2.1. Basic Glowworm Swarm Optimization
In nature, seemingly simple life-year individual, group activities have certain characteristics. Birds in the absence of migration, there is no centralized control, but to complete the collective action, ant through the sowing pheromone to remember the path of the way to inform the community to find food, bees through the different division of labor to exchange nectar source position information. The Glowworm Swarm Optimization simulates the luminous characteristics of the glowworms in nature to be randomly optimized, and the individuals with strong fluorescence intensity attract weak individuals with weak fluorescence. The Glowworm Swarm Optimization (GSO) is a new group of intelligent optimization algorithms based on multi-peak function optimization proposed by K.N. Krishnanad and D. Hheose in 2005, the algorithm has been successful in the optimization of complex functions. The Glowworm Swarm Optimization mimics the behavior of glowworms predators and spouses in nature, and glowworm individuals use their luminescent properties to find better individuals in their search areas to optimize their position. Nowadays, the Glowworm Swarm Optimization is attracting more and more attention and has been applied to many fields, for example, in the application of swarm robots(Krishnanand, & Ghose, 2005), leakage of harmful gas or nuclear leakage detection(Krishnanand, & Ghose, 2006), detection of multiple beam source localization(Krishnanand, 2007), and multi-modal optimization problems (Krishnanand, & Ghose, 2009, Krishnanand, & Ghose, 2009, Krishnanand, & Ghose, 2006), etc., the better application of this algorithm has attracted the attention of scholars at home and abroad, and has become a new research hot topic in the field of intelligent computing.