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Analysis and Prevention of the Realistic Threats of Bioterrorism
Li Heng(Heng Li),Zhou Shenglong(Shenglong Zhou),Hu Shuangqing(Shuangqing Hu) 아시아사회과학학회 2022 International Science Research Vol.2 No.3
At present, the abuse of biotechnology achievements is gradually escalating, and it has become the most urgent emerging biological threat source. The biosafety risks in laboratories are constantly escalating, and the procurement, transportation, transfer and research of virus samples often violate operational norms, posing a serious threat to public safety. From the perspective of the environment faced by China, the situation of anti-bioterrorism threat is not optimistic. There are still obvious shortcomings in internal risk control such as pathogenic microorganism laboratory safety. External risks such as biological virus transmission, biological weapon development, bioterrorism attack and biotechnology abuse continue to intensify, and new risk factors such as loss of biological genetic resources are making waves. All countries in the world should join hands to deal with the threat of bioterrorism.
Diversity-Guided Dynamic Step Firefly Algorithm
Shuhao Yu,Shenglong Zhu,Renjin Liu,Xiancun Zhou 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.8
Firefly Algorithm is a nature-inspired optimization method, which has been shown to implement well on numerous optimization problems. But it can easily fall into the local optima and low precision. Therefore, it is very important to overcome these defects. In this paper, we use a dynamic strategy for step setting, which takes into account the population diversity of fireflies. The experiments show that the proposed algorithm improves the performance of original firefly algorithm.
An Improved Firefly Algorithm Based on Nonlinear Time-varying Step-size
Shuhao Yu,Shenglong Zhu,Xiancun Zhou 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.7
Firefly algorithm (FA) is a novel population-based stochastic optimization algorithm and has been shown to yield good performance for solving varieties of optimization problems. Meanwhile, it sustains premature convergence because it is easily to fall into the local optima which may generate a low accuracy of solution or even fail. To overcome this defect, a nonlinear time-varying step strategy for firefly algorithm (NTSFA) is presented. It uses a nonlinear decreasing and time-varying step-size for fireflies to better balance the algorithm’s ability of exploration and exploitation. Numerical simulation on 20 test benchmark functions display that the proposed algorithm can increase the accuracy of the original FA. Finally, we apply NTSFA to integrate into k-means clustering for mouse dataset. The results show that NTSFA is an effective optimization algorithm.