Multi-objective optimization of a CSP-based dish Stirling field layout using Genetic Algorithm and TOPSIS method: Case studies in Ouarzazate and Madrid

2022 ◽  
Vol 254 ◽  
pp. 115220
Author(s):  
H. Allouhi ◽  
A. Allouhi ◽  
A. Jamil
Keyword(s):  
Genetic Algorithm ◽  
Case Studies ◽  
Multi Objective Optimization ◽  
Topsis Method ◽  
Multi Objective

Single and Multi-objective Optimization of Processing Parameters for Fused Deposition Modeling in 3D Printing Technology

2020 ◽  
Vol 17 (1) ◽  
pp. 7542-7551 ◽  
Author(s):  
V. H. Nguyen ◽  
T. N. Huynh ◽  
T. P. Nguyen ◽  
T. T. Tran
Keyword(s):  
Genetic Algorithm ◽  
3D Printing ◽  
Case Studies ◽  
Fused Deposition Modeling ◽  
Low Cost ◽  
Processing Parameters ◽  
Fused Deposition Modelling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Fused Deposition

This paper presents practice and application of Design of Experiment techniques and Genetic Algorithm in single and multi-objective optimization with low cost, robustness, and high effectiveness through 3D printing case studies. 3D printing brings many benefits for engineering design, product development, and production process. However, it faces many challenges related to parameters control. The wrong parameter setup can result in excessive time, high production cost, waste material, and low-quality printing. This study is conducted to optimize the parameter sets for 3D Fused Deposition Modelling (FDM) products. The parameter sets, i.e., layer height, infill percentage, printing temperature, printing speed with different levels are experimented and analyzed to build mathematic models. The objectives are to describe the relationship between the inputs (parameter values) and the outputs (printing quality in term of weight, printing time and tensile strength of products). Single-objective and multi-objective models according to user’s desire are constructed and studied to identify the optimal set, optimal trade-off set of parameters. Besides, an integrated method of response surface methodology and Genetic algorithm to deal with multi-objective optimization is discussed in the paper. 3D printer, testing machines, and quality tools are used for doing experiments, measurement and collecting data. Minitab and Matlab software aid for analysis and decision-making. Proposed solutions for handling multi-objective optimization through 3D Fused Deposition Modelling product printing case study are practical and can extend for other case studies.

MULTI-OBJECTIVE OPTIMIZATION OF OFFSHORE TSUNAMI OBSERVATION POINTS BY GENETIC ALGORITHM

2020 ◽  
Vol 76 (2) ◽  
pp. I_325-I_330
Author(s):  
Kazutoshi KURAMOTO ◽  
Fumiyasu MAKINOSHIMA ◽  
Anawat SUPPASRI ◽  
Fumihiko IMAMURA
Keyword(s):  
Genetic Algorithm ◽  
Multi Objective Optimization ◽  
Multi Objective

Multi-objective optimization of injection-molded plastic parts using entropy weight, random forest, and genetic algorithm methods

2020 ◽  
Vol 40 (4) ◽  
pp. 360-371
Author(s):  
Yanli Cao ◽  
Xiying Fan ◽  
Yonghuan Guo ◽  
Sai Li ◽  
Haiyue Huang
Keyword(s):  
Genetic Algorithm ◽  
Random Forest ◽  
Regression Model ◽  
Process Parameters ◽  
Multi Objective Optimization ◽  
Entropy Weight ◽  
Multi Objective ◽  
Injection Molded ◽  
Plastic Parts

AbstractThe qualities of injection-molded parts are affected by process parameters. Warpage and volume shrinkage are two typical defects. Moreover, insufficient or excessively large clamping force also affects the quality of parts and the cost of the process. An experiment based on the orthogonal design was conducted to minimize the above defects. Moldflow software was used to simulate the injection process of each experiment. The entropy weight was used to determine the weight of each index, the comprehensive evaluation value was calculated, and multi-objective optimization was transformed into single-objective optimization. A regression model was established by the random forest (RF) algorithm. To further illustrate the reliability and accuracy of the model, back-propagation neural network and kriging models were taken as comparative algorithms. The results showed that the error of RF was the smallest and its performance was the best. Finally, genetic algorithm was used to search for the minimum of the regression model established by RF. The optimal parameters were found to improve the quality of plastic parts and reduce the energy consumption. The plastic parts manufactured by the optimal process parameters showed good quality and met the requirements of production.

Multi-objective optimization of a recuperative gas turbine cycle using non-dominated sorting genetic algorithm

2011 ◽  
Vol 225 (8) ◽  
pp. 1041-1051 ◽  
Author(s):  
H Sayyaadi ◽  
H R Aminian
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Heat Exchangers ◽  
Mixed Integer ◽  
Design Stage ◽  
Tube Length ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Gas Turbine Cycle

A regenerative gas turbine cycle with two particular tubular recuperative heat exchangers in parallel is considered for multi-objective optimization. It is assumed that tubular recuperative heat exchangers and its corresponding gas cycle are in design stage simultaneously. Three objective functions including the purchased equipment cost of recuperators, the unit cost rate of the generated power, and the exergetic efficiency of the gas cycle are considered simultaneously. Geometric specifications of the recuperator including tube length, tube outside/inside diameters, tube pitch, inside shell diameter, outer and inner tube limits of the tube bundle and the total number of disc and doughnut baffles, and main operating parameters of the gas cycle including the compressor pressure ratio, exhaust temperature of the combustion chamber and the air mass flowrate are considered as decision variables. Combination of these objectives anddecision variables with suitable engineering and physical constraints (including NO x and CO emission limitations) comprises a set of mixed integer non-linear problems. Optimization programming in MATLAB is performed using one of the most powerful and robust multi-objective optimization algorithms, namely non-dominated sorting genetic algorithm. This approach is applied to find a set of Pareto optimal solutions. Pareto optimal frontier is obtained, and a final optimal solution is selected in a decision-making process.

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