基于平衡峰值和梯度进化策略的多模态免疫算法

Multi-Modal Immune Algorithm Based on Peaks Poised and Gradient Evolution Strategies

通过考察现有的多模态优化算法。指出其存在的不足,并根据它们对峰值等高函数搜索效果较好,而对峰值不等高函数效果较差的共同特点,提出评价函数的平衡峰值策略并加以实现.基于免疫系统的抗体进化机制,集成传统的梯度进化思想,设计一种新的多模态免疫算法(MIA).给出算法主要操作算子的具体实现,并分析其运行机理、完全收敛性和计算复杂性.通过仿真实验,验证算法求解多模态问题,特别是求解具有不等高多峰函数的有效性、完全收敛性及快速收敛能力.

Some available multi-modal optimization algorithms are analyzed and the faults of them are pointed out . Based on their same features that they all have fine search effect to functions with equivalenee peaks, the peaks poised strategy is proposed. Then a new Multi-modal Immune Algorithm （ MIA ） with mechanisms of antibody evolution in immune system and conventional gradient evolution is designed. The implementations of peaks poised strategy and main evolution operators are given, the algorithm＇s operating mechanisms, complete convergence and computation complicacy are analyzed . The simulation experiments are performed and the results testify that MIA has availability on solving multi-modal optimization problems, especially for functions with non-equivalence peaks, complete convergence and quickly convergence ability.