Soil Nailing Optimization Design Based on Improved Response Surface

2013 ◽  
Vol 671-674 ◽  
pp. 126-132
Author(s):  
Qiu Wang ◽  
Zhi Gang Song ◽  
Qing Xu
Keyword(s):  
Response Surface ◽  
Optimization Design ◽  
Uniform Design ◽  
Optimal Solution ◽  
Optimization Method ◽  
Gradient Algorithm ◽  
Soil Nailing ◽  
Global Optimal Solution ◽  
Global Optimal ◽  
Computationally Intensive

Gradient algorithm is difficult to obtain explicit analytic function of the optimization model, at the same time heuristic algorithm is computationally intensive with low speed and less efficient in soil nailing optimization. To overcome these problems, a new optimization method based on improved response surface (IRS) which constructed by uniform design (UD) and non-parametric regression (NR), is proposed. The soil nailing optimization is adopted by the combination of explicit analytic model based on IRS and composing program. The optimization process is explained and a soil nailing is optimized to verify the feasibility of the proposed method. The optimum results show that the introduction of UD and NR to construct the IRS calculate fast, do not need solving the specific analytic solution and can obtain global optimal solution.

Soil Nailing Optimization Design of Foundation Pit Based on Response Surface of SVM Technology

2013 ◽  
Vol 743 ◽  
pp. 27-30
Author(s):  
Tian Liu ◽  
Jing Cao ◽  
Hai Ming Liu ◽  
Hui Min Zhao
Keyword(s):  
Predictive Model ◽  
Response Surface ◽  
Optimization Design ◽  
Optimal Solution ◽  
Gradient Algorithm ◽  
Support Vector ◽  
Soil Nailing ◽  
Global Optimal Solution ◽  
Foundation Pit ◽  
Global Optimal

In order to study the soil nail optimization of foundation pit and obtain the global optimal solution, this paper establishes the predictive model of response surface by combining uniform test design(UD) and the support vector machine(SVM) technology. In order to avoid the disadvantages of large amount of calculation and low efficiency in heuristic algorithm, and to gain the optimal model of the explicitly analytical function in gradient algorithm, based on the predictive model of response surface under the constraint conditions, the foundation pit soil nailing is optimized through the application of enumerative algorithm. Through an engineering example, it is showed that is a time-saving method and don't need explicitly analytical model. Meanwhile, it can obtain the global optimal solution.

The Optimization Design of Wind Turbine Gearbox Based on Improved Genetic Algorithm and Feasibility Analysis

2014 ◽  
Vol 889-890 ◽  
pp. 107-112
Author(s):  
Ji Ming Tian ◽  
Xin Tan
Keyword(s):  
Genetic Algorithm ◽  
Wind Turbine ◽  
Optimization Design ◽  
Optimal Solution ◽  
Optimization Method ◽  
Strength Analysis ◽  
Improved Genetic Algorithm ◽  
Global Optimal Solution ◽  
Wind Turbine Gearbox ◽  
Global Optimal

The design of the gearbox must ensure the simplest structure and the lightest weight under the premise of meeting the reliability and life expectancy. According to the requirement of wind turbine, an improved method combined dynamic penalty function with pseudo-parallel genetic algorithm is used to optimize gearbox. It takes the minimum volumes as object functions. It is showed that the ability to search the global optimal solution of improved genetic algorithm and less number of iterations. The global optimal solution is worked out quickly. The size parameters are optimized, as much as the driving stability and efficiency. To verify the feasibility of improved genetic algorithm, ring gear of the gearbox is analyzed. Static strength analysis shows that the optimization method is reasonable and effective.

scholarly journals Heuristic approach applied to the optimum stratification problem

2021 ◽  
Author(s):  
José André Brito ◽  
Leonardo de Lima ◽  
Pedro Henrique González ◽  
Breno Oliveira ◽  
Nelson Maculan
Keyword(s):  
Variable Neighborhood Search ◽  
Optimal Solution ◽  
Optimization Method ◽  
Neighborhood Search ◽  
Optimal Solutions ◽  
Enumeration Algorithm ◽  
Global Optimal Solution ◽  
Optimal Sample ◽  
Precision Level ◽  
Global Optimal

