检索结果-内蒙古大学图书馆

A neurodynamic approach to nonlinear optimization problems with affine equality and convex inequality constraints

NEURAL NETWORKS 2019年 109卷 147-158页

作者： Liu, Na Qin, Sitian Harbin Inst Technol Dept Math Weihai Peoples R China

This paper presents a neurodynamic approach to nonlinear optimization problems with affine equality and convex inequality constraints. The proposed neural network endows with a time-varying auxiliary function, which can guarantee that the state of the neural network enters the feasible region in finite time and remains there thereafter. Moreover, the state with any initial point is shown to be convergent to the critical point set when the objective function is generally nonconvex. Especially, when the objective function is pseudoconvex (or convex), the state is proved to be globally convergent to an optimal solution of the considered optimization problem. Compared with other neural networks for related optimization problems, the proposed neural network in this paper has good convergence and does not depend on some additional assumptions, such as the assumption that the inequality feasible region is bounded, the assumption that the penalty parameter is sufficiently large and the assumption that the objective function is lower bounded over the equality feasible region. Finally, some numerical examples and an application in real-time data reconciliation are provided to display the well performance of the proposed neural network. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： nonlinear optimization problems Recurrent neural network Lyapunov function Global convergence

来源：评论

学校读者我要写书评

暂无评论

Integrating particle swarm optimization with genetic algorithms for solving nonlinear optimization problems

引用

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2011年第5期235卷 1446-1453页

作者： Abd-El-Wahed, W. F. Mousa, A. A. El-Shorbagy, M. A. Shebin El Kom Minufiya Univ Fac Engn Dept Basic Engn Sci Shibin Al Kawm Egypt Shebin El Kom Minufiya Univ Fac Comp & Informat Shibin Al Kawm Egypt

Heuristic optimization provides a robust and efficient approach for solving complex real-world problems. The aim of this paper is to introduce a hybrid approach combining two heuristic optimization techniques, particle swarm optimization (PSO) and genetic algorithms (GA). Our approach integrates the merits of both GA and PSO and it has two characteristic features. Firstly, the algorithm is initialized by a set of random particles which travel through the search space. During this travel an evolution of these particles is performed by integrating PSO and GA. Secondly, to restrict velocity of the particles and control it, we introduce a modified constriction factor. Finally, the results of various experimental studies using a suite of multimodal test functions taken from the literature have demonstrated the superiority of the proposed approach to finding the global optimal solution. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Particle swarm optimization Genetic algorithm nonlinear optimization problems Constriction factor

来源：评论

学校读者我要写书评

暂无评论

Finite-time recurrent neural networks for solving nonlinear optimization problems and their application

引用

NEUROCOMPUTING 2016年 177卷 120-129页

作者： Miao, Peng Shen, Yanjun Li, Yujiao Bao, Lei China Three Gorges Univ Hubei Prov Collaborat Innovat Ctr New Energy Micr Yichang 443002 Peoples R China Zhengzhou Sci & Technol Univ Dept Basic Courses Zhengzhou 450064 Hennan Peoples R China China Three Gorges Univ Coll Sci Yichang 443002 Peoples R China

This paper focuses on finite-time recurrent neural networks with continuous but non-smooth activation function solving nonlinearly constrained optimization problems. Firstly, definition of finite-time stability and finite-time convergence criteria are reviewed. Secondly, a finite-time recurrent neural network is proposed to solve the nonlinear optimization problem. It is shown that the proposed recurrent neural network is globally finite-time stable under the condition that the Hessian matrix of the associated Lagrangian function is positive definite. Its output converges to a minimum solution globally and finite time, which means that the actual minimum solution can be derived in finite-time period. In addition, our recurrent neural network is applied to a hydrothermal scheduling problem. Compared with other methods, a lower consumption scheme can be derived in finite-time interval. At last, numerical simulations demonstrate the superiority and effectiveness of our proposed neural networks by solving nonlinear optimization problems with inequality constraints. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Finite-time stable Recurrent neural network nonlinear optimization problems Continuous but non-smooth Hydrothermal scheduling problem

来源：评论

学校读者我要写书评

暂无评论

Second-order necessary conditions for nonlinear optimization problems in banach spaces and their application to an optimal control problem

引用

Mathematics of Operations Research 1990年第3期15卷 467-467页

作者： Maruyama, Yukihiro

We study second-order necessary conditions for nonlinear optimization problems with equality and set constraints in Banach spaces. The necessary conditions are given in the primal form and the dual form by the use of second-order Neustadt derivative. Moreover we apply the necessary conditions to an optimal control problem without state constraints.

