Glowworm Swarm Optimization for Multimodal Search Spaces

Abstract

This chapter presents glowworm swarm optimization (GSO), a novel swarm intelligence algorithm, which was recently proposed for simultaneous capture of multiple optima of multimodal functions. In particular, GSO prescribes individual-level rules that cause a swarm of agents deployed in a signal medium to automatically partition into subswarms that converge on the multiple sources of the signal profile. The sources could represent multiple optima in a numerical optimization problem or physical quantities like sound, light, or heat in a realistic robotic source localization task. We present the basic GSO model and use a numerical example to characterize the group-level phases of the algorithm that gives an insight into how GSO explicitly addresses the issue of achievement/maintenance of swarm diversity. We briefly summarize the results from the application of GSO to the following three problems−multimodal function optimization, signal source localization, and pursuit of mobile signal sources.