基于免疫学习机制的遗传算法及其应用

针对基本遗传算法在进化后期收敛速度慢、易早熟收敛的问题，提出一种基于免疫学习机制的遗传算法(ILGA)．该算法的核心在于保持种群的多样性和执行强化学习及弱小保护策略，算法不仅保持了优良抗体在进化中的主导地位，而且充分发掘强成长性抗体的寻优潜力，在优良记忆库的作用下，算法对全局最优的搜索快速且有效．通过标准函数的优化试验，仿真结果表明该算法有较强的全局收敛能力和较快的收敛速度．以二级倒立摆为被控对象，利用ILGA优化T S模糊神经网络控制器，实验证明了该方法具有稳态性好、响应速度快等优点．

A Genetic Algorithm Based on Immune Learning Mechanism and Its Application

Simple genetic algorithm has a slow convergence velocity in late evolution and gets premature con-vergence easily.To solve these problems,an immune learning based genetic algorithm(ILGA) is proposed.The key to the algorithm lies in maintaining diversity of the population and executing the strategy of reinforcement learning and immature protection.The algorithm not only keeps the leading position of excellent antibody,but also develops the potential of rapidly growing antibody in seeking optimum.Under the action of excellent memory cell,the search of algorithm to global optimum is rapid and effective.Simulation results show that the algorithm has better global con-vergence ability and rapider convergence velocity through optimization tests of benchmark functions.The T-S fuzzy neural network controller is optimized by ILGA on a double inverted pendulum.Experiment results demonstrate that the method has ideal steady performance and quick response speed.