一种基于免疫和Hopfield神经网络的多峰值优化算法

分析了免疫算法和Hopfield神经网络的优缺点，提出了一种解决多峰值函数优化问题的混合算法。Hopfield神经网络易于硬件实现，具有简单、快速的优点，但是对初始值具有依赖性以及容易陷入局部极值。免疫算法具有识别多样性的特点，但搜索效率和精度不高。将两算法结合起来，优势互补。首先用免疫算法寻优，然后对所得具有全局多样性的解进行聚类分析，所得聚类中心作为Hopfield神经网络的初始搜索点，最后利用Hopfield神经网络逐个寻优。实验表明，该算法是一种有效的求解多峰函数优化问题的方法，与免疫算法相比，搜索效率和精度都较高。

New algorithm for multimodal function optimization based on immune algorithm and Hopfield neural network

Based on the analysis of immune theory and Hopfield neural network(HNN),a new algorithm for multimodal function optimization was proposed.This new algorithm had the advantages of both HNN and immune algorithm(IA),and it showed excellent characteristics in optimization of multimodal function.In detail,a group of solutions with variety were taken by IA first;and then the solutions were partitioned into some clusters.Finally cluster centroids returned by clustering algorithm were taken as the initial value of each HNN,and Hopfield neural networks were used to obtain all minima.The experiments show that this new algorithm is an effective method for multimodal function optimization.Compared to immune algorithm,the efficiency and precision are both improved.