Incremental social learning in swarm intelligence algorithms for continuous optimization

Abstract

Swarm intelligence is the collective problem-solving behavior of groups of animals and artificial agents. Often, swarm intelligence is the result of self-organization, which emerges from the agents' local interactions with one another and with their environment. Such local interactions can be positive, negative, or neutral. Positive interactions help a swarm of agents solve a problem. Negative interactions are those that block or hinder the agents' task-performing behavior. Neutral interactions do not affect the swarm's performance. Reducing the effects of negative interactions is one of the main tasks of a designer of effective swarm intelligence systems. Traditionally, this has been done through the complexification of the behavior and/or the characteristics of the agents that comprise the system, which limits scalability and increases the difficulty of the design task. In collaboration with colleagues, I have proposed a framework, called incremental social learning (ISL), as a means to reduce the effects of negative interactions without complexifying the agents' behavior or characteristics. In this paper, I describe the ISL framework and three instantiations of it, which demonstrate the framework's effectiveness. The swarm intelligence systems used as case studies are the particle swarm optimization algorithm, ant colony optimization algorithm for continuous domains, and the artificial bee colony optimization algorithm. © Springer-Verlag Berlin Heidelberg 2013.