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An effective genetic algorithm to VDA with discontinuous “on-off” switches

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Science China Earth Sciences 2012年第3期55卷 1345-1357页

作者： ZHENG Qin SHA JianXin FANG ChangLuan Institute of Science PLA University of Science and TechnologyNanjing 211101China State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics(LASG)Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China Oceanic Hydrometeorological Center of the South Sea Navy Fleet Zhanjiang 524001China

With more and more improvements of atmosphere or ocean models,a growing number of physical processes in the form of parameterization are incorporated into the models,which,on the one hand,makes the models capable of describing the at-mospheric or oceanic movement more precisely,and on the other hand,introduces non-smoothness in the form of "on-off" switches into the models."On-off" switches enhance the nonlinearity of the models and finally result in the loss of the effec-tiveness of variational data assimilation(VDA) based on the conventional adjoint method(ADJ).This study,in virtue of the optimization ability of a genetic algorithm(GA) for non-smooth problems,presents a new GA(referred to as GA NEW) to solve the problems of the VDA with discontinuous "on-off" *** the GA-NEW,adaptive selection and mutation oper-ators,blend crossover operator,and elitist strategy are combined in *** order to verify the effectiveness and feasi-bility of the GA NEW in VDA,an idealized model of partial differential equation with discontinuous "on-off" switches in the forcing term is adopted as the governing *** comparison with the ADJ,it is shown that the GA NEW in VDA is more effective and can yield better assimilation *** addition,VDA experiments demonstrate that the performance of a GA is greatly related to the configuration of genetic operators(selection,crossover and mutation operators) and much better results may be attained with more proper genetic ***,the robustness of the GA NEW to observational noise,model errors and observation density is investigated,and the results show that the GA NEW has stronger robustness than the ADJ with respect to all the three observation noises,model errors,and sparse observation.

关键词： variational data assimilation "on-off" switches genetic algorithm

Gaussian fitting based optimal design of aircraft mission success space using multi-objective genetic algorithm

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Chinese Journal of Aeronautics 2020年第12期33卷 3318-3330页

作者： Yuan GAO Yongliang TIAN Hu LIU Xue SUN School of Aeronautic Science and Engineering Beihang UniversityBeijing 100083China

In order to obtain the optimized aircraft design concept which meets the increasingly complex operation environment at the conceptual design stage,System-of-systems(So S)engineering must be *** paper proposes a novel optimization method for the design of aircraft Mission Success Space(MSS)based on Gaussian fitting and genetic algorithm(GA)in the So S ***,the concepts in the design and evaluation of MSS are summarized to introduce the Contribution to System-of-Systems(CSS)by using a conventional effectiveness index,Mission Success Rate(MSR).Then,the mathematic modelling of Gaussian fitting technique is noted as the basis of the optimization *** that,the proposed optimal MSS design is illustrated by the multiobjective optimization process where GA acts as the search tool to find the best solution(via Pareto front).In the case study,a simulation system of penetration mission was *** simulation results are collected and then processed by two MSS design schemes(contour and neural network)giving the initial variable space to GA *** on that,the proposed optimization method is implemented under both schemes whose optimal solutions are compared to obtain the final best design in the case study.

关键词： Evaluation Gaussian fitting genetic algorithm Mission success space Neural network System-of-systems

Cobalt crust recognition based on kernel Fisher discriminant analysis and genetic algorithm in reverberation environment

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Journal of Central South University 2021年第1期28卷 179-193页

作者： ZHAO Hai-ming ZHAO Xiang HAN Feng-lin WANG Yan-li School of Mechanical and Electrical Engineering Central South UniversityChangsha 410083China State Key Laboratory of High Performance Complex Manufacturing Central South UniversityChangsha 410083China

Recognition of substrates in cobalt crust mining areas can improve mining *** at the problem of unsatisfactory performance of using single feature to recognize the seabed material of the cobalt crust mining area,a method based on multiple-feature sets is *** of the target echoes are extracted by linear prediction method and wavelet analysis methods,and the linear prediction coefficient and linear prediction cepstrum coefficient are also ***,the characteristic matrices of modulus maxima,sub-band energy and multi-resolution singular spectrum entropy are obtained,*** resulting features are subsequently compressed by kernel Fisher discriminant analysis(KFDA),the output features are selected using genetic algorithm(GA)to obtain optimal feature subsets,and recognition results of classifier are chosen as genetic fitness *** advantages of this method are that it can describe the signal features more comprehensively and select the favorable features and remove the redundant features to the greatest *** experimental results show the better performance of the proposed method in comparison with only using KFDA or GA.

