共查询到19条相似文献,搜索用时 187 毫秒
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基于克隆选择原理,引入混沌机制和小生境技术,提出一种改进型克隆选择算法(ICSA).该算法比传统的克隆选择算法具有更好的种群多样性和全局寻优能力.以随机过程理论为数学工具,分析了ICSA所形成抗体种群的平均适应度函数的鞅性质,并由此得出算法几乎处处强收敛性的结论.进而证明了,当状态空间有限时,该算法能在有限步内以概率1收敛到全局最优.仿真实验表明,该算法能有效地抑制早熟,具有更好的全局收敛性. 相似文献
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一种免疫记忆动态克隆策略算法 总被引:5,自引:0,他引:5
基于对克隆选择及免疫记忆动态过程的模拟,本文提出了一种人工智能算法,免疫记忆动态克隆策略算法,该算法模拟免疫系统的自我调节、记忆学习、自适应等机制,实现全局优化计算与局部优化计算机制的有机的结合,通过抗体与抗原的亲合度和抗体间亲合度的计算,促进和抑制抗体的产生,自适应地调节抗体群和记忆单元的克隆规模.理论分析证明该算法以概率1收敛,对多峰函数优化及货郎担问题的仿真试验表明,算法有效,而且具有全局搜索能力强,种群多样性好及收敛速度快等特点. 相似文献
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混沌免疫进化算法及其在函数优化中的应用 总被引:1,自引:0,他引:1
基于免疫系统的克隆选择机理,并利用混沌序列的遍历性,提出一种混沌免疫进化算法.算法首先将混沌序列引入算法初始群体的产生和抗体的扩展过程.其次将待扩展群体中的个体亲和度进行变换以调节个体的选择概率.最后利用概率分析方法,给出算法的全局收敛性证明.为了验证算法的有效性,将算法应用于函数优化问题.用不同的测试函数进行仿真实验.仿真结果表明该算法具有不易陷入局部最优、解的精度高、收敛速度快等优点. 相似文献
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一种克隆选择算法的收敛性分析 总被引:1,自引:0,他引:1
针对目前的免疫算法很少涉及分析其理论模型和收敛性的问题,就免疫算法中的一种克隆选择算法提出了该算法的收敛性分析。分析过程主要分为两步:首先利用马尔可夫链建立了这种克隆选择算法的马尔可夫模型,然后在此模型的基础上进一步分析了该算法的收敛性。分析结果从数学的角度证明了该算法是收敛的。为该算法进一步的完善、实用提供了一定的理论基础。 相似文献
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Based on the clonal selection theory and immune memory mechanism in the natural immune system, a novel artificial immune system algorithm, Clonal Strategy Algorithm based on the Immune Memory (CSAIM), is proposed in this paper. The algorithm realizes the evolution of antibody population and the evolution of memory unit at the same time, and by using clonal selection operator, the global optimal computation can be combined with the local searching. According to antibody-antibody (Ab-Ab) affinity and antibody-antigen (Ab-Ag) affinity, the algorithm can allot adaptively the scales of memory unit and antibody population. It is proved theoretically that CSAIM is convergent with probability 1. And with the computer simulations of eight benchmark functions and one instance of traveling salesman problem (TSP), it is shown that CSAIM has strong abilities in having high convergence speed, enhancing the diversity of the population and avoiding the premature convergence to some extent. 相似文献
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Inspired by the clonal selection theory together with the immune network model, we present a new artificial immune algorithm named the immune memory clonal algorithm (IMCA). The clonal operator, inspired by the immune system, is discussed first. The IMCA includes two versions based on different immune memory mechanisms; they are the adaptive immune memory clonal algorithm (AIMCA) and the immune memory clonal strategy (IMCS). In the AIMCA, the mutation rate and memory unit size of each antibody is adjusted dynamically. The IMCS realizes the evolution of both the antibody population and the memory unit at the same time. By using the clonal selection operator, global searching is effectively combined with local searching. According to the antibody-antibody (Ab-Ab) affinity and the antibody-antigen (Ab-Ag) affinity, The IMCA can adaptively allocate the scale of the memory units and the antibody population. In the experiments, 18 multimodal functions ranging in dimensionality from two, to one thousand and combinatorial optimization problems such as the traveling salesman and knapsack problems (KPs) are used to validate the performance of the IMCA. The computational cost per iteration is presented. Experimental results show that the IMCA has a high convergence speed and a strong ability in enhancing the diversity of the population and avoiding premature convergence to some degree. Theoretical roof is provided that the IMCA is convergent with probability 1. 相似文献
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Ruochen LIU Licheng JIAO Yangyang LI Jing LIU 《Frontiers of Computer Science in China》2010,4(4):536-559
Inspired by the clonal selection theory together with the immune network model, we present a new artificial immune algorithm
named the immune memory clonal algorithm (IMCA). The clonal operator, inspired by the immune system, is discussed first. The
IMCA includes two versions based on different immune memory mechanisms; they are the adaptive immune memory clonal algorithm
(AIMCA) and the immune memory clonal strategy (IMCS). In the AIMCA, the mutation rate and memory unit size of each antibody
is adjusted dynamically. The IMCS realizes the evolution of both the antibody population and the memory unit at the same time.
