A Dynamic Hybrid Approach Based on Ant Colony Optimization and Simulated Annealing to Solve the Multi-objective K-Minimum Spanning Tree Problem

This paper presents an efficient approximate hybrid algorithm designed to tackle the multi-objective k-Minimum Spanning Tree (MO k-MST) problem. Instead of aiming to identify the entire Pareto optimal solution set, we opt to convert the MO nature of the problem to a single objective one using the weighted sum method. Subsequently, we integrate both simulated annealing (SA) and ant colony optimization (ACO) algorithms in order discover practical solutions to the problem. The MO k-MST dilemma arises in various real-world decision-making scenarios. Numerical experiments demonstrate that our proposed hybrid approach outperforms the standalone simulated annealing method, thus offering enhanced performance.