关键词： feature extraction kernel Fisher discriminant analysis(KFDA) genetic algorithm multiple feature sets cobalt crust recognition

Multi-UAV reconnaissance task allocation for heterogeneous targets using an opposition-based genetic algorithm with double-chromosome encoding

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Chinese Journal of Aeronautics 2018年第2期31卷 339-350页

作者： Zhu WANG Li LIU Teng LONG Yonglu WENa School of Aerospace Engineering Beijing Institute of Technology Key Laboratory of Dynamics and Control of Flight Vehicle Ministry of Education

This paper presents a novel multiple Unmanned Aerial Vehicles（UAVs） reconnaissance task allocation model for heterogeneous targets and an effective genetic algorithm to optimize UAVs＇ task sequence. Heterogeneous targets are classified into point targets, line targets and area targets according to features of target geometry and sensor＇s field of view. Each UAV is regarded as a Dubins vehicle to consider the kinematic constraints. And the objective of task allocation is to minimize the task execution time and UAVs＇ total consumptions. Then, multi-UAV reconnaissance task allocation is formulated as an extended Multiple Dubins Travelling Salesmen Problem（MDTSP）, where visit paths to the heterogeneous targets must meet specific constraints due to the targets＇ feature. As a complex combinatorial optimization problem, the dimensions of MDTSP are further increased due to the heterogeneity of targets. To efficiently solve this computationally expensive problem, the Opposition-based genetic algorithm using Double-chromosomes Encoding and Multiple Mutation Operators（OGA-DEMMO） is developed to improve the population variety for enhancing the global exploration capability. The simulation results demonstrate that OGADEMMO outperforms the ordinary genetic algorithm, ant colony optimization and random search in terms of optimality of the allocation results, especially for large scale reconnaissance task allocation problems.

关键词： Unmanned aerial vehicles Task allocation genetic algorithm Travelling salesman problems Dubins vehicles

Virtual network function scheduling via multilayer encoding genetic algorithm with distributed bandwidth allocation

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Science China(Information Sciences) 2018年第9期61卷 93-111页

作者： Quan YUAN Hongbo TANG Wei YOU Xiaolei WANG Yu ZHAO National Digital Switching System Engineering and Technological Research Center

Network function virtualization represents a revolutionary approach to network service deployment. This software-oriented approach for virtual network functions(VNFs) deployment enables more flexible and dynamic network services to meet diversified demands. To minimize the execution time of all VNFs in service function chains, VNF scheduling must be addressed. In this paper, we improve upon the flexible job-shop model by introducing the process of bandwidth allocation. First, we propose a multilayer encoding genetic algorithm to solve the VNF scheduling model. In addition, we design a distributed method for bandwidth allocation based on the Nash bargaining solution. Finally, by combining the genetic algorithm with distributed bandwidth allocation, we present a heuristic algorithm that solves the VNF scheduling problem in one stage. Using a multilayer encoding genetic algorithm, we simplify the constraints of the VNF scheduling problem and reduce its time complexity. At the same time, our Nash game solution refines the granularity of bandwidth allocation to further reduce the transmission delay between VNFs. The effectiveness of our proposed heuristic algorithm is verified through numerical evaluation. Compared with existing approaches,our method exhibits shorter scheduling time and reduces CPU time by 45% in simulated scenarios.

关键词： network function virtualization virtual network function scheduling genetic algorithm bandwidth allocation convex optimization

Optimizing the neural network hyperparameters utilizing genetic algorithm

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2021年第6期22卷 407-426页

作者： Saeid NIKBAKHT Cosmin ANITESCU Timon RABCZUK Division of Computational Mechanics Ton Due Thang UniversityHo Chi Minh CityVietnam Faculty of Civil Engineering Ton Due Thang UniversityHo Chi Minh CityVietnam Institute of Structural Mechanics Bauhaus-Universitdt WeimarWeimar 99423Germany

Neural networks(NNs),as one of the most robust and efficient machine learning methods,have been commonly used in solving several ***,choosing proper hyperparameters(*** numbers of layers and neurons in each layer)has a significant influence on the accuracy of these ***,a considerable number of studies have been carried out to optimize the NN *** this study,the genetic algorithm is applied to NN to find the optimal ***,the deep energy method,which contains a deep neural network,is applied first on a Timoshenko beam and a plate with a ***,the numbers of hidden layers,integration points,and neurons in each layer are optimized to reach the highest accuracy to predict the stress distribution through these ***,applying the proper optimization method on NN leads to significant increase in the NN prediction accuracy after conducting the optimization in various examples.

