Multi-Population Ant Colony Optimization Algorithm Based on Congestion Factor and Co-Evolution Mechanism

To solve large-scale traveling salesmen problem (TSP) with better performance, we propose a multi-population ant colony optimization algorithm based on congestion factor and co-evolution mechanism (CCMACO). First, the congestion factor is introduced to control the number of ants on the path and can improve the adaptability of CCMACO. Then, the sub-populations restructuring strategy is proposed to balance the convergence speed and the diversity of solutions. Besides, the inter-specific competition mechanism can be used to strengthen the optimal solution and to accelerate convergence speed. Finally, the co-evolutionary strategy is used to interchange information among different sub-populations so as to maintain the diversity of populations. For the purpose of verifying the optimization performance of the CCMACO algorithm, CCMACO is compared with several improved multi-population ant colony optimization algorithms in TSP. The experiment results show that the proposed CCMACO algorithm can effectively obtain the best optimization value in solving TSP and it achieves better optimization ability and stability.