The problem of finding an optimal sample stratification has been extensively studied in the literature. In this paper, we propose a heuristic optimization method for solving the univariate optimum stratification problem aiming at minimizing the sample size for a given precision level. The method is based on the variable neighborhood search metaheuristic, which was combined with an exact method. Numerical experiments were performed over a dataset of 24 instances, and the results of the proposed algorithm were compared with two very well-known methods from the literature. Our results outperformed $94\%$ of  the considered cases. In addition, we developed an enumeration algorithm to find the global optimal solution in some populations and scenarios, which enabled us to validate our metaheuristic method. Furthermore, we find that our algorithm obtained the global optimal solutions for the vast majority of the cases.

scholarly journals Global Simultaneous Optimization of Oil, Hysteretic and Inertial Dampers Using Real-Valued Genetic Algorithm and Local Search

2021 ◽  
Vol 7 ◽  
Author(s):  
Ryohei Uemura ◽  
Hiroki Akehashi ◽  
Kohei Fujita ◽  
Izuru Takewaki
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Optimal Solution ◽  
Optimization Method ◽  
Simultaneous Optimization ◽  
Global Optimality ◽  
Building Structures ◽  
Global Optimal Solution ◽  
Maximum Acceleration ◽  
Global Optimal

A method for global simultaneous optimization of oil, hysteretic and inertial dampers is proposed for building structures using a real-valued genetic algorithm and local search. Oil dampers has the property that they can reduce both displacement and acceleration without significant change of natural frequencies and hysteretic dampers possess the characteristic that they can absorb energy efficiently and reduce displacement effectively in compensation for the increase of acceleration. On the other hand, inertial dampers can change (prolong) the natural periods with negative stiffness and reduce the effective input and the maximum acceleration in compensation for the increase of deformation. By using the proposed simultaneous optimization method, structural designers can select the best choice of these three dampers from the viewpoints of cost and performance indices (displacement, acceleration). For attaining the global optimal solution which cannot be attained by the conventional sensitivity-based approach, a method including a real-valued genetic algorithm and local search is devised. In the first stage, a real-valued genetic algorithm is used for searching an approximate global optimal solution. Then a local search procedure is activated for enhancing the optimal character of the solutions by reducing the total quantity of three types of dampers. It is demonstrated that a better design from the viewpoint of global optimality can be obtained by the proposed method and the preference of damper selection strongly depends on the design target (displacement, acceleration). Finally, a multi-objective optimization for the minimum deformation and acceleration is investigated.

Slope Optimization Design Based on Improved Response Surface

2012 ◽  
Vol 170-173 ◽  
pp. 723-728
Author(s):  
Qiu Wang ◽  
Zhi Gang Song ◽  
Jing Cao
Keyword(s):  
Response Surface ◽  
Analytical Model ◽  
Optimization Design ◽  
Uniform Design ◽  
Optimization Method ◽  
Optimum Result ◽  
Computational Costs ◽  
Regression Effect ◽  
Safety Constraints ◽  
Enumerative Algorithm

Due to lacking of explicitly analytical model, optimization design of complex slope is often consulted to enumerative algorithm which generally results huge computation efforts. To overcome this problem, a new optimization method based on improved response surface (IRS) is proposed. The IRS, constructed by uniform design (UD) and non-parametric regression (NR), provides a regressed and explicitly analytical model through a few trial computations without prior assumed sliding surface. The optimization process is explained and a slope is optimized to verify the feasibility of the proposed method. The results show that the provided method can optimize a slope with multiple sliding surfaces and provide an optimum result under the safety constraints of the slope. Meanwhile, the introduction of UD and NR to construct the IRS can provide a better regression effect with fewer computational costs.

The Optimization Design of Multistage Hybrid Planetary Transmission Based on Improved Genetic Algorithm

2014 ◽  
Vol 532 ◽  
pp. 422-426
Author(s):  
Ji Ming Tian ◽  
Xin Tan
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Global Optimal Solution ◽  
Global Optimal ◽  
Driving Stability ◽  
Planetary Transmission ◽  
Minimum Volumes ◽  
Pseudo Parallel Genetic Algorithm

According to the characteristics of genetic algorithm, an improved method combined dynamic penalty function with pseudo-parallel genetic algorithm is presented in this paper and it can overcome the disadvantages of genetic algorithm for improving the efficiency of algorithm. The improved genetic algorithm is applied to optimization design of multistage hybrid planetary transmission. It takes the minimum volumes as object functions, and fully considered such constraint condition. It is showed that the ability to search the global optimal solution of improved genetic algorithm and less number of iterations. The global optimal solution is worked out quickly. Therefore, the size parameters are optimized, as much as the driving stability and efficiency. Compared to the original program, the volume of 16.55% is decreased.

