scholarly journals Kriging-based optimization design for a new style shell with black box constraints

2017 ◽  
Vol 11 (3) ◽  
pp. 234-245 ◽  
Author(s):  
Huachao Dong ◽  
Baowei Song ◽  
Peng Wang
Keyword(s):  
Optimization Algorithm ◽  
Optimization Design ◽  
Autonomous Underwater Vehicle ◽  
Optimization Problems ◽  
Weight Ratio ◽  
Kriging Model ◽  
Black Box ◽  
Von Mises Stress ◽  
Design Parameters ◽  
Kriging Predictor

Complex engineering applications generally have the black box and computationally expensive characteristics. Surrogate-based optimization algorithms can effectively solve expensive black box optimization problems. This paper employs the kriging predictor to construct a surrogate model and uses an initial multistart optimization process to realize the global search on this kriging model. Based on a proposed trust region framework, a local search is carried out around the current promising solution. The whole optimization algorithm is implemented to solve a new style shell design of the autonomous underwater vehicle. Based on finite element analysis, buoyancy–weight ratio, maximum von Mises stress, and buckling critical load of the new style shell are calculated and stored as expensive sample values to construct the kriging model. Finally, the better design parameters of the new shell are obtained by this proposed optimization algorithm. In addition, compared with the traditional shell, the new shell shows the stronger stability and better buoyancy–weight ratio.

Multi-objective optimization of squirrel cage fan for range hood based on Kriging model

2021 ◽  
pp. 095440622199586
Author(s):  
Qianhao Xiao ◽  
Jun Wang ◽  
Boyan Jiang ◽  
Weigang Yang ◽  
Xiaopei Yang
Keyword(s):  
Flow Rate ◽  
Optimization Design ◽  
Volume Flow Rate ◽  
Kriging Model ◽  
Volume Flow ◽  
Design Parameters ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Squirrel Cage

In view of the multi-objective optimization design of the squirrel cage fan for the range hood, a blade parameterization method based on the quadratic non-uniform B-spline (NUBS) determined by four control points was proposed to control the outlet angle, chord length and maximum camber of the blade. Morris-Mitchell criteria were used to obtain the optimal Latin hypercube sample based on the evolutionary operation, and different subsets of sample numbers were created to study the influence of sample numbers on the multi-objective optimization results. The Kriging model, which can accurately reflect the response relationship between design variables and optimization objectives, was established. The second-generation Non-dominated Sorting Genetic algorithm (NSGA-II) was used to optimize the volume flow rate at the best efficiency point (BEP) and the maximum volume flow rate point (MVP). The results show that the design parameters corresponding to the optimization results under different sample numbers are not the same, and the fluctuation range of the optimal design parameters is related to the influence of the design parameters on the optimization objectives. Compared with the prototype, the optimized impeller increases the radial velocity of the impeller outlet, reduces the flow loss in the volute, and increases the diffusion capacity, which improves the volume flow rate, and efficiency of the range hood system under multiple working conditions.

scholarly journals Black-box combinatorial optimization using models with integer-valued minima

2020 ◽  
Author(s):  
Laurens Bliek ◽  
Sicco Verwer ◽  
Mathijs de Weerdt
Keyword(s):  
Optimization Algorithm ◽  
Surrogate Model ◽  
Optimization Problems ◽  
Random Search ◽  
Continuous Optimization ◽  
Optimal Solution ◽  
Black Box ◽  
Combinatorial Problems ◽  
Bayesian Optimization ◽  
Bayesian Optimization Algorithm

Abstract When a black-box optimization objective can only be evaluated with costly or noisy measurements, most standard optimization algorithms are unsuited to find the optimal solution. Specialized algorithms that deal with exactly this situation make use of surrogate models. These models are usually continuous and smooth, which is beneficial for continuous optimization problems, but not necessarily for combinatorial problems. However, by choosing the basis functions of the surrogate model in a certain way, we show that it can be guaranteed that the optimal solution of the surrogate model is integer. This approach outperforms random search, simulated annealing and a Bayesian optimization algorithm on the problem of finding robust routes for a noise-perturbed traveling salesman benchmark problem, with similar performance as another Bayesian optimization algorithm, and outperforms all compared algorithms on a convex binary optimization problem with a large number of variables.

scholarly journals A Kriging Model-Based Expensive Multiobjective Optimization Algorithm Using R2 Indicator of Expectation Improvement

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Ding Han ◽  
Jianrong Zheng
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Kriging Model ◽  
Utilization Efficiency ◽  
Function Evaluation ◽  
Good Convergence ◽  
Model Based ◽  
Multiobjective Optimization Problems ◽  
R2 Indicator

