A Detailed Analysis of the Population-Based Ant Colony Optimization Algorithm for the TSP and the QAP
نویسندگان
چکیده
The population-based ant colony optimization algorithm (PACO) uses a very different pheromone update when compared to other ACO algorithms. In this work, we study P-ACO’s behavior for solving the traveling salesman problem (TSP) and the quadratic assignment problem (QAP). In particular, we investigate the impact of a local search on P-ACO parameters and performance. The results clearly show that P-ACO is a very competitive tool whose parameters and behavior depend strongly on the problem tackled and on whether a local search is used.
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