Multi-Strategy Combinatorial Optimization Framework

The Multi-Strategy Combinatorial Optimization Framework is a problem-solving platform designed to tackle computationally challenging optimization problems through the integration of multiple algorithmic techniques. The framework focuses on finding efficient solutions for classical combinatorial optimization problems such as the Traveling Salesperson Problem (TSP), Knapsack Problem, and Graph Coloring Problem. The system implements and compares various optimization strategies, including Greedy Heuristics, Dynamic Programming, Backtracking, and Divide & Conquer algorithms. By applying different approaches to the same problem, the framework enables performance evaluation based on execution time, solution quality, and computational efficiency. Developed as an educational and research-oriented project, the framework demonstrates advanced problem-solving techniques, algorithm design, complexity analysis, and optimization principles. It provides a practical environment for understanding how different strategies perform across diverse combinatorial challenges.