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An improved group search optimizer for mechanical design optimization problems

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Progress in Natural Science:Materials International 2009年第1期19卷 91-97页

作者： Hai Shena,b,c,Yunlong Zhu a,Ben Niu a,b,Q.H.Wu d a Key Laboratory of Industrial Informatics,Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China b Graduate School of the Chinese Academy of Sciences,Beijing 100049,China c College of Physics Science and Technology,Shenyang Normal University,Shenyang 110034,China d Department of Electrical Engineering and Electronics,The University of Liverpool,Brownlow Hill,Liverpool,L69 3GJ,UK Key Laboratory of Industrial Informatics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China Graduate School of the Chinese Academy of Sciences Beijing 100049 China College of Physics Science and Technology Shenyang Normal University Shenyang 110034 China Department of Electrical Engineering and Electronics The University of Liverpool Brownlow Hill Liverpool L69 3GJ UK

This paper presents an improved group search optimizer(iGSO) for solving mechanical design optimization *** the proposed algorithm,subpopulations and a co-operation evolutionary strategy were adopted to improve the global search capability and convergence *** iGSO is evaluated on two optimization problems of classical mechanical design:spring and pressure *** experimental results are analyzed in comparison with those reported in the *** results show that iGSO has much better convergence performance and is easier to implement in comparison with other existing evolutionary algorithms.

关键词： Mechanical optimization problem GSO constrained optimization problem

A hybrid cuckoo search algorithm with feasibility-based rule for constrained structural optimization

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Journal of Central South University 2014年第8期21卷 3197-3204页

作者：龙文张文专黄亚飞陈义雄 Guizhou Key Laboratory of Economics System Simulation Guizhou University of Finance & Economics School of Information Science and Engineering Central South University

constrained optimization problems are very important as they are encountered in many science and engineering *** a novel evolutionary computation technique,cuckoo search(CS) algorithm has attracted much attention and wide applications,owing to its easy implementation and quick convergence.A hybrid cuckoo pattern search algorithm(HCPS) with feasibility-based rule is proposed for solving constrained numerical and engineering design optimization *** algorithm can combine the stochastic exploration of the cuckoo search algorithm and the exploitation capability of the pattern search *** and comparisons based on several well-known benchmark test functions and structural design optimization problems demonstrate the effectiveness,efficiency and robustness of the proposed HCPS algorithm.

关键词： constrained optimization problem cuckoo search algorithm pattem search feasibility-based rule engineeringoptimization

维普期刊数据库

Algorithm Studies on How to Obtain a Conditional Nonlinear Optimal Perturbation (CNOP)

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Advances in Atmospheric Sciences 2010年第6期27卷 1311-1321页

作者：孙国栋穆穆张雅乐 The State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of Sciences Key Laboratory of Ocean Circulation and Wave Institute of OceanologyChinese Academy of Sciences Graduate University of the Chinese Academy of Sciences

The conditional nonlinear optimal perturbation （CNOP）, which is a nonlinear generalization of the linear singular vector （LSV）, is applied in important problems of atmospheric and oceanic sciences, including ENSO predictability, targeted observations, and ensemble forecast. In this study, we investigate the computational cost of obtaining the CNOP by several methods. Differences and similarities, in terms of the computational error and cost in obtaining the CNOP, are compared among the sequential quadratic programming （SQP） algorithm, the limited memory Broyden-Fletcher-Goldfarb-Shanno （L-BFGS） algorithm, and the spectral projected gradients （SPG2） algorithm. A theoretical grassland ecosystem model and the classical Lorenz model are used as examples. Numerical results demonstrate that the computational error is acceptable with all three algorithms. The computational cost to obtain the CNOP is reduced by using the SQP algorithm. The experimental results also reveal that the L-BFGS algorithm is the most effective algorithm among the three optimization algorithms for obtaining the CNOP. The numerical results suggest a new approach and algorithm for obtaining the CNOP for a large-scale optimization problem.

关键词： conditional nonlinear optimal perturbation constrained optimization problem unconstrainedoptimization problem

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A Naive Particle Swarm Algorithm for constrained optimization problems 25th

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A Naive Particle Swarm Algorithm for Constrained Optimizatio...

第25届中国控制与决策会议

作者： Jin Qin Benliang Xie College of Computer Science & Information Guizhou University College of Science Guizhou University

ISBN: (纸本)9781467355339

In this paper, a naive particle swarm algorithm for constrained optimization problems is proposed. Also, a direct constraint handling method is adopted, which is more direct and easier to be implemented in comparison with other constraint handling methods, such as penalty function, etc. The experimental results on benchmark problems show that the proposed algorithm can solve constrained optimization problems effectively and has a robust performance.

关键词： Particle Swarm optimization constrained optimization problem Constraint Handling Penalty Function

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隐互补问题的极小化变形及其稳定点

高等学校计算数学学报 2009年第1期31卷 11-18页

作者：袁泉殷洪友南京航空航天大学数学系

1引言互补问题在最优化中有着广泛的应用,例如线性规划中的对偶问题,非线性规划中求稳定点的KKT条件以及变分不等式的求解都可以转化为互补问题,另外,某些均衡网络设计问题、信号最优化问题以及交通配置等问题也可利用互补问题来求解.文... 详细信息

1引言互补问题在最优化中有着广泛的应用,例如线性规划中的对偶问题,非线性规划中求稳定点的KKT条件以及变分不等式的求解都可以转化为互补问题,另外,某些均衡网络设计问题、信号最优化问题以及交通配置等问题也可利用互补问题来求解.文献[1]-[3]详细描述了线性互补问题和非线性互补问题及它们的实际应用.线性互补问题与非线性互补问题是隐互补问题的特殊形式,因此对隐互补问题的研究也更具意义.

关键词： implicit complementarity problems merit function unconstrained optimization problem constrained optimization problem regularity condition

A Modified Global Harmony Search Algorithm for Robust PID Controller Tuning

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A Modified Global Harmony Search Algorithm for Robust PID Co...

A Constraint Consensus Vector-based PSO Algorithm for Solving Engineering optimization problems

第三十届中国控制会议

作者： ZOU Dexuan~1,LIU Haikuan~1,GAO Liqun~2 1.School of Electrical Engineering and Automation,Xuzhou Normal University,Xuzhou 221116,P.R.China 2.School of Information Science and Engineering,Northeastern University,Shenyang 110004,P.R.China

The robust turning of proportional-integral-derivative(PID) controller is a hard work,and it is restricted by multiple H performance *** robust PID controller turning is essentially a constrained optimization problem,therefore,a modified global harmony search algorithm(MGHS) is proposed to handle this *** MGHS algorithm is a populationbased algorithm,and it updates the worst solution in each iteration so as to improve the quality of all candidate *** this paper,we combine the MGHS and a penalty function method to trade off the objective function value and constraint violations. Experimental results demonstrate that the MGHS can find satisfactory controller parameters for robust PID controller tuning.

关键词： Robust PID Controller Tuning constrained optimization problem Modified Global Harmony Search Algorithm Penalty Function Method

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A Constraint Consensus Vector-based PSO Algorithm for Solvin...