基于蚁群优化的混合智能算法研究

Research on Hybrid Intelligent Algorithm Based on Ant Colony Optimization

为了解决混合蚁群优化算法存在的问题,研究了当前求解组合优化问题性能表现最好的迭代局部搜索算法,并分析了其关键技术——局部搜索和扰动;通过将局部搜索和扰动分别用于增强蚂蚁系统算法的开发能力和探索能力,提出了一种基于蚁群优化的混合智能算法。求解TSP的实验表明,该混合智能算法保持了其开发能力和探索能力间的平衡,并实现了在合理的计算时间内对蚁群优化算法较高质量的改进。

Iterated local search algorithm with the best performance for the combinatorial optimizationproblems is studied so as to solve the existing problems of the hybrid ant colony optimization algorithms. Local search and perturbation are analyzed, which are the key techniques of iterated local search algorithm. Local search and perturbation are respectively used to enhance the exploitation and exploration ability of ant system algorithm, whereby a hybrid intelligent algorithm based on ant colony optimization is proposed. The experiment for the traveling salesman problem demonstrates that the hybrid intelligent algo- rithm can keep balance between the exploitation and exploration ability and realize the improvement of high quality on ant colony optimization algorithms in rational computational time.