scholarly journals Optimizing the 3D Distributed Climate inside Greenhouses Using Multi-Objective Optimization Algorithms and Computer Fluid Dynamics

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2873 ◽  
Author(s):  
Kangji Li ◽  
Wenping Xue ◽  
Hanping Mao ◽  
Xu Chen ◽  
Hui Jiang ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Field Experiments ◽  
Climate Model ◽  
Pareto Frontier ◽  
Environmental Parameters ◽  
Optimization Method ◽  
Crop Growth ◽  
Control Variables ◽  
Greenhouse Production ◽  
Multi Objective

As one of the major production facilities in agriculture, a greenhouse has many spatial distributed factors influencing crop growth and energy consumption, such as temperature field, air flow pattern, CO 2 concentration distribution, etc. By introducing a hybrid computational fluid dynamics–evolutionary algorithm (CFD-EA) method, this paper constructs a micro-climate model of greenhouse with main environmental parameters optimized. Considering environmental factors’ spatial influences together with energy usage simultaneously, the optimal solutions of control variables for crop growth are calculated. A commercial greenhouse located in east China is chosen for the method validation. Field experiments using temperature/velocity sensor matrix are carried out for CFD accuracy investigation. On this basis, the proposed optimization method is employed to search for the optimal control variables and parameters corresponding to the environmental Pareto frontier. By the proposed multi-objective scheme, we believe the method can provide set point basis for the design and regulation of large/medium-sized greenhouse production with high spatial resolution.

scholarly journals A Kriging-Assisted Multi-Objective Constrained Global Optimization Method for Expensive Black-Box Functions

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 149
Author(s):  
Yaohui Li ◽  
Jingfang Shen ◽  
Ziliang Cai ◽  
Yizhong Wu ◽  
Shuting Wang
Keyword(s):  
Global Optimization ◽  
Pareto Frontier ◽  
Kriging Model ◽  
Optimization Method ◽  
Function Evaluation ◽  
Sampling Point ◽  
Constrained Global Optimization ◽  
Multi Objective ◽  
Sample Data ◽  
Sampling Points

The kriging optimization method that can only obtain one sampling point per cycle has encountered a bottleneck in practical engineering applications. How to find a suitable optimization method to generate multiple sampling points at a time while improving the accuracy of convergence and reducing the number of expensive evaluations has been a wide concern. For this reason, a kriging-assisted multi-objective constrained global optimization (KMCGO) method has been proposed. The sample data obtained from the expensive function evaluation is first used to construct or update the kriging model in each cycle. Then, kriging-based estimated target, RMSE (root mean square error), and feasibility probability are used to form three objectives, which are optimized to generate the Pareto frontier set through multi-objective optimization. Finally, the sample data from the Pareto frontier set is further screened to obtain more promising and valuable sampling points. The test results of five benchmark functions, four design problems, and a fuel economy simulation optimization prove the effectiveness of the proposed algorithm.

scholarly journals New multiobjective optimization algorithm using NBI-SASP approaches for mechanical structural problems

2022 ◽  
Vol 13 ◽  
pp. 4
Author(s):  
Samira El Moumen ◽  
Siham Ouhimmou
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Pareto Frontier ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Design Problems ◽  
Multi Objective ◽  
Structural Problems ◽  
Engineering Design Problems ◽  
Set Of Points

Various engineering design problems are formulated as constrained multi-objective optimization problems. One of the relevant and popular methods that deals with these problems is the weighted method. However, the major inconvenience with its application is that it does not yield a well distributed set. In this study, the use of the Normal Boundary Intersection approach (NBI) is proposed, which is effective in obtaining an evenly distributed set of points in the Pareto set. Given an evenly distributed set of weights, it can be strictly shown that this approach is absolutely independent of the relative scales of the functions. Moreover, in order to ensure the convergence to the Global Pareto frontier, NBI approach has to be aligned with a global optimization method. Thus, the following paper suggests NBI-Simulated Annealing Simultaneous Perturbation method (NBI-SASP) as a new method for multiobjective optimization problems. The study shall test also the applicability of the NBI-SASP approach using different engineering multi-objective optimization problems and the findings shall be compared to a method of reference (NSGA). Results clearly demonstrate that the suggested method is more efficient when it comes to search ability and it provides a well distributed global Pareto Front.

A Methodology for Trading-Off Performance and Robustness Under Uncertainty

2005 ◽  
Author(s):  
Zissimos P. Mourelatos ◽  
Jinghong Liang
Keyword(s):  
Design Optimization ◽  
Pareto Frontier ◽  
Mathematical Optimization ◽  
Optimization Method ◽  
Mean Value ◽  
Preference Aggregation ◽  
Efficient Design ◽  
Multi Objective ◽  
Reliability Based Design ◽  
Reliability And Robustness

Mathematical optimization plays an important role in engineering design, leading to greatly improved performance. Deterministic optimization however, may result in undesired choices because it neglects uncertainty. Reliability-based design optimization (RBDO) and robust design can improve optimization by considering uncertainty. This paper proposes an efficient design optimization method under uncertainty, which simultaneously considers reliability and robustness. A mean performance is traded-off against robustness for a given reliability level of all performance targets. This results in a probabilistic multi-objective optimization problem. Variation is expressed in terms of a percentile difference, which is efficiently computed using the Advanced Mean Value (AMV) method. A preference aggregation method converts the multi-objective problem to a single-objective problem, which is then solved using an RBDO approach. Indifference points are used to select the best solution without calculating the entire Pareto frontier. Examples illustrate the concepts and demonstrate their applicability.