By using the clonal selection operator, global searching is effectively combined with local searching. According to the antibody-antibody
(Ab-Ab) affinity and the antibody-antigen (Ab-Ag) affinity, The IMCA can adaptively allocate the scale of the memory units
and the antibody population. In the experiments, 18 multimodal functions ranging in dimensionality from two, to one thousand
and combinatorial optimization problems such as the traveling salesman and knapsack problems (KPs) are used to validate the
performance of the IMCA. The computational cost per iteration is presented. Experimental results show that the IMCA has a
high convergence speed and a strong ability in enhancing the diversity of the population and avoiding premature convergence
to some degree. Theoretical roof is provided that the IMCA is convergent with probability 1. 相似文献
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克隆选择算法是基于免疫学中的克隆选择学说而产生的一种免疫优化算法。它通过克隆算子进行操作。本文首先介绍了标准的克隆选择算法;其次引入了克隆算子并对标准的克隆选择算法进行改进;然后以数列知识为基础,以抗体群的克隆选择过程为对象,对克隆选择算法的收敛性进行分析;最后应用区间套定理证明了算法的全局收敛性。 相似文献
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针对传统的克隆选择算法可能存在的早熟收敛现象和缺少交叉操作问题,提出一种高效的克隆退火优化算法.该算法结合了模拟退火算法与免疫系统的克隆选择机制,并保持全局搜索和局部搜索的平衡,可以有效提高算法的搜索效率,从而加快算法的收敛速度.同时,提出一种品质因数模型来分析该算法的动态性能,并运用Markov链理论对其收敛性进行分析.最后,将该算法应用到关联规则数据挖掘中,取得了较为理想的实验结果. 相似文献
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为了提高认知无线网络的参数优化效果,提出了一种基于免疫优化的认知引擎参数调整算法。免疫克隆优化是一种有效的智能优化算法,适合求解认知无线网络的引擎参数调整问题。免疫优化中,变异概率影响着算法的搜索能力;利用正态云模型云滴的随机性和稳定倾向性特点,提出了一种基于云模型的自适应变异概率调整方法,并用于认知无线网络的参数优化。在多载波环境下对算法进行了仿真实验。结果表明,所提算法收敛速度较快,参数调整结果与对目标函数的偏好一致,能够实现认知引擎参数优化。 相似文献
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受克隆选择过程生物学原理的启发, 提出了一种采用生物信息克隆的免疫算法. 抗体克隆依赖于一个动态平衡的网络, 并与遗传因素相关. 为了解决传统克隆过程中信息不能充分利用的问题, 该进化算法将环境信息、抗体历史信息以及抗体遗传特征积累的影响引入人工免疫系统, 用这多种信息作为先验知识为克隆过程提供决策支持, 引导抗体系统的更新. 同时采用实数与二进制混合编码方式增加种群多样性, 提高收敛速度, 然后分析了该算法的收敛性. 仿真实验结果表明, 该克隆策略能较大的提高免疫克隆算法的优化能力; 与几种高级免疫克隆算法和进化算法相比, 该算法寻优精度高, 收敛速度快, 能有效的克服早熟现象, 并具有很好的高维优化能力. 相似文献
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对多目标组合优化的组卷问题,借鉴生物免疫系统原理中抗体多样性产生及保持机理,定义多目标选择熵和浓度调节选择概率概念,利用自适应免疫遗传算法,运用抗体克隆、高变异策略,实现组卷问题的多目标优化。该算法充分体现了pareto最优解的概念,具有并行搜索及个体编码长度动态调整、pareto最优个体保存于群体外(免疫记忆)并不断更新等特点。 相似文献