Name
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. |
Year | Venue | DocType |
|---|---|---|
2018 | arXiv: Neural and Evolutionary Computing | Journal |
Volume | Citations | PageRank |
abs/1811.03539 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcos A. C. Oliveira | 1 | 4 | 1.54 |
| Diego Pinheiro | 2 | 0 | 0.68 |
| Mariana Macedo | 3 | 5 | 2.50 |
| Carmelo J. A. Bastos Filho | 4 | 5 | 1.66 |
| Ronaldo Menezes | 5 | 402 | 51.00 |