MATLAB Source Code for Floyd Algorithm, Dijkstra Algorithm, Greedy Algorithm, Genetic Algorithm, Search Algorithms, Ant Colony Algorithm, and Hamiltonian Cycle

This article presents MATLAB source code implementations with accompanying documentation for several fundamental algorithms: Floyd's algorithm for shortest paths in weighted graphs, Dijkstra's algorithm for single-source shortest path solutions, greedy algorithms for local optimization problems, genetic algorithms for evolutionary optimization, various search algorithms including depth-first and breadth-first approaches, ant colony optimization for combinatorial problems, and Hamiltonian cycle detection for graph theory applications. These algorithms represent crucial components in computer science, widely employed for solving diverse computational problems. The implementations demonstrate core MATLAB programming techniques including matrix operations for Floyd's algorithm, priority queue simulations for Dijkstra's method, heuristic selection mechanisms in greedy approaches, population-based optimization in genetic algorithms, graph traversal methods in search algorithms, pheromone-based path selection in ant colony systems, and backtracking solutions for Hamiltonian cycles. Each algorithm implementation includes practical examples showing how to initialize problem parameters, execute the core computation steps, and interpret results. The code follows MATLAB best practices with clear variable naming, commented sections explaining key operations, and modular structure for easy adaptation. These resources aim to help users understand algorithmic principles through practical implementation and apply them effectively to real-world problems.