A New Method Based on Interation Algorithm to Extract Parameters of Constitutive Equations

2013 ◽  
Vol 281 ◽  
pp. 505-510
Author(s):  
Yong Zhi Hua ◽  
Li Wen Guan ◽  
Xin Jun Liu ◽  
Yu Hui Zhang
Keyword(s):  
Evolutionary Algorithms ◽  
Convergence Rate ◽  
Objective Function ◽  
Constitutive Equations ◽  
Orthogonal Cutting ◽  
New Method ◽  
Iteration Algorithm ◽  
Experiment Data ◽  
Initial Value ◽  
Gradient Based

These methods that extract parameters of constitutive equations can be divided into three groups: direct search-based strategies, gradient-based methods and evolutionary algorithms. By analyzing these strategies, a new method based on iteration algorithm was proposed. To obtain parameters of JC and ZA model for Ti-6Al-4V, the error between prediction data and SHPB experiment data was set as objective function, then initial value was calculated using iteration algorithm. The effect of convergence rate and precision at various steps and experiment data was invested. The main advantage of the method are as follows：fast calculation; compatible with SHPB data and orthogonal cutting data; compatible with the decoupling and coupling constitutive equations. Finally, it has shown that the algorithm is stable, and acceptable results can be obtained.

scholarly journals New One-Dimensional Search Iteration Algorithm and Engineering Application

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yiping Luo ◽  
Jinhao Meng ◽  
Defa Wang ◽  
Guobin Xue
Keyword(s):  
Objective Function ◽  
Newton Method ◽  
Golden Section ◽  
Engineering Application ◽  
New Method ◽  
Iteration Algorithm ◽  
Section Method ◽  
One Dimensional ◽  
Target Value ◽  
Golden Section Method

In structural optimization design, obtaining the optimal solution of the objective function is the key to optimal design, and one-dimensional search is one of the important methods for function optimization. The Golden Section method is the main method of one-dimensional search, which has better convergence and stability. Based on the solution of the Golden Section method, this paper proposes an efficient one-dimensional search algorithm, which has the advantages of fast convergence and good stability. An objective function calculation formula is introduced to compare and analyse this method with the Golden Section method, Newton method, and Fibonacci method. It is concluded that when the accuracy is set to 0.1, the new algorithm needs 3 iterations to obtain the target value. The Golden Section method takes 11 iterations, and the Fibonacci method requires 11 iterations. The Newton method cannot obtain the target value. When the accuracy is set to 0.01, the number of iterations of the new method is still the least. The optimized design of the T-section beam is introduced for engineering application research. When the accuracy is set to 0.1, the new method needs 3 iterations to obtain the target value and the Golden Section method requires 13 iterations. When the accuracy is set to 0.01, the new method requires 4 iterations and the Golden Section method requires 18 iterations. The new method has significant advantages in the one-dimensional search optimization problem.

A Penalty-Free Method with Trust Region for Nonlinear Semidefinite Programming

2015 ◽  
Vol 32 (01) ◽  
pp. 1540006 ◽  
Author(s):  
Zhongwen Chen ◽  
Shicai Miao
Keyword(s):  
Semidefinite Programming ◽  
Objective Function ◽  
Penalty Function ◽  
Trust Region ◽  
Numerical Results ◽  
New Method ◽  
Penalty Parameter ◽  
Nonlinear Semidefinite Programming ◽  
Globally Convergent ◽  
Nonlinear Objective Function

In this paper, we propose a class of new penalty-free method, which does not use any penalty function or a filter, to solve nonlinear semidefinite programming (NSDP). So the choice of the penalty parameter and the storage of filter set are avoided. The new method adopts trust region framework to compute a trial step. The trial step is then either accepted or rejected based on the some acceptable criteria which depends on reductions attained in the nonlinear objective function and in the measure of constraint infeasibility. Under the suitable assumptions, we prove that the algorithm is well defined and globally convergent. Finally, the preliminary numerical results are reported.

Reduction of Forced Vibration Response by Optimum Balance of Linkage and Optimum Design of System

1997 ◽  
Author(s):  
She-min Zhang ◽  
Nobuyoshi Morita ◽  
Takao Torii
Keyword(s):  
Objective Function ◽  
Root Mean Square ◽  
Forced Vibration ◽  
Vibration Response ◽  
New Method ◽  
Mean Square ◽  
Design Variables ◽  
Calculation Results ◽  
Angular Velocities ◽  
Optimum Balance

Abstract This paper proposes a new method to reduce the forced vibration response of frame of linkage. It is that the root-mean-square (RMS) value of binary maximum (Bmax) of forced vibration response at a series of angular velocities is taken as the objective function, and the counterweight mass parameters of links and the stiffness factors are used as design variables. Then, it is found out that the responses are related not only to the Bmax value of shaking forces, but also to the shape of curve of shaking forces. The calculation results are compared with those of two other methods used in the reduction of forced vibration response by optimized balance of linkages, and it is shown that the new method can significantly reduce the responses of frame of linkage.

