检索结果-内蒙古大学图书馆

14th International Conference on Control, Automation and Systems (ICCAS)

作者： Osman, Haitham King Khalid Univ Dept Chem Engn Abha Saudi Arabia

ISBN: (纸本)9788993215069

This paper presents an evolution algorithm as a powerful optimisation technique for tuning Model Based Predictive Control (MBPC) at the implications of different levels of model uncertainties. Although Standard Genetic algorithms (SGAs) are proven to successfully tune and optimise MBPC parameters when no model mismatch. SGAs are trapped in a local optimum at the price of model uncertainty. The multi-objective evaluation algorithms are capable to incorporate many objective functions that can meet simultaneously robust control design objective functions. These promising techniques are successfully implemented to stabilised MBPC at high model uncertainty.

关键词： Model Base Predictive Control multi-objective evolution algorithm Control Tuning

来源：评论

学校读者我要写书评

暂无评论

Constraint multi-objective automated synthesis for CMOS operational amplifier

引用

NEUROCOMPUTING 2012年 98卷 108-113页

作者： Tao, Jili Fan, Qinru Chen, Xiaoming Zhu, Yong Zhejiang Univ Ningbo Inst Technol Ningbo Zhejiang Peoples R China

A multi-objective evolution algorithm (MOEA) is presented to automatically determine the parameters in Op-Amp synthesis where the cost functions (e.g., minimizing the power dissipation and the chip area) and the constraint functions (e.g., the user-defined specifications) can be modeled as polynomials of the design variables. The proposed algorithm is based on MOEA which does not use weighting coefficients in converting multiple objectives into single objective. A constraint handling strategy without penalty parameters is proposed to avoid the difficulty of penalty parameter selection. Moreover, an elitist maintaining scheme is utilized to keep the evenness of the Pareto front. Simulations over several benchmark functions validate the efficiency of the proposed algorithm for the evenness of population distribution and the convergence to the Pareto front. Numerical experiments of a Miller compensated two-stage Op-Amp show that the proposed MOEA is able to achieve better performance than NSGA-II + PCH, GA + SPF and GA + PCH. (c) 2012 Elsevier B.V. All rights reserved.

关键词： multi-objective evolution algorithm CMOS Op-Amp Constraint handling Elitist maintaining

来源：评论

学校读者我要写书评

暂无评论

Security-Enhanced Filter Design for Stochastic Systems under Malicious Attack via Smoothed Signal Model and multiobjective Estimation Method

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年 68卷 4971-4986页

作者： Chen, Bor-Sen Lee, Min-Yen Chen, Xin-Hong Natl Tsing Hua Univ Dept Elect Engn Hsinchu 30013 Taiwan Yuan Ze Univ Dept Elect Engn Taoyuan 32003 Taiwan

In this study, a novel security-enhanced filter (SEF) is proposed for the system state and malicious attack signal estimation of the stochastic jump-diffusion systems with the external disturbance, measurement noise and malicious attack signal on system and sensor. To efficiently estimate the system state and malicious attack signal by the traditional Luenberger-type filter, a novel smoothed signal model of malicious attack signals is embedded in the system model so that the attack signals in the augmented system do not corrupt the augmented states estimation of SEF again. For the optimal filtering robustness and security, the stochastic multi-objective (MO) H-2/H-infinity SEF scheme is proposed to achieve optimal disturbance and noise filtering performance and the optimal security enhancement undermalicious attack. By using the suboptimal method, the stochastic MO H-2/H-infinity SEF design could be equivalently transformed to linear matrix inequalities (LMIs)-constrained multi-objective optimization problem (MOP). In the case of nonlinear stochastic system, the MO H-2/H-infinity SEF design problem could be converted to a Hamilton-Jacobi inequalities (HJIs)-constrained MOP. In order to overcome the difficulty in solving the HJIs-constrained MOP, based on the global linearization technique, the HJIs-constrained MOP for SEF design of nonlinear stochastic systems could be transformed to an LMIsconstrained MOP. Further, an LMIs-constrained multi-objective evolution algorithm (MOEA) is proposed to efficiently solve the LMIs-constrained MOP for the design of SEF. Two simulation examples including the missile trajectory estimation problem by ground radar system under the malicious attack signals and estimation of netwoked-based mass spring system are given to validate the effectiveness of the proposed method.