Most of the multiobjective optimization problems in engineering involve the evaluation of expensive objectives and constraint functions, for which an approximate model-based multiobjective optimization algorithm is usually employed, but requires a large amount of function evaluation. Aiming at effectively reducing the computation cost, a novel infilling point criterion EIR2 is proposed, whose basic idea is mapping a point in objective space into a set in expectation improvement space and utilizing the R2 indicator of the set to quantify the fitness of the point being selected as an infilling point. This criterion has an analytic form regardless of the number of objectives and demands lower calculation resources. Combining the Kriging model, optimal Latin hypercube sampling, and particle swarm optimization, an algorithm, EIR2-MOEA, is developed for solving expensive multiobjective optimization problems and applied to three sets of standard test functions of varying difficulty and comparing with two other competitive infill point criteria. Results show that EIR2 has higher resource utilization efficiency, and the resulting nondominated solution set possesses good convergence and diversity. By coupling with the average probability of feasibility, the EIR2 criterion is capable of dealing with expensive constrained multiobjective optimization problems and its efficiency is successfully validated in the optimal design of energy storage flywheel.

Application of Particle Swarm Optimization Based on Support Vector Machine in Multi-Objective Structure Optimization

2012 ◽  
Vol 201-202 ◽  
pp. 283-286
Author(s):  
Chen Yang Chang ◽  
Jing Mei Zhai ◽  
Qin Xiang Xia ◽  
Bin Cai
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Optimization Design ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Structure Optimization ◽  
Design Parameters ◽  
Support Vector ◽  
Swarm Optimization ◽  
Multi Objective

Aiming at addressing optimization problems of complex mathematical model with large amount of calculation, a method based on support vector machine and particle swarm optimization for structure optimization design was proposed. Support Vector Machine (SVM) is a powerful computational tool for problems with nonlinearity and could establish approximate structures model. Grey relational analysis was utilized to calculate the coefficient between target parameters in order to change the multi-objective optimization problem into a single objective one. The reconstructed models were solved by Particle Swam Optimization (PSO) algorithm. A slip cover at medical treatment was adopted as an example to illustrate this methodology. Appropriate design parameters were selected through the orthogonal experiment combined with ANSYS. The results show this methodology is accurate and feasible, which provides an effective strategy to solve complex optimization problems.

scholarly journals OPTIMASI DESAIN STENT PLA MENGGUNAKAN METODE RESPONSE SURFACE (RSM) UNTUK MEMPEROLAH FLEKSIBILITAS TERBAIK

2019 ◽  
Vol 8 (1) ◽  
pp. 48
Author(s):  
Sukiman B
Keyword(s):  
Blood Vessel ◽  
Response Surface ◽  
Optimization Design ◽  
Bending Moment ◽  
Von Mises Stress ◽  
Design Parameters ◽  
Angular Change ◽  
Minimum Stress ◽  
Von Mises ◽  
Safety Limit

The stent installation is one of cardiovascular disease treatments which is selected the most to handle patients with blood vessel disease. As the demand for stents increases, more researches are aimed at developing them. This study aims to obtain the optimal link design to produce the best flexibility to the change of stent angle with minimum stress so as not to injure blood vessel plaque. In this study, the stents are polymer stent with different types of links made with PLA materials with strut mirror (S><) design. The study was conducted on two stent configurations, namely crimped and expanded to determine the ability of angular change and maximum stress experienced by both when bending moment applied. The bending moment test was done through simulation based on finite element method in software Abaqus 6.14. The simulation results were then used as a model-making reference to determine the desired optimization design using the help of Minitab 18 software based on the response surface method. The results of this study indicate that the best optimal flexibility on crimped stent L1 to L5, which is the highest flexibility with von mises stress in the safety limit can be obtained based on a combination of link design parameters in the form of bending moment of 0.0074 N.mm with a thickness of 100 μm L3, and 0,0087 N.mm with a thickness of 106 μm L5. While at the expanded stent L1 to L5, the optimal link design parameter value for obtaining the best flexibility with von mises stress within the safety limit is a bending moment of 0.0075 N.mm with a thickness of 63.78 μm L3, 0.0067 N.mm with a thickness of 70 μm L5.

