ABSTRACT
This book chapter focuses on the Glowworm Swarm Optimization (GSO) algorithm, a swarm intelligence algorithm loosely based on the behavior of glowworms, which are also known as fireflies or lightning bugs. Although there are other swarm intelligence algorithms in the literature based on certain perceived behaviour of fireflies (for instance, the firefly algorithm and its extensions), the GSO algorithm precedes these by several years. The GSO algorithm was originally intended to be implementable in robotic systems where a team of mobile robots, or glowworms, equipped with sensors that would enable them to mimic the sensing capabilities of glowworms, would explore a signal landscape to identify multiple signal sources. The mathematical formulation of this problem devolved into a numerical optimization problem requiring the computation of multiple optima of a multimodal function (as against computation of the global optimum of such a function that most other swarm intelligence algorithms aim for). In this chapter, the basic working principle of GSO is introduced, which is followed by a description of the phases that constitute each cycle of the algorithm. Next, the pseudocode, MATLAB code, and C++ code of the algorithm are presented. Finally, the working of various steps of the algorithm is illustrated by using a numerical example.
