A compound algorithm of genetic annealing is designed for optimizing the luffing mechanism locus of a plane link by means of random optimal algorithm, genetic and annealing algorithm. The computing experiment shows th...A compound algorithm of genetic annealing is designed for optimizing the luffing mechanism locus of a plane link by means of random optimal algorithm, genetic and annealing algorithm. The computing experiment shows that the algorithm has much better steady convergence performance of optimal process and can hunt out the global optimal solution by biggish probability for objective function of multi peak value.展开更多
Degenerative disc disease is a multifaceted progressive irreversible condition and an inevitable part of aging,which has been found to be a contributing factor for low back pain and might cause radiculopathy,myelopath...Degenerative disc disease is a multifaceted progressive irreversible condition and an inevitable part of aging,which has been found to be a contributing factor for low back pain and might cause radiculopathy,myelopathy,spinal stenosis,degenerative spondylolisthesis,and herniations.Its etiology is complex and multifactorial.Although genetics influence more dominant,the occupational and mechanical influences still persist as a major risk factor.This review emphasizes up-to-date knowledge regarding etiology of disc degeneration with special consideration on occupational,lifestyle factors,and genetic polymorphisms.展开更多
Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it...Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it avoids converging to the local optimum. However, it takes too much CPU time in the late process of GA. On the other hand, in the late process Simulated Annealing (SA) converges faster than GA but it is easily trapped to local optimum. In this letter, a useful method that unifies GA and SA is introduced, which utilizes the advantage of the global search ability of GA and fast convergence of SA. The experimental results show that the proposed algorithm outperforms GA in terms of CPU time without degradation of performance. It also achieves highly comparable placement cost compared to the state-of-the-art results obtained by Versatile Place and Route (VPR) Tool.展开更多
准确的风功率预测对电力系统安全、稳定运行具有重要意义,而风速预测是风功率预测的关键。文章提出一种基于优化模糊C均值(Optimal Fuzzy C means,OFCM)聚类的组合风速短期预测方法。首先,采用模拟退火遗传算法优化模糊C均值聚类算法的...准确的风功率预测对电力系统安全、稳定运行具有重要意义,而风速预测是风功率预测的关键。文章提出一种基于优化模糊C均值(Optimal Fuzzy C means,OFCM)聚类的组合风速短期预测方法。首先,采用模拟退火遗传算法优化模糊C均值聚类算法的初始聚类中心;其次,基于优化模糊C均值聚类算法将初始风速属性样本数据进行分组;再根据不同风速样本组,运用极限学习机(Extremely Learning Machine,ELM)构建组合风速预测模型;最后,通过风速实测值与预测值的对比,验证了该方法的可行性。展开更多
The component-based business architecture integration of military information systems is a popu- lar research topic in the field of military operational research. Identifying enterprise-level business components is an...The component-based business architecture integration of military information systems is a popu- lar research topic in the field of military operational research. Identifying enterprise-level business components is an important issue in business architecture integration. Currently used methodologies for business component identification tend to focus on software-level business components, and ignore such enterprise concerns in business architectures as organizations and resources. Moreover, approaches to enterprise-level business component identi- fication have proven laborious. In this study, we propose a novel approach to enterprise-level business component identification by considering overall cohesion, coupling, granularity, maintainability, and reusability. We first define and formulate enterprise-level business components based on the component business model and the Department of Defense Architecture Framework （DoDAF） models. To quantify the indices of business components, we formulate a create, read, update, and delete （CRUD） matrix and use six metrics as criteria. We then formulate business com- ponent identification as a multi：objective optimization problem and solve it by a novel meta-heuristic optimization algorithm called the ＇simulated annealing hybrid genetic algorithm （SHGA）＇. Case studies showed that our approach is more practical and efficient for enterprise-level business component identification than prevalent approaches.展开更多
文摘A compound algorithm of genetic annealing is designed for optimizing the luffing mechanism locus of a plane link by means of random optimal algorithm, genetic and annealing algorithm. The computing experiment shows that the algorithm has much better steady convergence performance of optimal process and can hunt out the global optimal solution by biggish probability for objective function of multi peak value.
文摘Degenerative disc disease is a multifaceted progressive irreversible condition and an inevitable part of aging,which has been found to be a contributing factor for low back pain and might cause radiculopathy,myelopathy,spinal stenosis,degenerative spondylolisthesis,and herniations.Its etiology is complex and multifactorial.Although genetics influence more dominant,the occupational and mechanical influences still persist as a major risk factor.This review emphasizes up-to-date knowledge regarding etiology of disc degeneration with special consideration on occupational,lifestyle factors,and genetic polymorphisms.
基金Supported by School of Engineering, Napier University, United Kingdom, and partially supported by the National Natural Science Foundation of China (No.60273093).
文摘Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it avoids converging to the local optimum. However, it takes too much CPU time in the late process of GA. On the other hand, in the late process Simulated Annealing (SA) converges faster than GA but it is easily trapped to local optimum. In this letter, a useful method that unifies GA and SA is introduced, which utilizes the advantage of the global search ability of GA and fast convergence of SA. The experimental results show that the proposed algorithm outperforms GA in terms of CPU time without degradation of performance. It also achieves highly comparable placement cost compared to the state-of-the-art results obtained by Versatile Place and Route (VPR) Tool.
基金Project supported by the National.Natural Science Foundation of China （No. 71571189）
文摘The component-based business architecture integration of military information systems is a popu- lar research topic in the field of military operational research. Identifying enterprise-level business components is an important issue in business architecture integration. Currently used methodologies for business component identification tend to focus on software-level business components, and ignore such enterprise concerns in business architectures as organizations and resources. Moreover, approaches to enterprise-level business component identi- fication have proven laborious. In this study, we propose a novel approach to enterprise-level business component identification by considering overall cohesion, coupling, granularity, maintainability, and reusability. We first define and formulate enterprise-level business components based on the component business model and the Department of Defense Architecture Framework （DoDAF） models. To quantify the indices of business components, we formulate a create, read, update, and delete （CRUD） matrix and use six metrics as criteria. We then formulate business com- ponent identification as a multi：objective optimization problem and solve it by a novel meta-heuristic optimization algorithm called the ＇simulated annealing hybrid genetic algorithm （SHGA）＇. Case studies showed that our approach is more practical and efficient for enterprise-level business component identification than prevalent approaches.
