyber-physical systems (CPS). CPSs are a way to encompass the large spectrum of technologies including robotics, internet of things (IoT), Systems on Chip (SoC), embedded systems, and so on. Thereby, we give concrete examples for visionary applications and their challenges representing the physical embodiment of swarm intelligence in autonomous driving and smart traffic, emergency response, environmental monitoring, electric energy grids, space missions, medical applications, and human networks.

We do not aim to provide new solutions for the swarm intelligence or CPS community, but rather build a bridge between these two communities. This allows us to view the research problems of swarm intelligence from a broader perspective and motivate future research activities in modeling, design, validation/verification, and human-in-the-loop concepts.

The trend in today’s technologies is that computers are becoming embedded in everyday objects. Such systems range from Systems on Chip (SoC) that make the computational core of modern everyday devices such as smartphones, to the Internet of Things (IoT) that connects billions of edge devices, and the Internet of Everything (IoE), which brings together people, processes, data, and devices. In short, we can say that the world is becoming truly collective and connected, increasingly featuring systems that integrate large numbers of multiple interacting components at different scales. Nevertheless, the era of pervasive computing is still at the very beginning, and a number of fundamental issues related to design, computation, prediction, and control in large systems of interacting autonomous components still need to be addressed.

If we escalate to the more general CPSoS model, the design and control challenges become even harder, since individual component autonomy must be glued by explicitly addressing interdependence and coordination, interoperability, distributed control and emergence of behaviors. In general, centralized and static management and control model are not expected to be the right solution approach to address all these challenges. Instead, the methods that should be employed should achieve the following goals:

1. distributed control, supervision and management,
2. local coordination among the composing subsystems,
3. partial autonomy of the subsystems,
4. capability of dynamic reconfiguration of the system as a whole on different time-scales,
5. evolution of the overall system during its operation,
6. possibility of generating useful emerging behaviors at the system level

Regards
Sarah Rose
Managing Editor
International Journal of Swarm Intelligence and Evolutionary Computation