ABSTRACT

Swarm intelligence corresponds to a collection of quantified algorithms that simulate natural learning behavior in a system of individuals that synchronize using self-organizing and distributed control mechanism. The working principle of the algorithms focuses toward the collective behavior inference, which results from interactions among individuals in the group and with the environment. Statistical learning denotes the applicability of statistical tools for effective modeling, data analysis, and understanding the patterns over complex datasets. Particle swarm optimization (PSO) is a meta-heuristic optimization technique modeled after the social behavior of animals like birds and fish. The PSO method begins by seeding a random population of particles across the search space. Each particle is given a place and initial velocity. The role of swarm intelligence algorithms and data classification in biomedical engineering is an emerging domain for research in building a real-world decision support system.