The particle swarm: social adaptation of knowledge

Abstract

Particle swarm adaptation is an optimization paradigm that simulates the ability of human societies to process knowledge. The algorithm models the exploration of a problem space by a population of individuals; individuals' successes influence their searches and those of their peers. The algorithm is relevant to cognition, in particular the representation of schematic knowledge in neural networks. Particle swarm optimization successfully optimizes network weights, simulating the adaptive sharing of representations among social collaborators. The paper introduces the algorithm, begins to develop a social science context for it, and explores some aspects of its functioning.