关键词： Machine learning Neural network(NN) Hyperparameters genetic algorithm

Parameters inversion of high central core rockfill dams based on a novel genetic algorithm

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Science China(Technological Sciences) 2016年第5期59卷 783-794页

作者： ZHOU Wei LI Shao Lin MA Gang CHANG Xiao Lin MA Xing ZHANG Chao State Key Laboratory of Water Resources and Hydropower Engineering Science Wuhan University Changjiang Institute of Survey Planning Design and Research

Parameters identification of rockfill materials is a crucial issue for high rockfill dams. Because of the scale effect, random sampling and sample disturbance, it is difficult to obtain the actual mechanical properties of rockfill from laboratory tests. Parameters inversion based on in situ monitoring data has been proven to be an efficient method for identifying the exact parameters of the rockfill. In this paper, we propose a modified genetic algorithm to solve the high-dimension multimodal and nonlinear optimal parameters inversion problem. A novel crossover operator based on the sum of differences in gene fragments(So DX) is proposed, inspired by the cloning of superior genes in genetic engineering. The crossover points are selected according to the difference in the gene fragments, defining the adaptive length. The crossover operator increases the speed and accuracy of algorithm convergence by reducing the inbreeding and enhancing the global search capability of the genetic algorithm. This algorithm is compared with two existing crossover operators. The modified genetic algorithm is then used in combination with radial basis function neural networks(RBFNN) to perform the parameters back analysis of a high central earth core rockfill dam. The settlements simulated using the identified parameters show good agreement with the monitoring data, illustrating that the back analysis is reasonable and accurate. The proposed genetic algorithm has considerable superiority for nonlinear multimodal parameter identification problems.

关键词： rockfill dam parameters back analysis genetic algorithm crossover operator sum of differences in gene fragments

Solution for integer linear bilevel programming problems using orthogonal genetic algorithm

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Journal of Systems Engineering and Electronics 2014年第3期25卷 443-451页

作者： Hong Li Li Zhang Yongchang Jiao School of Mathematics and Statistics Xidian University Institute of Antennas and EM Scattering Xidian University

An integer linear bilevel programming problem is firstly transformed into a binary linear bilevel programming problem, and then converted into a single-level binary implicit programming. An orthogonal genetic algorithm is developed for solving the binary linear implicit programming problem based on the orthogonal design. The orthogonal design with the factor analysis, an experimental design method is applied to the genetic algorithm to make the algorithm more robust, statistical y sound and quickly convergent. A crossover operator formed by the orthogonal array and the factor analysis is presented. First, this crossover operator can generate a smal but representative sample of points as offspring. After al of the better genes of these offspring are selected, a best combination among these offspring is then generated. The simulation results show the effectiveness of the proposed algorithm.

关键词： integer linear bilevel programming problem integer optimization genetic algorithm orthogonal experiment design

Exponential distribution-based genetic algorithm for solving mixed-integer bilevel programming problems

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Journal of Systems Engineering and Electronics 2008年第6期19卷 1157-1164页

作者： Li Hecheng Wang Yuping School of Computer Science and Technology Xidian Univ. Xi'an 710071 P. R. China Dept. of Mathematics Science Xidian Univ. Xi'an 710071 P. R. China

Two classes of mixed-integer nonlinear bilevel programming problems are discussed. One is that the follower＇s functions are separable with respect to the follower＇s variables, and the other is that the follower＇s functions are convex if the follower＇s variables are not restricted to integers. A genetic algorithm based on an exponential distribution is proposed for the aforementioned problems. First, for each fixed leader＇s variable x, it is proved that the optimal solution y of the follower＇s mixed-integer programming can be obtained by solving associated relaxed problems, and according to the convexity of the functions involved, a simplified branch and bound approach is given to solve the follower＇s programming for the second class of problems. Furthermore, based on an exponential distribution with a parameter λ, a new crossover operator is designed in which the best individuals are used to generate better offspring of crossover. The simulation results illustrate that the proposed algorithm is efficient and robust.

关键词： mixed-integer nonlinear bilevel programming genetic algorithm exponential distribution optimalsolutions