基于免疫遗传算法的负载均衡策略

针对基于遗传算法的负载均衡策略(SGALB)搜索效率不高、局部寻优性能不佳、容易产生退化的问题，提出一种基于免疫遗传算法的负载均衡策略(IGALB)。通过在SGALB基础上对种群进行亲和力和浓度计算，增加基于浓度的调节概率因子，确保种群的多样性，克服了SGALB早熟收敛；同时在一定条件下引入免疫算子，进行接种疫苗和免疫选择，有效缓解了SGALB的退化现象。仿真实验表明，该算法的寻优能力高于SGALB，并有效提高了集群系统的性能。

Load balancing strategy based on immune genetic algorithm

Load Balancing Based on Immune Genetic Algorithm (IGALB) was proposed to improve the efficiency of search quality and the poor performance of local search of the load balancing strategy based on simple genetic algorithm (SGALB). IGALB ensured the diversity of population and overcame the premature convergence of SGALB by carrying out the affinity and concentration calculations and increasing the probability adjustment factor based on the concentration. Meanwhile, the degradation of SGALB was effectively alleviated by introducing immune operator and carrying out the vaccination and immune selection under certain conditions. The simulations show that the search ability of IGALB algorithm is higher than that of SGALB algorithm, and it can improve the performance of cluster system effectively.