融合免疫机制的协同进化模型

针对传统进化算法在计算效能方面存在的一些问题,借鉴协同进化算法的思想,提出了一种融合免疫机制的协同进化模型。该模型通过多个子种群各自分别进化以保持整个种群的多样性。在每次迭代进化过程中,各个子种群分别选择精英抗体并进行免疫记忆。随后各个子种群分别以不同的算法进行变异。若变异后抗体的适应度降低,则利用精英抗体对其进行引导操作。群体间的协作包括子种群间若干个抗体的随机交叉和子种群间的大规模迁移。最终进行免疫代谢,去除群中的弱适应度个体。算法反复迭代进行以上操作,直至达到既定目标或预定的循环迭代次数。通过对13个标准测试函数进行的仿真实验显示,该模型在搜索最优解或满意解时均优于传统的进化算法,同时在寻优效率上有较大的提升。

A co-evolution model integrated with an immune mechanism

To solve the problems of traditional evolutionary algorithms in computational efficiency,a co-evolution model integrated with an immune mechanism was proposed by referring the idea of co-evolution algorithm.The model maintained the diversity of a population through the respective evolution of multiple sub-populations.During the evolution in each iteration,each sub-population selected the elite antibodies individually and carried out the immune memory operation.Then every sub-population independently mutated with a variety of the algorithm.If the mutation reduced the fitness of the antibody,the antibody was guided by the elite ones.Group collaboration included randomized crossover of a number of individual between sub-populations and large-scale migration among sub-populations.Final the immune metabolism operation removed the weak antibodies in the population.The above operations were repeated until the algorithm reached the established goals or intended loop iterations.Simulation experiments with 13 benchmark functions showed that the optimal solution or satisfactory solution of the model obtained from the search was better than traditional evolutionary algorithms,and its optimization efficiency was also greatly improved.