Optimization of Pre-Treatment Parameters before Diamond Coating Using Non-Dominated Sorting Genetic Algorithm (NSGA-II)

2012 ◽  
Vol 463-464 ◽  
pp. 399-405
Author(s):  
Abolfazl Golshan ◽  
Mostafa Rezazadeh Shirdar ◽  
Soheil Gohari ◽  
Mohammadfarid Alvansazyazdi
Keyword(s):  
Genetic Algorithm ◽  
Cobalt Content ◽  
Single Step ◽  
Etching Time ◽  
Work Piece ◽  
Nsga Ii ◽  
Quality Work ◽  
Short Period ◽  
Software Codes ◽  
Pre Treatment

In this study a single step of the chemical pre-treatment is implemented to tungsten carbide (WC 6 [%]) at the surface of the substrate in order to solve poor adhesion problem. During the pre-treatment process, numerous parameters such as etching time, acid temperature and concentration affect on the surface roughness and Cobalt content of WC-Co substrate are investigated. Optimal selection of these parameters is one of the significant issues to achieve high-quality work-piece in etching process. Thus, the statistical model based on nonlinear polynomial equations is developed for the different responses. Non-dominated Sorting Genetic Algorithm (NSGA-II) with the use of MATLAB Software codes is used to solve multi-objective optimization problem in order to provide a preferred solution for a process engineer in a short period of time.

scholarly journals Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User Preferences

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 344
Author(s):  
Alejandro Humberto García Ruiz ◽  
Salvador Ibarra Martínez ◽  
José Antonio Castán Rocha ◽  
Jesús David Terán Villanueva ◽  
Julio Laria Menchaca ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Energy Saving ◽  
User Satisfaction ◽  
Air Conditioning ◽  
State Of The Art ◽  
User Preferences ◽  
The State ◽  
Nsga Ii ◽  
Air Conditioning System

Electricity is one of the most important resources for the growth and sustainability of the population. This paper assesses the energy consumption and user satisfaction of a simulated air conditioning system controlled with two different optimization algorithms. The algorithms are a genetic algorithm (GA), implemented from the state of the art, and a non-dominated sorting genetic algorithm II (NSGA II) proposed in this paper; these algorithms control an air conditioning system considering user preferences. It is worth noting that we made several modifications to the objective function’s definition to make it more robust. The energy-saving optimization is essential to reduce CO2 emissions and economic costs; on the other hand, it is desirable for the user to feel comfortable, yet it will entail a higher energy consumption. Thus, we integrate user preferences with energy-saving on a single weighted function and a Pareto bi-objective problem to increase user satisfaction and decrease electrical energy consumption. To assess the experimentation, we constructed a simulator by training a backpropagation neural network with real data from a laboratory’s air conditioning system. According to the results, we conclude that NSGA II provides better results than the state of the art (GA) regarding user preferences and energy-saving.

scholarly journals Carbon Emission Reduction of Apparel Material Distribution Based on Multi-Objective Genetic Algorithm (NSGA-II)

2019 ◽  
Vol 11 (9) ◽  
pp. 2571
Author(s):  
Xujing Zhang ◽  
Lichuan Wang ◽  
Yan Chen
Keyword(s):  
Genetic Algorithm ◽  
Carbon Emissions ◽  
Optimal Solution ◽  
Material Distribution ◽  
Low Carbon ◽  
Nsga Ii ◽  
Management Objectives ◽  
Multi Objective Genetic Algorithm ◽  
Distribution Process ◽  
The Cost

Low-carbon production has become one of the top management objectives for every industry. In garment manufacturing, the material distribution process always generates high carbon emissions. In order to reduce carbon emissions and the number of operators to meet enterprises’ requirements to control the cost of production and protect the environment, the paths of material distribution were analyzed to find the optimal solution. In this paper, the model of material distribution to obtain minimum carbon emissions and vehicles (operators) was established to optimize the multi-target management in three different production lines (multi-line, U-shape two-line, and U-shape three-line), while the workstations were organized in three ways: in the order of processes, in the type of machines, and in the components of garment. The NSGA-II algorithm (non-dominated sorting genetic algorithm-II) was applied to obtain the results of this model. The feasibility of the model and algorithm was verified by the practice of men’s shirts manufacture. It could be found that material distribution of multi-line layout produced the least carbon emissions when the machines were arranged in the group of type.

