基于Elitism的改进免疫遗传算法应用研究

Study on Improved Immune-genetic Algorithm Based on the Elitism

研究遗传算法等经典算法存在早熟、收敛速度慢等问题,针对上述问题,提出了新的抗体相似度、期望繁殖率以及克隆选择概率的定义及算法,结合Elitism策略提出了免疫遗传算法并建立了数学模型。抗体的相似度和期望繁殖率在进化过程中可以动态调整,以平衡群体的多样性和算法的收敛速度,采用了Elitism策略,保证算法收敛到全局最优解,选用PID控制进行仿真实验,通过与其他经典算法比较,结果表明算法具有一定的可行性。

GA has some drawbacks such as easily getting trapped and the convergence speed is slow. Aiming at these problems, a new definitions and formulas of antibody similarity, expected reproduction probability, and selection probability are proposed. Based on these definitions and the elitism strategy, a novel immune - genetic algorithm is presented, which is called the immune genetic algorithm with elitism （IGAE）. IGAE has two important properties. The similarity and expected reproduction probability of antibody can be adjusted dynamically in the evolutionary process of the antibody population to balance the diversity of the population and the convergence speed of the algo- rithm, which helps the algorithm find the high quality solutions rapidly. The algorithm is able to find the globally optimal solution because of the use of elitism strategy. Based on the PID control, compared with other algorithms, it shows that this method is feasibility.