Uncovering the social interaction network in swarm intelligence algorithms

This is the final version. Available from the publisher via the DOI in this record.Swarm intelligence is the collective behavior emerging in systems with locally interacting components. Because of their self-organization capabilities, swarm-based systems show essential properties for handling real-world problems, such as robustness, scalability, and flexibility. Yet, we fail to understand why swarm-based algorithms work well, and neither can we compare the various approaches in the literature. The absence 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 a systematic comparison over existing approaches. Here we address this gap by introducing a network-based framework—the swarm interaction network—to examine computational swarm-based systems via the optics of the social dynamics. We investigate the structure of social interaction in four swarm-based algorithms, showing that our approach enables researchers to study distinct algorithms from a common viewpoint. We also provide an in-depth case study of the Particle Swarm Optimization, revealing that different communication schemes tune the social interaction in the swarm, controlling the swarm search mode. With the swarm interaction network, researchers can study swarm algorithms as systems, removing the algorithm particularities from the analyses while focusing on the structure of the swarm social interaction

Study of Different Small-world Topology Generation Mechanisms for Genetic Algorithms

The use of small-world graphs as a topology structure for the population of Evolutionary Algorithms (EAs) has been recently proposed in the literature. The motivation is clear: the high clustering coefficient and low characteristic path length of such networks makes them suitable for fast local information dissemination, while at the same time preventing it from quickly spreading on the whole population, as it happens in panmictic populations. However, even though several papers addressed this issue so far, only a few of them are able to provide competitive results with other panmictic and/or decentralized population EAs with similar configurations. Therefore, we perform ax study in this work, both theoretically and empirically, on the most appropriate mechanisms to generate SW topologies for Genetic Algorithms (a family of EA). The algorithms are analyzed in terms of efficiency and efficacy, and the best studied variant is validated versus other GAs using well known centralized and decentralized population structures, outperforming them