Multivariate Constrained GNSS Real-time Full Attitude Determination Based on Attitude Domain Search

2018 ◽  
Vol 72 (2) ◽  
pp. 483-502
Author(s):  
Hongtao Wu ◽  
Xiubin Zhao ◽  
Chunlei Pang ◽  
Liang Zhang ◽  
Bo Feng
Keyword(s):  
Objective Function ◽  
Success Rate ◽  
High Efficiency ◽  
Attitude Determination ◽  
A Priori ◽  
Three Dimensional ◽  
Satellite System ◽  
New Method ◽  
Lambda Method ◽  
Attitude Angle

A priori attitude information can improve the success rate and reliability of Global Navigation Satellite System (GNSS) multi-antennae attitude determination. However, a priori attitude information is nonlinear, and integrating a priori information into the objective function rigorously will increase the complexity of an ambiguity domain search, such as the Multivariate Constrained-Least-squares Ambiguity Decorrelation Adjustment (MC-LAMBDA) method. In this paper, a new method based on attitude domain search is presented to make use of the a priori attitude angle information with high efficiency. First, the a priori information of pitch and roll is integrated into the search process to derive the analytic search step for attitude angle, and the integer candidates are determined by traversal search in the three-dimensional attitude domain. Then, the objective function is parameterised with Euler angles, and a non-iterative approximate method is utilised to simplify the iterative computation in calculating objective function values. Experimental results reveal that compared to the MC-LAMBDA method, our new method has the same success rate and reliability, but higher efficiency in making use of a priori attitude information.

scholarly journals Heliostat Field Layout Optimization with Evolutionary Algorithms

10.29007/7p6t ◽  
2018 ◽  
Author(s):  
Pascal Richter ◽  
David Laukamp ◽  
Levin Gerdes ◽  
Martin Frank ◽  
Erika Ábrahám
Keyword(s):  
Power Plant ◽  
Convex Optimization ◽  
Evolutionary Algorithms ◽  
Convergence Rate ◽  
Evolutionary Algorithm ◽  
Computer Science ◽  
Energy Supply ◽  
Optimization Problem ◽  
Convex Optimization Problem ◽  
New Genotype

The exploitation of solar power for energy supply is of increasing importance. While technical development mainly takes place in the engineering disciplines, computer science offers adequate techniques for optimization. This work addresses the problem of finding an optimal heliostat field arrangement for a solar tower power plant.We propose a solution to this global, non-convex optimization problem by using an evolutionary algorithm. We show that the convergence rate of a conventional evolutionary algorithm is too slow, such that modifications of the recombination and mutation need to be tailored to the problem. This is achieved with a new genotype representation of the individuals.Experimental results show the applicability of our approach.

scholarly journals Evolving Solutions to Community-Structured Satisfiability Formulas

2019 ◽  
Vol 33 ◽  
pp. 2346-2353
Author(s):  
Frank Neumann ◽  
Andrew M. Sutton
Keyword(s):  
Community Structure ◽  
Evolutionary Algorithms ◽  
Objective Function ◽  
Search Space ◽  
Propositional Satisfiability ◽  
Satisfying Assignment ◽  
Distance Correlation ◽  
Strong Signal ◽  
Fitness Distance Correlation ◽  
Correlation Properties

We study the ability of a simple mutation-only evolutionary algorithm to solve propositional satisfiability formulas with inherent community structure. We show that the community structure translates to good fitness-distance correlation properties, which implies that the objective function provides a strong signal in the search space for evolutionary algorithms to locate a satisfying assignment efficiently. We prove that when the formula clusters into communities of size s ∈ ω(logn) ∩O(nε/(2ε+2)) for some constant 0

Modified Harmony Search Algorithm for 0-1 Knapsack Problems

2013 ◽  
Vol 365-366 ◽  
pp. 182-185
Author(s):  
Hong Gang Xia ◽  
Qing Liang Wang
Keyword(s):  
Convergence Rate ◽  
Objective Function ◽  
Objective Function Value ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Knapsack Problems ◽  
Convergence And Stability ◽  
Update Strategy ◽  
Harmony Memory

In this paper, a modified harmony search (MHS) algorithm was presented for solving 0-1 knapsack problems. MHS employs position update strategy for generating new solution vectors that enhances accuracy and convergence rate of harmony search (HS) algorithm. Besides, the harmony memory consideration rate (HMCR) is dynamically adapted to the changing of objective function value in the current harmony memory, and the key parameters PAR and BW dynamically adjusted with the number of generation. Based on the experiment of solving ten classic 0-1 knapsack problems, the MHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its two improved algorithms (IHS and NGHS).

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