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利用免疫系统的免疫记忆机制,提出一种适于函数优化的基于变异记忆矩阵的克隆选择算法.首先,利用变异记忆矩阵保存进化中有用的变异信息,以引导抗体的克隆和变异操作,加强局部搜索能力;然后,利用当代种群的综合信息生成新抗体进入种群,以加强全局搜索能力;最后,对最优抗体进行自学习,以提高算法结果的精度.标准函数仿真表明,该算法适合求解复杂函数优化问题,具有收敛速度快、全局收敛能力强、精度高、鲁棒性强的优点.
Using the immune system of immune system, a clonal selection algorithm based on mutation memory matrix is proposed, which is suitable for function optimization.First, the use of mutation memory matrix to save the evolutionary useful mutation information to guide the antibody cloning and mutation operation, strengthen the local Then the new antibody is generated into the population by using the comprehensive information of the contemporary population to strengthen the global search ability.Finally, the optimal antibody is self-learning to improve the accuracy of the algorithm results.The standard function simulation shows that the algorithm is suitable for solving complex Function optimization problems, with the advantages of fast convergence, strong global convergence, high precision and robustness.