蚁群算法的全局收敛性研究及改进

针对蚁群算法(ACA)寻优性质优良,但搜索时间长、收敛速度慢、易限于局部最优解,从而使其进一步推广应用受到局限的问题,对算法的全局收敛性进行了深入的理论研究,并从改善全局收敛性的角度对算法作了一系列改进,最后对Bayes29这一典型的TSP问题进行了仿真实验。实验结果证明,改进后的蚁群算法具有很好的全局收敛性能。这为蚁群算法的进一步理论研究打下了很好的基础,对其在各优化领域中的推广应用具有重要意义。

Research and improvement on the global convergence of ant colony algorithm

Ant colony algorithm (ACA) is a novel heuristic algorithm, which is based on the process of ants in the nature searching for food. ACA has many good features in optimization, but it has the limitations of stagnation and poor convergence, and is easy to fall in local optima, which are the bottlenecks of its wide application. A detailed theoretical research on the global convergence of ACA is performed. A series of improvement schemes are also proposed. Finally, a typical example of TSP Bayes 29 is calculated. The results verify that the improved ACA has a satisfied global convergence, and lays a good foundation for further research on ACA in theory.