关键词： nonlinear optimization problems second order necessary conditions second-order Neustadt derivative primal form dual form optimal control problem

来源：评论

学校读者我要写书评

暂无评论

OpenMP Genetic Algorithm for Continuous nonlinear Large-Scale optimization problems 5th

OpenMP Genetic Algorithm for Continuous Nonlinear Large-Scal...

引用

5th International Conference on Soft Computing for Problem Solving (SocProS)

作者： Umbarkar, A. J. Walchand Coll Engn Dept Informat Technol Sangli MS India

ISBN: (纸本)9789811004513;9789811004506

Genetic algorithms (GAs) are one of the evolutionary algorithms for solving continuous nonlinear large-scale optimization problems. In an optimization problem, when dimension size increases, the size of search space increases exponentially. It is quite difficult to explore and exploit such huge search space. GA is highly parallelizable optimization algorithm;still there is a challenge to use all the cores of multicore (viz. Dual core, Quad core, and Octa cores) systems. The paper analyzes the parallel implementation of SGA (Simple GA) called as OpenMP GA. OpenMP (Open Multi-Processing) GA attempts to explore and exploit the search space on the multiple cores' system. The performance of OpenMP GA is compared with SGA with respect to time required and cores utilized for obtaining optimal solution. The results show that the performance of the OpenMP GA is remarkably superior to that of the SGA in terms of execution time and CPU utilization. In case of OpenMP GA, CPU utilization is almost double for continuous nonlinear large-scale test problems for the given system configuration.

关键词： Function optimization Genetic algorithm (GA) Open multi-processing (OpenMP) nonlinear optimization problems optimization benchmarks functions

来源：评论

学校读者我要写书评

暂无评论

nonlinear optimization VIA NOVEL NEURAL NETWORK METHODS

引用

JOURNAL OF INDUSTRIAL AND MANAGEMENT optimization 2025年第4期21卷 2472-2489页

作者： Teng, Jiao Yiu, Ka fai cedric Hong Kong Polytech Univ Dept Appl Math Hong Kong Peoples R China

This paper introduces a novel approach for solving high-dimensional nonlinear optimization problems by integrating neural networks into the optimization process. The method leverages the capabilities of neural networks to efficiently handle complex, high-dimensional data and to approximate discrete numerical solutions of nonlinear optimization problems using continuous functions. By combining the nonlinear mapping ability of neural networks with iterative optimization algorithms, the proposed approach provides a superior method to solve nonlinear optimization problems. The paper demonstrates the adaptability of neural network-based solution methods in solving various nonlinear optimization problems and illustrates that the method can be applied to many different problem scenarios. The effectiveness and correctness of the proposed method are demonstrated through examples.

关键词： nonlinear optimization problems Neural networks

来源：评论

学校读者我要写书评

暂无评论

Quantum-Behaved Particle Swarm optimization Algorithm Based on the Two-Body Problem

引用

Chinese Journal of Electronics 2019年第3期28卷 569-576页

作者： YAN Tao LIU Fengxian Institute of Big Data Science and Industry Shanxi University School of Computer and Information Technology Shanxi University Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Guangzhou Institute of Electronic Technology Chinese Academy of Sciences University of Chinese Academy of Sciences

The present study proposes an improved Quantum-behaved particle swarm optimization algorithm based on the two-body problem model(QTPSO) for solving the problem that other quantum-behaved particle swarm optimization algorithms easily converge on local optimal solutions when solving complex nonlinear problems. In the proposed QTPSO algorithm, particles are categorised as core particles and edge particles. Once the position of the core particle is determined, the edge particle appears in the vicinity of the attractor exhibiting a high probability, and the attractor is obtained through the random weighted sum of the core particle and the optimal mean position. Through simulation of the motion of these two particles by applying the interaction of the particles in the two-body problem, this mechanism not only improves the diversity of the population, but also enhances the local search capacity. To validate the proposed algorithm, three groups of experimental results were obtained to compare the proposed algorithm with other swarm intelligence algorithms. The experimental results indicate the superiority of the QTPSO algorithm.

