针对约束优化问题提出一种基于精英库机制的改进型免疫克隆优化算法ICOAEB(Immune clonal optimization algorithm based on elite bank)。该算法利用精英库机制动态存储迭代过程中父代优势个体,实现优秀个体的多代记忆,从而提高算法寻...针对约束优化问题提出一种基于精英库机制的改进型免疫克隆优化算法ICOAEB(Immune clonal optimization algorithm based on elite bank)。该算法利用精英库机制动态存储迭代过程中父代优势个体,实现优秀个体的多代记忆,从而提高算法寻优能力;并利用灾变算子扰动算法运行过程从而摆脱迭代缓慢的状态,避免局部收敛。通过对五个约束优化函数的测试,实验结果表明ICOAEB的求解精度和稳定性较高,可以较好地解决约束优化问题。最后针对影响算法性能的两项重要参数选择问题给出了相关的实验及分析。展开更多
Combining the clonal selection mechanism of the immune system with the evolution equations of particle swarm optimization, an advanced algorithm was introduced for functions optimization. The advantages of this algori...Combining the clonal selection mechanism of the immune system with the evolution equations of particle swarm optimization, an advanced algorithm was introduced for functions optimization. The advantages of this algorithm lies in two aspects. Via immunity operation, the diversity of the antibodies was maintained, and the speed of convergent was improved by using particle swarm evolution equations. Simulation programme and three functions were used to check the effect of the algorithm. The advanced algorithm were compared with clonal selection algorithm and particle swarm algorithm. The results show that this advanced algorithm can converge to the global optimum at a great rate in a given range, the performance of optimization is improved effectively.展开更多
Objective： To optimize the ELISA for the determination of tetrodotoxin. Methods： A competitive enzyme-linked immunosorbent assay （ELISA） was used. In the ELISA, 100 μl antigen （1. 0 μg/ml） was coated on the mi...Objective： To optimize the ELISA for the determination of tetrodotoxin. Methods： A competitive enzyme-linked immunosorbent assay （ELISA） was used. In the ELISA, 100 μl antigen （1. 0 μg/ml） was coated on the microtiter plate for 60 min at 37 C or over night at 4 C. The plate was then washed 3 times with PBS-T for 3-5 s each time. The optimal incubation time for monoclonal antibody （mAb）, goat anti-mice IgG peroxidase conjugate and OPD were 30 min, 20 min and 10 min at 37 C, re- spectively. Results.. The detection limit is 0. 05 ng in each well. The curve was linear for TTX doses be- tween 5-5 000 ng/ml （0. 25-250 ng for every assay）. The linear regress equation was Y = 0. 30 88X-0.17 41 （R=0.99 01）. The average callback for TTX of muscles and gonads were 99.74% and 100.30%, respectively. The sensitivity of optimization ELISA was 5 times than traditional method and the time of 1.8 h were saved. Conclusion： The optimized ELISA is an ideal method for the determination of tetrodotoxin.展开更多
A self-adaptive learning based immune algorithm （SALIA） is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned prc,blems with the high robustness. The SALIA algorithm ad...A self-adaptive learning based immune algorithm （SALIA） is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned prc,blems with the high robustness. The SALIA algorithm adopted a mutation strategy pool which consists of four effective mutation strategies to generate new antibodies. A self-adaptive learning framework is implemented to select the mutation strategies by learning from their previous performances in generating promising solutions. Twenty-six state-of-the-art optimization problems with different characteristics, such as uni-modality, multi-modality, rotation, ill-condition, mis-scale and noise, are used to verify the validity of SALIA. Experimental results show that the novel algorithm SALIA achieves a higher universality and robustness than clonal selection algorithms （CLONALG）, and the mean error index of each test function in SALIA decreases by a factor of at least 1.0×10^7 in average.展开更多
文摘针对约束优化问题提出一种基于精英库机制的改进型免疫克隆优化算法ICOAEB(Immune clonal optimization algorithm based on elite bank)。该算法利用精英库机制动态存储迭代过程中父代优势个体,实现优秀个体的多代记忆,从而提高算法寻优能力;并利用灾变算子扰动算法运行过程从而摆脱迭代缓慢的状态,避免局部收敛。通过对五个约束优化函数的测试,实验结果表明ICOAEB的求解精度和稳定性较高,可以较好地解决约束优化问题。最后针对影响算法性能的两项重要参数选择问题给出了相关的实验及分析。
基金Project(A1420060159) supported by the National Basic Research of China projects(60234030, 60404021) supported by the National Natural Science Foundation of China
文摘Combining the clonal selection mechanism of the immune system with the evolution equations of particle swarm optimization, an advanced algorithm was introduced for functions optimization. The advantages of this algorithm lies in two aspects. Via immunity operation, the diversity of the antibodies was maintained, and the speed of convergent was improved by using particle swarm evolution equations. Simulation programme and three functions were used to check the effect of the algorithm. The advanced algorithm were compared with clonal selection algorithm and particle swarm algorithm. The results show that this advanced algorithm can converge to the global optimum at a great rate in a given range, the performance of optimization is improved effectively.
基金the grants from PhD Priming Foundation of Jilin University(430505010276)
文摘Objective： To optimize the ELISA for the determination of tetrodotoxin. Methods： A competitive enzyme-linked immunosorbent assay （ELISA） was used. In the ELISA, 100 μl antigen （1. 0 μg/ml） was coated on the microtiter plate for 60 min at 37 C or over night at 4 C. The plate was then washed 3 times with PBS-T for 3-5 s each time. The optimal incubation time for monoclonal antibody （mAb）, goat anti-mice IgG peroxidase conjugate and OPD were 30 min, 20 min and 10 min at 37 C, re- spectively. Results.. The detection limit is 0. 05 ng in each well. The curve was linear for TTX doses be- tween 5-5 000 ng/ml （0. 25-250 ng for every assay）. The linear regress equation was Y = 0. 30 88X-0.17 41 （R=0.99 01）. The average callback for TTX of muscles and gonads were 99.74% and 100.30%, respectively. The sensitivity of optimization ELISA was 5 times than traditional method and the time of 1.8 h were saved. Conclusion： The optimized ELISA is an ideal method for the determination of tetrodotoxin.
基金Project(2010ZC13012) supported by the Aviation Science Funds of China
文摘A self-adaptive learning based immune algorithm （SALIA） is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned prc,blems with the high robustness. The SALIA algorithm adopted a mutation strategy pool which consists of four effective mutation strategies to generate new antibodies. A self-adaptive learning framework is implemented to select the mutation strategies by learning from their previous performances in generating promising solutions. Twenty-six state-of-the-art optimization problems with different characteristics, such as uni-modality, multi-modality, rotation, ill-condition, mis-scale and noise, are used to verify the validity of SALIA. Experimental results show that the novel algorithm SALIA achieves a higher universality and robustness than clonal selection algorithms （CLONALG）, and the mean error index of each test function in SALIA decreases by a factor of at least 1.0×10^7 in average.
