Title: Unveiling Swarm Intelligence with Network Science$-$the Metaphor Explained,
Abstract: Self-organization is a natural phenomenon that emerges in systems with a
large number of interacting components. Self-organized systems show robustness,
scalability, and flexibility, which are essential properties when handling
real-world problems. Swarm intelligence seeks to design nature-inspired
algorithms with a high degree of self-organization. Yet, we do not know why
swarm-based algorithms work well and neither we can compare the different
approaches in the literature. The lack of a common framework capable of
characterizing these several swarm-based algorithms, transcending their
particularities, has led to a stream of publications inspired by different
aspects of nature without much regard as to whether they are similar to already
existing approaches. We address this gap by introducing a network-based
framework$-$the interaction network$-$to examine computational swarm-based
systems via the optics of social dynamics. We discuss the social dimension of
several swarm classes and provide a case study of the Particle Swarm
Optimization. The interaction network enables a better understanding of the
plethora of approaches currently available by looking at them from a general
perspective focusing on the structure of the social interactions.