AO-BBO: A Novel Optimization Algorithm and Its Application in Plant Drug Extraction

2019 ◽  
Vol 19 (2) ◽  
pp. 139-145 ◽  
Author(s):  
Bote Lv ◽  
Juan Chen ◽  
Boyan Liu ◽  
Cuiying Dong
Keyword(s):  
Optimization Algorithm ◽  
Learning Strategy ◽  
Optimal Solution ◽  
Population Diversity ◽  
Global Optimal Solution ◽  
Plant Medicine ◽  
Suitability Index ◽  
Sensing Model ◽  
Local Optimal Solution ◽  
Global Optimal

<P>Introduction: It is well-known that the biogeography-based optimization (BBO) algorithm lacks searching power in some circumstances. </P><P> Material & Methods: In order to address this issue, an adaptive opposition-based biogeography-based optimization algorithm (AO-BBO) is proposed. Based on the BBO algorithm and opposite learning strategy, this algorithm chooses different opposite learning probabilities for each individual according to the habitat suitability index (HSI), so as to avoid elite individuals from returning to local optimal solution. Meanwhile, the proposed method is tested in 9 benchmark functions respectively. </P><P> Result: The results show that the improved AO-BBO algorithm can improve the population diversity better and enhance the search ability of the global optimal solution. The global exploration capability, convergence rate and convergence accuracy have been significantly improved. Eventually, the algorithm is applied to the parameter optimization of soft-sensing model in plant medicine extraction rate. Conclusion: The simulation results show that the model obtained by this method has higher prediction accuracy and generalization ability.</P>

scholarly journals Best Proximity Point Results in Complex Valued Metric Spaces

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Binayak S. Choudhury ◽  
Nikhilesh Metiya ◽  
Pranati Maity
Keyword(s):  
Minimum Distance ◽  
Metric Spaces ◽  
Optimal Solution ◽  
Global Optimal Solution ◽  
Complex Valued Metric Space ◽  
Fixed Point Equation ◽  
Point Equation ◽  
Global Optimal ◽  
Metric Function ◽  
Complex Valued

We introduce the concept of proximity points for nonself-mappings between two subsets of a complex valued metric space which is a recently introduced extension of metric spaces obtained by allowing the metric function to assume values from the field of complex numbers. We apply this concept to obtain the minimum distance between two subsets of the complex valued metric spaces. We treat the problem as that of finding the global optimal solution of a fixed point equation although the exact solution does not in general exist. We also define and use the concept of P-property in such spaces. Our results are illustrated with examples.

A Systematic Optimization Design Method for Thermal Management of Passenger Vehicles

2021 ◽  
pp. 1-27
Author(s):  
Jie Zhang ◽  
Qidong Wang ◽  
Han Zhang ◽  
Min Zhang ◽  
Jianwei Lin
Keyword(s):  
Prediction Model ◽  
Surface Temperature ◽  
Thermal Management ◽  
Optimization Design ◽  
Cfd Simulation ◽  
Optimal Solution ◽  
Optimization Method ◽  
Drive Shaft ◽  
Management Problem ◽  
Systematic Optimization

Abstract In this study, a systematic optimization method for the thermal management problem of passenger vehicle was proposed. This article addressed the problem of the drive shaft sheath surface temperature exceeded allowable value. Initially, the causes and initial measures of the thermal problem were studied through computational fluid dynamics (CFD) simulation. Furthermore, the key measures and the relevant parameters were determined through Taguchi method and significance analysis. A prediction model between the parameters and optimization objective was built by radial basis function neural network (RBFNN). Finally, the prediction model and particle swarm optimization (PSO) algorithm were combined to calculate the optimal solution, and the optimal solution was selected for simulation and experiment verification. Experiment results indicated that this method reduced the drive shaft sheath surface temperature promptly, the decreasing amplitude was 22%, which was met the experimental requirements.

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