This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satell...This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.展开更多
In order to overcome some defects of the traditional immune algorithm, the immune algorithm was improved for solving a path optimization problem in deep immune learning of a gene network. Firstly, the diversity of the...In order to overcome some defects of the traditional immune algorithm, the immune algorithm was improved for solving a path optimization problem in deep immune learning of a gene network. Firstly, the diversity of the solution population was enhanced in the evolution process by improving the memory cell processing method. Moreover, effective gene information was dynamically extracted from the genes of the excellent antibodies to make good vaccines in the process of immune evolution. Worse antibodies were optimized by vaccinating these antibodies, and the convergence of the immune algorithm to the optimal solution was improved. Finally, the feasibility of the improved immune algorithm was verified in the experimental simulation for solving the classic NP problem in deep immune learning of the gene network.展开更多
The settlement curve of the foundation endured the ramp load is an S-type curve,which is usually simulated via Poisson curve.Aimed at the difficulty of preferences in Poisson curve,an immune algorithm (IA) is used.IA ...The settlement curve of the foundation endured the ramp load is an S-type curve,which is usually simulated via Poisson curve.Aimed at the difficulty of preferences in Poisson curve,an immune algorithm (IA) is used.IA is able to obtain a multiple quasi-optimum solution while maintaining the population diversity.In this paper,IA is used in an attempt to obtain accurate settlement prediction.The predicted settlements obtained by IA are compared with those predicted by the least squares fitting method (LSM),the Asaoka method and the genetic algorithm (GA).The results show that IA is a useful technique for predicting the settlement of foundations with an acceptable degree of accuracy and has much better performance than GA and the Asaoka methods.展开更多
This paper presents a new method based on an immune-tabu hybrid algorithm to solve the thermal unit commitment （TUC） problem in power plant optimization. The mathematical model of the TUC problem is established by a...This paper presents a new method based on an immune-tabu hybrid algorithm to solve the thermal unit commitment （TUC） problem in power plant optimization. The mathematical model of the TUC problem is established by analyzing the generating units in modem power plants. A novel immune-tabu hybrid algorithm is proposed to solve this complex problem. In the algorithm, the objective function of the TUC problem is considered as an antigen and the solutions are considered as antibodies, which are determined by the affinity computation. The code length of an antibody is shortened by encoding the continuous operating time, and the optimum searching speed is improved. Each feasible individual in the immune algorithm （IA） is used as the initial solution of the tabu search （TS） algorithm after certain generations of IA iteration. As examples, the proposed method has been applied to several thermal unit systems for a period of 24 h. The computation results demonstrate the good global optimum searching performance of the proposed immune-tabu hybrid algorithm. The presented algorithm can also be used to solve other optimization problems in fields such as the chemical industry and the power industry.展开更多
Aiming at the solving problem of improved nonhomogeneous Poisson process( NHPP) model in engineering application,the immune clone maximum likelihood estimation( MLE)method for solving model parameters was proposed. Th...Aiming at the solving problem of improved nonhomogeneous Poisson process( NHPP) model in engineering application,the immune clone maximum likelihood estimation( MLE)method for solving model parameters was proposed. The minimum negative log-likelihood function was used as the objective function to optimize instead of using iterative method to solve complex system of equations,and the problem of parameter estimation of improved NHPP model was solved by immune clone algorithm. And the interval estimation of reliability indices was given by using fisher information matrix method and delta method. An example of failure truncated data from multiple numerical control( NC) machine tools was taken to prove the method. and the results show that the algorithm has a higher convergence rate and computational accuracy, which demonstrates the feasibility of the method.展开更多
基金supported by the National Natural Science Foundation of China(7127106671171065+1 种基金71202168)the Natural Science Foundation of Heilongjiang Province(GC13D506)
文摘This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.
文摘In order to overcome some defects of the traditional immune algorithm, the immune algorithm was improved for solving a path optimization problem in deep immune learning of a gene network. Firstly, the diversity of the solution population was enhanced in the evolution process by improving the memory cell processing method. Moreover, effective gene information was dynamically extracted from the genes of the excellent antibodies to make good vaccines in the process of immune evolution. Worse antibodies were optimized by vaccinating these antibodies, and the convergence of the immune algorithm to the optimal solution was improved. Finally, the feasibility of the improved immune algorithm was verified in the experimental simulation for solving the classic NP problem in deep immune learning of the gene network.
