Optimization of the Golomb Ruler Problem Using Metaheuristics and Cooperative Methods

Developed an optimization approach for solving the Golomb ruler problem using metaheuristics and cooperative methods. Implemented a hybrid algorithm combining Simulated Annealing and Genetic Algorithm to enhance solution quality. Designed and tested an application in Python for generating, verifying, and optimizing Golomb rulers, comparing different heuristic strategies. This project demonstrates expertise in combinatorial optimization, algorithmic efficiency, and problem-solving with AI techniques