An Improved Adaptive Immune Genetic Algorithm Based on Information Entropy

Xingxing Liu, Zhichao Yang, Wang Pan
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引用次数: 2

Abstract

The promotion on search efficacy of immune genetic algorithm is an enduring issue. An adaptive immune genetic algorithm based on information entropy is proposed. The parameters of similarity and affinity are designed based on information entropy. It integrated density control, improved crossover and mutation. The algorithm can make better use of global and local information and differences between antibodies for diversity control. The adjustments improve the speed, accuracy and convergence stability. Simulation results on multimodal function show that the proposed method has better optimization capability.
基于信息熵的改进自适应免疫遗传算法
提高免疫遗传算法的搜索效率是一个长期存在的问题。提出了一种基于信息熵的自适应免疫遗传算法。基于信息熵设计了相似度和亲和度参数。它集成了密度控制,改进了交叉和变异。该算法可以更好地利用全局和局部信息以及抗体之间的差异进行多样性控制。这些调整提高了速度、精度和收敛稳定性。对多模态函数的仿真结果表明,该方法具有较好的优化能力。
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