MATLAB Implementation of Artificial Glowworm Swarm Optimization for Solving the Knapsack Problem

In this study, we implemented an artificial glowworm swarm optimization algorithm using MATLAB to solve the knapsack problem. The algorithm mimics the behavior of glowworms searching for food by incorporating concepts of luminescence intensity and attractiveness to maximize individual fitness. For code implementation, we developed functions to initialize glowworm positions representing potential solutions, calculate fitness based on knapsack value constraints, and update positions through attraction mechanisms. The algorithm was applied to knapsack problem optimization by continuously adjusting parameters including glowworm positions and luminosity levels to achieve maximum knapsack value. Key MATLAB functions involved include distance calculation between glowworms, dynamic luminosity updates, and constraint handling for invalid solutions. Furthermore, we conducted detailed analysis and discussion on algorithm runtime efficiency and solution accuracy to better understand its advantages, limitations, and application scope. Performance metrics were evaluated through multiple test cases with varying problem sizes and constraint conditions.