MATLAB Implementation of Hybrid Ant Colony Algorithm with 2-Opt Neighborhood Search (ACA)

In combinatorial optimization problems, Ant Colony Algorithm (ACA) and exchange neighborhood search are two commonly used heuristic methods. Combining these approaches leverages their respective strengths: ACA excels at global exploration, while exchange neighborhood search enables local refinement. Algorithm Integration Approach ACA Framework: Ants construct initial solutions by selecting paths based on pheromone trails, with pheromone updates reflecting path quality. Embedded Neighborhood Search: After each ACA iteration, perform 2-opt operations (e.g., swapping positions of two nodes) on the current best solution to verify if new solutions yield improvements, thereby escaping local optima. Key Implementation Considerations Pheromone Matrix: Stores edge selection probabilities and undergoes dynamic updates to guide search direction. In MATLAB, this can be implemented as a 2D array with evaporation and reinforcement mechanisms. Neighborhood Operations: Implement element swapping either randomly or through systematic traversal while maintaining problem constraints (e.g., path continuity requirements in TSP). The 2-opt swap typically involves reversing path segments between selected indices. Hybrid Strategy: Configure trigger conditions for neighborhood search (e.g., every N generations) to balance computational efficiency and solution precision using conditional statements and iteration counters. This hybrid approach proves particularly effective for Traveling Salesman Problem (TSP), job scheduling scenarios, and similar combinatorial optimizations, significantly enhancing solution convergence quality through complementary global and local search mechanisms.