Glowworm Swarm Optimization (GSO) is a nature-inspired algorithm designed to solve optimization problems, particularly those with multiple optimal solutions. It mimics the behavior of glowworms, which use bioluminescence to attract peers in dark environments. In GSO, artificial agents (glowworms) move through a search space, adjusting their positions based on the brightness of nearby neighbors. Each glowworm carries a “luciferin” value, which represents the quality of its current location—higher luciferin means a better solution. The algorithm iteratively updates agent positions, allowing the swarm to identify and converge toward multiple optima simultaneously, making it suitable for multimodal optimization tasks like sensor deployment or robotics path planning.
The process begins by initializing glowworms with random positions and luciferin values. During each iteration, every agent updates its lucuerin based on the fitness of its current location (e.g., the objective function value). Next, glowworms detect neighbors within a dynamic “decision range,” which determines their visibility. Each agent selects a neighbor with higher luciferin and moves toward it, with step size controlled by the brightness difference and distance. For example, in a robot swarm searching for multiple targets, robots (glowworms) would gravitate toward peers closer to targets, splitting the swarm to cover different areas. The decision range also adapts over time to avoid overcrowding, ensuring agents spread out to explore distinct regions of the search space.
GSO is advantageous for problems requiring the discovery of multiple peaks or solutions. Unlike Particle Swarm Optimization (PSO), which converges to a single global optimum, GSO maintains diversity in the swarm by allowing subgroups to form around local optima. For instance, in wireless sensor network deployment, GSO could position sensors around multiple event hotspots without overlapping coverage. Developers can implement GSO by tuning parameters like luciferin decay rate, step size, and neighbor count, which balance exploration and exploitation. While computationally efficient for moderate-sized problems, its performance depends on parameter choices and problem complexity. This flexibility makes GSO a practical tool for tasks like image processing, clustering, or route optimization where multiple high-quality solutions are valuable.