Multi-objective optimization of auto-body fixture layout based on an ant colony algorithm

2019 ◽  
Vol 234 (6) ◽  
pp. 1137-1145
Author(s):  
Milad Khodabandeh ◽  
Maryam Ghassabzadeh Saryazdi ◽  
Abdolreza Ohadi
Keyword(s):  
Ant Colony Algorithm ◽  
Pareto Frontier ◽  
Optimization Method ◽  
Layout Optimization ◽  
Ant Colony ◽  
Car Industry ◽  
Multi Objective ◽  
Fixture Layout ◽  
Fixture Layout Optimization ◽  
Auto Body

Fixtures are extensively used in many industries such as the car industry, to locate and constrain the sheet part during the assembly stage. Fixture layout affects on deformation of sheet parts. Therefore, fixture layout optimization is crucial to the accuracy and quality of products. In addition, the number of clamps that uses in the fixture is another important factor that must be considered in fixture design. This article presents a novel fixture layout optimization method by combining multi-objective ant colony algorithm (M-ACO) and the finite element method. The proposed method optimizes the fixture layout and the number of clamps simultaneously as a multi-objective problem. An approximation of Pareto frontier is acquired by the proposed method. The fixture layout for the side reinforcement of a car is optimized using the proposed method. The results show that the proposed approach performs effectively to optimize the auto-body fixture layout.

Pareto-Optimal Solutions for a Truss Problem

2011 ◽  
Vol 423 ◽  
pp. 53-64
Author(s):  
W. El Alem ◽  
A. El Hami ◽  
Rachid Ellaia
Keyword(s):  
Optimization Problems ◽  
Optimization Technique ◽  
Pareto Frontier ◽  
Optimization Method ◽  
Simultaneous Optimization ◽  
Multi Objective Optimization ◽  
Recent Contribution ◽  
Multi Objective ◽  
Structural Problems ◽  
Nbi Method

Most optimization problems, particularly those in engineering design, require the simultaneous optimization of more than one objective function. In this context, the solutions of these problems are based on the Pareto frontier construction. Substantial efforts have been made in recent years to develop methods for the construction of Pareto frontiers that guarantee uniform distribution and exclude the non-Pareto and local Pareto points. The Normal Boundary Intersection (NBI) is a recent contribution that generates a well-distributed Pareto frontier efficiently. Nevertheless, this method should be combined with a global optimization method to ensure the convergence to the global Pareto frontier. This paper proposes the NBI method using Adaptive Simulated Annealing (ASA) algorithm, namely NBI-ASA as a global nonlinear multi-objective optimization method. A well known benchmark multi-objective problem has been chosen from the literature to demonstrate the validity of the proposed method, applicability of the method for structural problems has been tested through a truss problem and promising results were obtained. The results indicate that the proposed method is a powerful search and multi-objective optimization technique that may yield better solutions to engineering problems than those obtained using current algorithms.

Performance analysis and parameter optimization of an inner spiral in-pipe robot

Robotica ◽  
2014 ◽  
Vol 34 (2) ◽  
pp. 361-382 ◽  
Author(s):  
Liang Liang ◽  
Hui Peng ◽  
Bai Chen ◽  
Yong Tang ◽  
Sun Chen ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Blood Flow ◽  
Silicone Oil ◽  
Environmental Parameters ◽  
Optimization Method ◽  
Operating Parameters ◽  
Methyl Silicone ◽  
Flow Function ◽  
Working Principle ◽  
Dynamics Method

SUMMARYA novel micro in-pipe robot using an internally threaded profile for propulsion is proposed in this paper, and the dynamic model of the robot in the turbulent liquid pipeline is established, and the computational fluid dynamics method is used to solve the influence of environmental parameters and operating parameters on the robotic performance. By the orthogonal experimental optimization method, the optimal inner spiral structural parametrical combination is obtained. According to the working principle of the inner spiral robot, an inner spiral driving device is designed and fabricated, and the running experiment in the pipeline full of 201 methyl silicone oil verifies the feasibility of the proposed robot. Adopting the pulsating blood flow function as the inlet condition, in a pulsating period, the robotic performance is numerically analyzed.

Student Admission Assesment using Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA)

2018 ◽  
Author(s):  
Rivalri Kristianto Hondro ◽  
Mesran Mesran ◽  
Andysah Putera Utama Siahaan
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Selection Process ◽  
Initial Step ◽  
Optimization Method ◽  
Ratio Analysis ◽  
Multi Objective Optimization ◽  
Prospective Students ◽  
Multi Objective

Procurement selection process in the acceptance of prospective students is an initial step undertaken by private universities to attract superior students. However, sometimes this selection process is just a procedural process that is commonly done by universities without grouping prospective students from superior students into a class that is superior compared to other classes. To process the selection results can be done using the help of computer systems, known as decision support systems. To produce a better, accurate and objective decision result is used a method that can be applied in decision support systems. Multi-Objective Optimization Method by Ratio Analysis (MOORA) is one of the MADM methods that can perform calculations on the value of criteria of attributes (prospective students) that helps decision makers to produce the right decision in the form of students who enter into the category of prospective students superior.

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