关键词： Stochastic jump-diffusion system malicious attack multi-objective evolution algorithm linear matrix inequality Hamilton-Jacobi inequality nonlinear filter

来源：评论

学校读者我要写书评

暂无评论

An effective L-MONG algorithm for solving multi-objective flow-shop inverse scheduling problems

引用

JOURNAL OF INTELLIGENT MANUFACTURING 2018年第4期29卷 789-807页

作者： Mou, Jianhui Li, Xinyu Gao, Liang Yi, Wenchao Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan Hubei Peoples R China

Generally, in handling traditional scheduling problems, ideal manufacturing system environments are assumed before determining effective scheduling. Unfortunately, "ideal environments" are not always possible. Real systems often encounter some uncertainties which will change the status of manufacturing systems. These may cause the original schedule to no longer to be optimal or even feasible. Traditional scheduling methods are not effective in coping with these cases. Therefore, a new scheduling strategy called "inverse scheduling" has been proposed to handle these problems. To the best of our knowledge, this research is the first to provide a comprehensive mathematical model for multi-objective permutation flow-shop inverse scheduling problem (PFISP). In this paper, first, a PFISP mathematical model is devised and an effective hybrid multi-objective evolutionary algorithm is proposed to handle uncertain processing parameters (uncertainties) and multiple objectives at the same time. In the proposed algorithm, we take an insert method NEH-based (Nawaz-Enscore-Ham) as a local improving procedure and propose several adaptations including efficient initialization, decimal system encoding, elitism and population diversity. Finally, 119 public problem instances with different scales and statistical performance comparisons are provided for the proposed algorithm. The results show that the proposed algorithm performs better than the traditional multi-objective evolution algorithm (MOEA) in terms of searching quality, diversity level and efficiency. This paper is the first to propose a mathematical model and develop a hybrid MOEA algorithm to solve PFISP in inverse scheduling domain.

关键词： Inverse scheduling Flow-shop scheduling multi-objective evolution algorithm Local improving strategy Hybrid algorithm

来源：评论

学校读者我要写书评

暂无评论

An evolutionary algorithm with clustering-based selection strategies for multi-objective optimization

引用

INFORMATION SCIENCES 2023年第1期624卷 217-234页

作者： Zhou, Shenghao Mo, Xiaomei Wang, Zidong Li, Qi Chen, Tianxiang Zheng, Yujun Sheng, Weiguo Hangzhou Normal Univ Dept Comp Sci Hangzhou 311121 Peoples R China Commun Univ Zhejiang Coll Media Engn Hangzhou 310018 Peoples R China Brunel Univ London Dept Comp Sci Uxbridge UB8 9PH Middx England San Jose State Univ Dept Comp Sci 1 Washington SQ San Jose CA 95192 USA

This paper proposes an evolutionary algorithm with clustering-based selection strategies to deal with multi-objective optimization problems. In the proposed algorithm, two clustering based selection strategies, named local indicator selection and local crowding selection, have been devised to appropriately search the space. The local indicator selection is developed to select diverse and well-converged individuals for mating while the local crowding selection strategy is designed to maintain a set of evenly distributed individuals on the Pareto front for next generation of evolution. The proposed method is further enhanced by a clustering based crowding degree strategy, which is introduced to extract a uniformly distributed and convergent solutions as the final output. The performance of proposed algorithm has been evaluated on 31 benchmark problems and compared with related methods. The results clearly show the merits of proposed strategies and the proposed method could significantly outperform related methods to be compared.(c) 2022 Elsevier Inc. All rights reserved.

关键词： multi-objective evolution algorithm Selection strategy Clustering I ? plus indicator

来源：评论

学校读者我要写书评

暂无评论

Disease biomarker identification based on sample network optimization

引用

METHODS 2023年第1期213卷 42-49页

作者： Wei, Pi-Jing Ma, Wenwen Li, Yanxin Su, Yansen Anhui Univ Inst Phys Sci & Informat Technol Informat Mat & Intelligent Sensing Lab Anhui Prov 111 Jiulong Rd Hefei 230601 Anhui Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence 5089 Wangjiang West Rd Hefei 230088 Peoples R China Anhui Univ Sch Comp Sci & Technol Key Lab Intelligent Comp & Signal Proc 111 Jiulong Rd Hefei 230601 Peoples R China Third Hosp Xingtai Dept Cardiol Xingtai 054000 Hebei Peoples R China Anhui Univ Sch Artificial Intelligence 111 Jiulong Rd Hefei 230601 Peoples R China

A large amount of evidence shows that biomarkers are discriminant features related to disease development. Thus, the identification of disease biomarkers has become a basic problem in the analysis of complex diseases in the medical fields, such as disease stage judgment, disease diagnosis and treatment. Research based on networks have become one of the most popular methods. Several algorithms based on networks have been proposed to identify biomarkers, however the networks of genes or molecules ignored the similarities and associations among the samples. It is essential to further understand how to construct and optimize the networks to make the identified biomarkers more accurate. On this basis, more effective strategies can be developed to improve the performance of biomarkers identification. In this study, a multi-objective evolution algorithm based on sample similarity networks has been proposed for disease biomarker identification. Specifically, we design the sample similarity networks to extract the structural characteristic information among samples, which used to calculate the influence of the sample to each class. Besides, based on the networks and the group of biomarkers we choose in every iteration, we can divide samples into different classes by the importance for each class. Then, in the process of evolution algorithm population iteration, we develop the elite guidance strategy and fusion selection strategy to select the biomarkers which make the sample classification more accurate. The experiment results on the five gene expression datasets suggests that the algorithm we proposed is superior over some state-of-the-art disease biomarker identification methods.

