Swarm intelligence (SI) is a branch of artificial intelligence, which has been existing in nature among grouping and activities of animals, birds, ants, fish or even microbes. Swarm Intelligence is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept was originally used by Beni and Wang in the context of cellular robotic systems. In this paper, we show a self-awareness concept and theory of swarm intelligence that can be used to discover authoritative and popular information as well as emerging and anomalous information when traditional connections among information nodes (e.g., hyperlinks or citations) are not available. The different categories of information can be all high-value depending on the application requirements. A self-awareness of swarm intelligence is a data-driven framework, modeled and measured using a recursive distributed infrastructure of machine learning. The combination of the machine learning and swarm intelligence to be extended and enhanced in a completely new perspective. Since swarm intelligence systems consist typically of a population of simple agents interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of “intelligent” global behavior, unknown to the individual agents. We used the technology described above, built a data model from USPTO database, NCBI database, JGI (Joint Genomic Database) and KEGG database, as well as our own bio-database. We used the machine learning method on these data models to select microbial consortia for wastewater treatment using the swarm intelligence of microbes. The collective behaviors of the selected microbes are used for cleaning wastewater and convert bio-wastes to usable energy.
Journal: TechConnect Briefs
Volume: 4, Informatics, Electronics and Microsystems: TechConnect Briefs 2018
Published: May 13, 2018
Pages: 16 - 19
Industry sectors: Advanced Materials & Manufacturing | Sensors, MEMS, Electronics
Topics: Informatics, Modeling & Simulation