Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications

Abstract

This paper presents multimodal function optimization, using a nature-inspired glowworm swarm optimization (GSO) algorithm, with applications to collective robotics. GSO is similar to ACO and PSO but with important differences. A key feature of the algorithm is the use of an adaptive local-decision domain, which is used effectively to detect the multiple optimum locations of the multimodal function. Agents in the GSO algorithm have a finite sensor range which defines a hard limit on the local-decision domain used to compute their movements. The GSO algorithm is memoryless and the glowworms do not retain any information in their memory. Some theoretical results related to the luciferin update mechanism in order to prove the bounded nature and convergence of luciferin levels of the glowworms are provided. Simulations demonstrate the efficacy of the GSO algorithm in capturing multiple optima of several multimodal test functions. The algorithm can be directly used in a realistic collective robotics task of simultaneously localizing multiple sources of interest such as nuclear spills, aerosol/hazardous chemical leaks, and fire-origins in a fire calamity.