一种社会网络搜索免疫优化算法

基于社会网络所表现出的强大的信息搜索和传播能力，提出了一种新颖的免疫优化算法--社会网络搜索免疫优化算法．该算法将优化问题的求解看作是信息的传递过程，利用经典社会网络搜索模型即Kleinberg网络模型的建模方法来构造免疫算法的寻优进化过程．通过网络的结构增长机制，分别由短程连接算子和长程连接算子来引入抗体种群中的新个体．当搜索进行到一定程度时，自适应地调整长程连接搜索概率，避免算法陷入局部极值，能够最终找到目标的最优解．短程连接算子和长程连接算子的引入充分利用了抗体种群的结构信息，加快了种群收敛速度，同时降低了算法陷入局部极值点的概率．通过对复杂函数优化问题的测试、理论分析及实验结果表明，与粒子群算法、克隆选择算法等已有算法相比，新算法可以更好地保持解的多样性，收敛速度快，求解精度高，鲁棒性强．

Immune optimization algorithm based on the social network searching model

Based on the effective information searching and transmission ability of the social network, a novel immune optimization algorithm, named the Immune Optimization Algorithm based on the Social Network Searching Model (SNSIA), is proposed. The new algorithm considers the settling of optimization problem as the process of information transmission. It constructs the evolutionary process by the modeling method of classical social network searching model known as the Kleinberg network model. The new individual is introduced into the antibody population via short-range and long-range connections with the aid of the network's structure growth mechanism. The probability of long-range connection would adjust adaptively when the searching reaches a certain degree, which would avoid the local optimum effectively and find the global optimum at last. The new algorithm introduces the short-range and long-range connections which take full advantage of the population's structure info, and it quickens the speed of convergence. At the same time, it brings down the probability of getting in the local optima. In experiments, the SNSIA is tested on the complex functions and compared with the particle swarm algorithm, the clonal selection algorithm and other optimization methods. Theoretical analysis and experimental results indicate that the SNSIA could maintain the solution's diversity better with a high convergence rate, and that it is also an effective and robust technique for optimization.