关键词： Quantum-behaved particle swarm optimization Two-body problem Quantum potential well Wave function nonlinear optimization problems

来源：评论

学校读者我要写书评

暂无评论

Sequential linear programming and particle swarm optimization for the optimization of energy districts

引用

ENGINEERING optimization 2019年第1期51卷 84-100页

作者： Riccietti, Elisa Bellavia, Stefania Sello, Stefano Univ Firenze Dipartimento Matemat & Informat Ulisse Dini Florence Italy Univ Firenze Dipartimento Ingn Ind Florence Italy Enel Ingn & Ric Pisa Italy IRIT Toulouse France

This article deals with the optimization of energy resource management of industrial districts, with the aim of minimizing customer energy expenses. A model of the district is employed, whose optimization gives rise to a nonlinear constrained optimization problem. Here the focus is on its numerical solution. Two different methods are considered: a sequential linear programming method and a particle swarm optimization method. Efficient implementations of both approaches are devised and the results of the tests performed on several energetic districts are reported, including a real case study.

关键词： nonlinear optimization problems sequential linear programming particle swarm optimization energy districts penalty functions

来源：评论

学校读者我要写书评

暂无评论

DYNAMIC optimization problems WITH BOUNDED TERMINAL CONDITIONS

引用

JOURNAL OF optimization THEORY AND APPLICATIONS 1987年第1期52卷 151-162页

作者： LEE, AY STANFORD UNIV STANFORDCA 94305

Bounded terminal conditions of nonlinear optimization problems are converted to equality terminal conditions via the Valentine's device. In so doing, additional unknown parameters are introduced into the problem. The transformed problems can still be easily solved using the sequential gradient-restoration algorithm (SGRA) via a simple augmentation of the unknown parameter vector π. Three example problems with bounded terminal conditions are solved to verify this technique.

关键词： Sequential gradient-restoration algorithm nonlinear optimization problems bounded terminal conditions Valentine's device

来源：评论

学校读者我要写书评

暂无评论

optimization of short-time gasoline blending scheduling problem with a DNA based hybrid genetic algorithm

引用

CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION 2010年第10期49卷 1076-1083页

作者： Chen, Xiao Wang, Ning Zhejiang Univ Natl Lab Ind Control Technol Inst Cyber Syst & Control Hangzhou 310027 Peoples R China Hangzhou Dianzi Univ Inst Automat Hangzhou 310018 Peoples R China

Gasoline blending is a key process in the petroleum refinery industry posed as a nonlinear optimization problem with heavily nonlinear constraints This paper presents a DNA based hybrid genetic algorithm (DNA-HGA) to optimize such nonlinear optimization problems In the proposed algorithm potential solutions are represented with nucleotide bases Based on the complementary properties of nucleotide bases operators inspired by DNA are applied to improve the global searching ability of GA for efficiently locating the feasible domains After the feasible region is obtained the sequential quadratic programming (SQP) is implemented to improve the solution The hybrid approach is tested on a set of constrained nonlinear optimization problems taken from the literature and compared with other approaches The computation results validate the effectiveness of the proposed algorithm The recipes of a short-time gasoline blending problem are optimized by the hybrid algorithm and the comparison results show that the profit of the products is largely improved while achieving more satisfactory quality indicators in both certainty and uncertainty environment (C) 2010 Elsevier B V All rights reserved

关键词： DNA computing Genetic algorithm SQP Hybrid optimization method nonlinear optimization problems Short-time gasoline blending scheduling

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：