关键词： Disease biomarker identification Sample similarity network multi-objective evolution algorithm Sample classification

来源：评论

学校读者我要写书评

暂无评论

Constraint multi-objective Automated Synthesis for CMOS Operational Amplifier

Constraint Multi-objective Automated Synthesis for CMOS Oper...

引用

International Conference on Life System Modeling and Simulation / International Conference on Intelligent Computing for Sustainable Energy and Environment (LSMS-ICSEE)

作者： Tao, Jili Chen, Xiaoming Zhu, Yong Zhejiang Univ Ningbo Inst Technol Ningbo Zhejiang Peoples R China

ISBN: (纸本)9783642155963

The synthesis of CMOS operational amplifier (Op-Amp) can be translated into a constrained multi-objective optimization problem, in which a large number of specifications have to be taken into account, i.e., gain, unity gain-bandwidth (GBW), slew-rate (SR), common-mode rejection ratio (CMRR) and bias conditions. A constraint handling strategy without penalty parameters for multi-objective optimization algorithm is proposed. A standard operational amplifier is then designed, the results show the proposed methodology is very effective and can obtain better specifications than other methods.

关键词： multi-objective evolution algorithm CMOS Op-Amp constraint handling

来源：评论

学校读者我要写书评

暂无评论

An Improved NSGA-II to Solve multi-objective Optimization Problem

An Improved NSGA-II to Solve Multi-Objective Optimization Pr...

引用

26th Chinese Control and Decision Conference (CCDC)

作者： Fu, Yaping Huang, Min Wang, Hongfeng Jiang, Guanjie Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9781479937066

NSGA-II(nondominated sorting genetic algorithm II) is a popular multi-objective evolution algorithm (MOEA), which applies binary tournament selection, elitist preserving strategy, nondominated sorting and crowding distance mechanism to obtain a good quality and uniform spread nondominated solution set. In this paper, an improved version of NSGA-II (INSGA-II) is proposed aiming to increase the diversity and enhance the local search ability. The INSGA-II has two populations: interior population and external population. The external population is used to store the nondominated solution found in the search process, while the interior population takes part in generation evolution. When the interior population tends to converge, it is updated by the individuals in the external population and generated randomly. A local search based on the amount of domination is applied to enhance the local search ability. In order to demonstrate the effectiveness of the proposed INSGA-II, comparisons with NSGA-II is carried out by ten functions, and the results show the quality and spread of INSGA-II are better than NSGA-II.

关键词： multi-objective evolution algorithm Nondominated sorting genetic algorithm Local search acceptance with probability

来源：评论

学校读者我要写书评

暂无评论

Weighting Matrix Selection Method for LQR Design Based on a multi-objective evolutionary algorithm

Weighting Matrix Selection Method for LQR Design Based on a ...

引用

International Conference on Manufacturing Science and Technology (ICMST 2011)

作者： Nekoui, Mohammad Ali Bozorgi, Hassan Heidari Jame Islamic Azad Univ S Tehran Branch Tehran Iran

ISBN: (纸本)9783037852958

This paper introduces an application of multi-objective evolution algorithm (MOEA) to design Q and R weighting matrices in Linear Quadratic regulators (LQR). Considering the difficulty of designing weighting matrices for a linear quadratic regulator, a multi-objective evolutionary algorithm based approach is proposed. The LQR weighting matrices, state feedback control rate and consequently the optimal controller are obtained by means of establishing the multi-objective optimization model of LQR weighting matrices and applying MOEA to it, which makes control system meet multiple performance indexes simultaneously. Controller of double inverted pendulum system is designed using the proposed approach. Simulation results show that it has shorter adjusting time and smaller amplitude value deviating from steady-state than a Non-dominated Sorting Genetic algorithm LQR (NSGA-LQR)weighting matrices design approach.

关键词： Linear Quadratic Regulators multi-objective evolution algorithm Double-Inverted Pendulum

来源：评论

学校读者我要写书评

暂无评论

Portfolio optimization under solvency II: a multi-objective approach incorporating market views and real-world constraints

引用

DECISIONS IN ECONOMICS AND FINANCE 2021年第1期44卷 269-294页

作者： Di Francesco, Marco UnipolSai Assicuraz Via Stalingrado 45 Bologna Italy

We propose a new approach to handle the problem of portfolio optimization for non-life insurance company incorporating the solvency capital requirement (SCR), market views and their confident levels, several equality and inequality real-world constraints and transaction costs. We analyze two case studies: first, we consider a tri-objective optimization problem in which we minimize the Market SCR, the variance of the so-called basic own funds (BOF) and maximize the return of portfolio;secondly, we consider bi-objective optimization problem in which we minimize the variance of BOF and maximize the return of portfolio while considering the Market SCR as a constraint. We introduce a scenario-based framework in which the reference model is given by an internal model. By entropy pooling approach, we blended market views and their confident levels with the reference model to build the posterior distribution. The latter is used to compute the variance of BOF and the portfolio return. In both case studies, we obtain good results in term of risk-reward tradeoff and diversification.

关键词： Portfolio theory Solvency II multi-objective evolution algorithm Real-world constraints Non-life insurance company

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：