Glowworm Swarm Optimization (GSO) Original Code Implementation

The Glowworm Swarm Optimization (GSO) algorithm is a swarm intelligence optimization technique inspired by the natural luminous behavior of fireflies. This algorithm solves multidimensional optimization problems by simulating the attraction and movement mechanisms of glowworm swarms, particularly suitable for multimodal function optimization and dynamic environment searching. Core Algorithm Principles: Luminous Attraction Mechanism: Each glowworm attracts neighboring individuals based on its luminosity (fitness value), with higher luminosity generating stronger attraction forces. Dynamic Perception Range: The visible range of each glowworm dynamically adjusts according to neighbor density, preventing local clustering. Position Update Rule: Lower-luminosity individuals move toward higher-luminosity neighbors, with movement step size decaying proportionally to distance. MATLAB Implementation Features: Efficient matrix operations for handling population position and luminosity updates. Euclidean distance calculation for determining neighborhood relationships between individuals. Dynamic perception radius adjustment to balance exploration and exploitation phases. Key functions include: - initialize_glowworms(): Sets initial positions and luciferin levels - update_luciferin(): Computes fitness-based luminosity values - movement_step(): Implements position updates using attraction vectors Application Scenarios: Robotic path planning and navigation optimization Wireless sensor network deployment and coverage optimization Machine learning hyperparameter tuning and model optimization Multi-peak function optimization in engineering design