Nowadays, the vein based recognition system becomes an emerging and facilitating biometric technology in the recognition system. Vein recognition exploits the different modalities such as finger, palm and hand image f...Nowadays, the vein based recognition system becomes an emerging and facilitating biometric technology in the recognition system. Vein recognition exploits the different modalities such as finger, palm and hand image for the person identification. In this work, the fuzzy least brain storm optimization and Euclidean distance(EED) are proposed for the vein based recognition system. Initially, the input image is fed into the region of interest(ROI) extraction which obtains the appropriate image for the subsequent step. Then, features or vein pattern is extracted by the image enlightening, circular averaging filter and holoentropy based thresholding. After the features are obtained, the entropy based Euclidean distance is proposed to fuse the features by the score level fusion with the weight score value. Finally, the optimal matching score is computed iteratively by the newly developed fuzzy least brain storm optimization(FLBSO) algorithm. The novel algorithm is developed by the least mean square(LMS) algorithm and fuzzy brain storm optimization(FBSO). Thus, the experimental results are evaluated and the performance is compared with the existing systems using false acceptance rate(FAR), false rejection rate(FRR) and accuracy. The performance outcome of the proposed algorithm attains the higher accuracy of 89.9% which ensures the better recognition rate.展开更多
针对工业生产中铝挤压生产线存在的工序繁杂、排产量大等导致的生产工期较长、效率低等问题,建立了铝挤压生产线的时延Petri网(timed Petri net,TdPN)模型并进行合理性分析;将头脑风暴优化算法(brain storm optimization,BSO)引入TdPN模...针对工业生产中铝挤压生产线存在的工序繁杂、排产量大等导致的生产工期较长、效率低等问题,建立了铝挤压生产线的时延Petri网(timed Petri net,TdPN)模型并进行合理性分析;将头脑风暴优化算法(brain storm optimization,BSO)引入TdPN模型,提出了基于变迁序列个体编码解码方式的铝挤压排产调度问题优化调度算法。算法中采用模拟退火局部搜索机制改善BSO算法在后期的寻优性能,实现最小化批次完工时间的排产调度目标;仿真结果表明该方法能够缩短生产线排产工期提高生产效率,为工业生产排产调度问题提供了新的解决方法。展开更多
作为一种新型的群体智能优化算法,头脑风暴优化(brain storm optimization,BSO)算法一经提出便引起了众多研究者的关注.本文在对原始头脑风暴算法的聚类操作和变异操作改进的基础上,提出了基于目标空间聚类的差分头脑风暴(difference br...作为一种新型的群体智能优化算法,头脑风暴优化(brain storm optimization,BSO)算法一经提出便引起了众多研究者的关注.本文在对原始头脑风暴算法的聚类操作和变异操作改进的基础上,提出了基于目标空间聚类的差分头脑风暴(difference brain storm optimization based on clustering in objective space,DBSO–OS)算法.算法通过对目标空间的聚类替代对决策空间的聚类,减小了算法的运算复杂度;采用差分变异代替高斯变异来增加种群的多样性.多个测试函数的仿真结果表明,目标空间聚类的差分头脑风暴算法不仅提高了算法的寻优速度,而且提高了算法的寻优精度.文中进一步分析了参数对算法性能的影响,设计了最佳参数选择方案,并用于对实际热电联供经济调度问题的求解,验证了算法的实用性.展开更多
文摘Nowadays, the vein based recognition system becomes an emerging and facilitating biometric technology in the recognition system. Vein recognition exploits the different modalities such as finger, palm and hand image for the person identification. In this work, the fuzzy least brain storm optimization and Euclidean distance(EED) are proposed for the vein based recognition system. Initially, the input image is fed into the region of interest(ROI) extraction which obtains the appropriate image for the subsequent step. Then, features or vein pattern is extracted by the image enlightening, circular averaging filter and holoentropy based thresholding. After the features are obtained, the entropy based Euclidean distance is proposed to fuse the features by the score level fusion with the weight score value. Finally, the optimal matching score is computed iteratively by the newly developed fuzzy least brain storm optimization(FLBSO) algorithm. The novel algorithm is developed by the least mean square(LMS) algorithm and fuzzy brain storm optimization(FBSO). Thus, the experimental results are evaluated and the performance is compared with the existing systems using false acceptance rate(FAR), false rejection rate(FRR) and accuracy. The performance outcome of the proposed algorithm attains the higher accuracy of 89.9% which ensures the better recognition rate.
文摘针对工业生产中铝挤压生产线存在的工序繁杂、排产量大等导致的生产工期较长、效率低等问题,建立了铝挤压生产线的时延Petri网(timed Petri net,TdPN)模型并进行合理性分析;将头脑风暴优化算法(brain storm optimization,BSO)引入TdPN模型,提出了基于变迁序列个体编码解码方式的铝挤压排产调度问题优化调度算法。算法中采用模拟退火局部搜索机制改善BSO算法在后期的寻优性能,实现最小化批次完工时间的排产调度目标;仿真结果表明该方法能够缩短生产线排产工期提高生产效率,为工业生产排产调度问题提供了新的解决方法。
文摘作为一种新型的群体智能优化算法,头脑风暴优化(brain storm optimization,BSO)算法一经提出便引起了众多研究者的关注.本文在对原始头脑风暴算法的聚类操作和变异操作改进的基础上,提出了基于目标空间聚类的差分头脑风暴(difference brain storm optimization based on clustering in objective space,DBSO–OS)算法.算法通过对目标空间的聚类替代对决策空间的聚类,减小了算法的运算复杂度;采用差分变异代替高斯变异来增加种群的多样性.多个测试函数的仿真结果表明,目标空间聚类的差分头脑风暴算法不仅提高了算法的寻优速度,而且提高了算法的寻优精度.文中进一步分析了参数对算法性能的影响,设计了最佳参数选择方案,并用于对实际热电联供经济调度问题的求解,验证了算法的实用性.