Seagull optimization algorithm for solving real-world design optimization problems

2020 ◽  
Vol 62 (6) ◽  
pp. 640-644 ◽  
Author(s):  
Natee Panagant ◽  
Nantiwat Pholdee ◽  
Sujin Bureerat ◽  
Khon Kaen ◽  
Ali Rıza Yıldız ◽  
...  
Keyword(s):  
Shape Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Kriging Model ◽  
Stress Constraint ◽  
Constraint Function ◽  
Element Analysis ◽  
Structural Shape ◽  
Whale Optimization ◽  
Structural Mass

AbstractIn this research paper, a new surrogate-assisted metaheuristic for shape optimization is proposed. A seagull optimization algorithm (SOA) is used to solve the shape optimization of a vehicle bracket. The design problem is to find structural shape while minimizing structural mass and meeting a stress constraint. Function evaluations are carried out using finite element analysis and estimated by using a Kriging model. The results show that SOA has outstanding features just as the whale optimization algorithm and salp swarm optimization algorithm for designing optimal components in the industry.

scholarly journals A self-adaptive population Rao algorithm for optimization of selected bio-energy systems

2020 ◽  
Author(s):  
R Venkata Rao ◽  
Hameer Singh Keesari
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Energy Systems ◽  
Design Parameters ◽  
Perturbation Equation ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Fitness Value ◽  
Self Adaptive

Abstract This work proposes a metaphor-less and algorithm-specific parameter-less algorithm, named as self-adaptive population Rao algorithm, for solving the single-, multi-, and many-objective optimization problems. The proposed algorithm adapts the population size based on the improvement in the fitness value during the search process. The population is randomly divided into four sub-population groups. For each sub-population, a unique perturbation equation is randomly allocated. Each perturbation equation guides the solutions toward different regions of the search space. The performance of the proposed algorithm is examined using standard optimization benchmark problems having different characteristics in the single- and multi-objective optimization scenarios. The results of the application of the proposed algorithm are compared with those obtained by the latest advanced optimization algorithms. It is observed that the results obtained by the proposed method are superior. Furthermore, the proposed algorithm is used to identify optimum design parameters through multi-objective optimization of a fertilizer-assisted microalgae cultivation process and many-objective optimization of a compression ignition biodiesel engine system. From the results of the computational tests, it is observed that the performance of the self-adaptive population Rao algorithm is superior or competitive to the other advanced optimization algorithms. The performances of the considered bio-energy systems are improved by the application of the proposed optimization algorithm. The proposed optimization algorithm is more robust and may be easily extended to solve single-, multi-, and many-objective optimization problems of different science and engineering disciplines.

scholarly journals Automated Algorithm Selection on Continuous Black-Box Problems by Combining Exploratory Landscape Analysis and Machine Learning

2019 ◽  
Vol 27 (1) ◽  
pp. 99-127 ◽  
Author(s):  
Pascal Kerschke ◽  
Heike Trautmann
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Ensemble Methods ◽  
Black Box ◽  
Small Sample ◽  
Landscape Analysis ◽  
Initial Population ◽  
Algorithm Selection ◽  
Practical Applications ◽  
High Performing

In this article, we build upon previous work on designing informative and efficient Exploratory Landscape Analysis features for characterizing problems' landscapes and show their effectiveness in automatically constructing algorithm selection models in continuous black-box optimization problems. Focusing on algorithm performance results of the COCO platform of several years, we construct a representative set of high-performing complementary solvers and present an algorithm selection model that, compared to the portfolio's single best solver, on average requires less than half of the resources for solving a given problem. Therefore, there is a huge gain in efficiency compared to classical ensemble methods combined with an increased insight into problem characteristics and algorithm properties by using informative features. The model acts on the assumption that the function set of the Black-Box Optimization Benchmark is representative enough for practical applications. The model allows for selecting the best suited optimization algorithm within the considered set for unseen problems prior to the optimization itself based on a small sample of function evaluations. Note that such a sample can even be reused for the initial population of an evolutionary (optimization) algorithm so that even the feature costs become negligible.

A Dynamic Optimization Design Method for the Turbine Engine Frame Foundation

2012 ◽  
Vol 226-228 ◽  
pp. 784-787
Author(s):  
Zhao Jun Li ◽  
Xi Cheng Wang
Keyword(s):  
Dynamic Optimization ◽  
Optimization Design ◽  
Optimization Problems ◽  
Design Method ◽  
Kriging Model ◽  
Optimization Method ◽  
Turbine Engine ◽  
Design Variables ◽  
Side Constraints ◽  
Dynamic Reanalysis

An effective optimization method using Kriging model and parametric sampling evaluation strategy is proposed to solve dynamic optimization design. The optimization problem is to find the design variables such that the structural weight is minimum and dynamic displacement of the points concerned plus certain side constraints are satisfied. The types of design variables are considered as the sizing variables of the beams and columns. Kriging model is used to build the approximate mapping relationship between the forced vibration amplitude and design variables, reducing expensive dynamic reanalysis. A dynamic analysis program is used as black-box to obtain dynamic response. Numerical examples show that the method has good accuracy and efficiency. Versatility of this method can be expected to play an important role in future engineering optimization problems.

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