基于遗传变异特性的异类多种群蚁群优化算法研究

Heterogeneous multiple ant colonies algorithm based on genetic mutation features

遗传变异特性的异类多种群蚁群优化算法由多种不同寻优机制的蚂蚁群体构成,不同群体之间协同进化优势互补。遗传变异思想的融入,使之能在局部和全局之间达到平衡,从而保有跳离局部最优的能力。在不同数据类型TSP问题上的仿真实验表明该算法具有较好的寻优能力,对某些问题实例具有明显优势。

Based on genetic mutation features, a Heterogeneous Multiple Ant Colonies Algorithm is proposed. This algorithm introduces more than one type of ant colonies, each with different optimization mechanisms and complementary advantages while in the co-evolution. In order to find a balance between local and global search, the algo- rithm incorporates the idea of genetic mutation, which can help to skip the local optimum. Some simulations for TSP problems with different data types show that the proposed algorithm has better optimization capabilities, and has significant advantages in some instances.