基金Project(No.NCET-06-0649)supported by the New Century Excel-lent Talents in University,China
文摘The settlement curve of the foundation endured the ramp load is an S-type curve,which is usually simulated via Poisson curve.Aimed at the difficulty of preferences in Poisson curve,an immune algorithm (IA) is used.IA is able to obtain a multiple quasi-optimum solution while maintaining the population diversity.In this paper,IA is used in an attempt to obtain accurate settlement prediction.The predicted settlements obtained by IA are compared with those predicted by the least squares fitting method (LSM),the Asaoka method and the genetic algorithm (GA).The results show that IA is a useful technique for predicting the settlement of foundations with an acceptable degree of accuracy and has much better performance than GA and the Asaoka methods.
基金Project partially supported by the Lamar Research Enhancement Grant and the National Science Foundation Grant (No. DUE-0737173) to Dr. W. Zhu at Lamar University
文摘This paper presents a new method based on an immune-tabu hybrid algorithm to solve the thermal unit commitment （TUC） problem in power plant optimization. The mathematical model of the TUC problem is established by analyzing the generating units in modem power plants. A novel immune-tabu hybrid algorithm is proposed to solve this complex problem. In the algorithm, the objective function of the TUC problem is considered as an antigen and the solutions are considered as antibodies, which are determined by the affinity computation. The code length of an antibody is shortened by encoding the continuous operating time, and the optimum searching speed is improved. Each feasible individual in the immune algorithm （IA） is used as the initial solution of the tabu search （TS） algorithm after certain generations of IA iteration. As examples, the proposed method has been applied to several thermal unit systems for a period of 24 h. The computation results demonstrate the good global optimum searching performance of the proposed immune-tabu hybrid algorithm. The presented algorithm can also be used to solve other optimization problems in fields such as the chemical industry and the power industry.
文摘为了解决当前研究中边坡稳定性预测模型计算量大且预测能力不佳的问题,提出了一种基于改进的主成分分析(PCA)和免疫算法优化扩展径向基函数(radial basis function,RBF)支持向量机的边坡安全系数预测方法。首先,在传统的主成分分析方法中引入了线性判别的思想,实现输入空间的降维。此外,利用对称正定矩阵对RBF核函数进行扩展,构建基于ERBF核函数的支持向量机模型,并用免疫算法记性优化。采用均方根误差(root mean squared error,RMSE)、平方和误差(sum of squares for error,SSE)、平均绝对误差(mean absolute error,MAE)、平均绝对百分比误差(mean absolute percentage error,MAPE)和可靠性来验证模型的性能。与网格搜索、粒子群优化算法和遗传算法相比,所提出方法的4种误差分别为0.0226、0.0058、0.0735和0.0121,数值偏差更小,预测结果更接近实际值且可靠性更高。此外,还将以上方法与强度折减法进行了对比,结果表明所提出方法与边坡安全系数计算值最为接近。最后,从边坡安全系数得出的边坡稳定级别也可为相关决策部门在应急响应中提供参考。
基金National CNC Special Project,China(No.2010ZX04001-032)the Youth Science and Technology Foundation of Gansu Province,China(No.145RJYA307)
文摘Aiming at the solving problem of improved nonhomogeneous Poisson process( NHPP) model in engineering application,the immune clone maximum likelihood estimation( MLE)method for solving model parameters was proposed. The minimum negative log-likelihood function was used as the objective function to optimize instead of using iterative method to solve complex system of equations,and the problem of parameter estimation of improved NHPP model was solved by immune clone algorithm. And the interval estimation of reliability indices was given by using fisher information matrix method and delta method. An example of failure truncated data from multiple numerical control( NC) machine tools was taken to prove the method. and the results show that the algorithm has a higher convergence rate and computational accuracy, which demonstrates the feasibility of the method.
