带免疫机制的蚁群算法求解几何约束多解

Combining Immune with Ant Colony Algorithm for Geometric Constraint Multi-Solution Problem

针对蚁群优化算法易于陷入早熟收敛和局部求精能力不足的缺点,从人工免疫系统的基本原理出发,设计具有免疫能力的蚂蚁抗体来保持蚁群的多样性,使算法在迭代的后期依然保持进化能力,提高算法的局部求精能力,使蚁群优化算法在局部开采和全局探索间取得更好的平衡。算法具有良好的优化性能和时间性能。

The constraint multi-solutions problem can be transformed to an optimization problem. It is proposed to introduce IACOA （Immune Ant Colony Optimization Algorithm） in finding optimal solution. The solution for preventing ACO from premature convergence and improving the precision of local optimization algorithm is to develop ant operators with immunity based on the basic Principles of artificial immune systems. The immune ant operators will create better balance between exploration and exploitation by keeping the diversity of ant colony, maintaining the intensification in the later iteration phase, and improving the precision of local optimization algorithm. The algorithm has good effect in optimization capability and time capability.