Shape optimization of egg-shaped sewer pipes based on the nondominated sorting genetic algorithm (NSGA-II)

2022 ◽  
Vol 204 ◽  
pp. 111999
Author(s):  
Hanting Wu ◽  
Yangrui Huang ◽  
Lei Chen ◽  
Yingjie Zhu ◽  
Huaizheng Li
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Nsga Ii ◽  
Sewer Pipes ◽  
Nondominated Sorting

scholarly journals EFFECT OF ETCHING AS PRE-TREATMENT FOR ELECTROLESS COPPER PLATING ON SILICON WAFER

2017 ◽  
Vol 79 (7) ◽  
Author(s):  
Shazatul Akmaliah Mior Shahidin ◽  
Nor Akmal Fadil ◽  
Mohd Zamri Yusop ◽  
Mohd Nasir Tamin ◽  
Saliza Azlina Osman
Keyword(s):  
Silicon Wafer ◽  
Hydrofluoric Acid ◽  
Copper Film ◽  
Etching Time ◽  
Electroless Copper Plating ◽  
Cross Sectional ◽  
Electroless Process ◽  
Pre Treatment ◽  
Sectional Analysis ◽  
Mechanical Bonding

Metallic coatings, such as copper films can be easily deposited on semiconductor materials like silicon wafer without prior surface pre-treatment using the electroless process. However, the adhesion of the copper film can be very weak and can easily peels off. In this study, the effect of etching in hydrofluoric acid solution as a surface pre-treatment prior to electroless plating on silicon wafer was studied. The etching time in hydrofluoric acid was varied at 1, 3 and 5 minutes in order to investigate the adhesion behaviour of the coating layer. The surface morphology of the electroless plated samples was observed using a field emission scanning electron microscope (FESEM) and the coating thickness was measured using cross sectional analysis. The results showed that longer etching time (5 minutes) produced thicker Cu deposits (8.5μm) than 1 minute etching time (5μm). In addition, by increasing the etching time, the mechanical bonding between the copper film and the substrate is improved.

scholarly journals Multiobjective Optimization Methods for Congestion Management in Deregulated Power Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
K. Vijayakumar
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Electricity Market ◽  
Optimization Methods ◽  
Evolutionary Programming ◽  
Congestion Management ◽  
Transmission Congestion ◽  
Nsga Ii ◽  
Line Overload ◽  
Nondominated Sorting

Congestion management is one of the important functions performed by system operator in deregulated electricity market to ensure secure operation of transmission system. This paper proposes two effective methods for transmission congestion alleviation in deregulated power system. Congestion or overload in transmission networks is alleviated by rescheduling of generators and/or load shedding. The two objectives conflicting in nature (1) transmission line over load and (2) congestion cost are optimized in this paper. The multiobjective fuzzy evolutionary programming (FEP) and nondominated sorting genetic algorithm II methods are used to solve this problem. FEP uses the combined advantages of fuzzy and evolutionary programming (EP) techniques and gives better unique solution satisfying both objectives, whereas nondominated sorting genetic algorithm (NSGA) II gives a set of Pareto-optimal solutions. The methods propose an efficient and reliable algorithm for line overload alleviation due to critical line outages in a deregulated power markets. The quality and usefulness of the algorithm is tested on IEEE 30 bus system.

REFORM OF LEGAL REGULATION OF REMOTE WORK AS A STIMULATION FOR ENTREPRENEURSHIP DEVELOPMENT IN RUSSIA

2020 ◽  
pp. 136-143
Author(s):  
Maria A. Andrianova ◽  
Keyword(s):  
Labor Law ◽  
Legal Regulation ◽  
Remote Work ◽  
High Quality ◽  
Quality Work ◽  
Entrepreneurship Development ◽  
Short Period ◽  
The World ◽  
Work Done ◽  
Usual Order

The pandemic has created many difficulties for entrepreneurs around the world, including in Russia. As you know, difficulties, disrupting the usual order, can give impetus for radical changes that would not have a chance to be realized in times of peace and prosperity. It seems that remote mode is not suitable for all forms of employment, but if initially the employer assumes such an opportunity, the main problem is not the lack of the ability to control the employee, but ensuring effective communication with him and the ability to timely obtain the results of high-quality work done. It is noted that this goal can be achieved with the help of greater detail in local regulations of the order and conditions of interaction between the employee and the employer. One of the most promising consequences of the pandemic has been the reform of the legal regulation of remote work. In a very short period of time, remote work in Russia from an unviable rudiment has become one of the most progressive institutions, which has every chance of making all labor law more flexible and effective. Such labor law will undoubtedly become one of the incentives for the development of entrepreneurship in Russia.

scholarly journals A multivariable multiobjective predictive controller

2013 ◽  
Vol 23 (1) ◽  
pp. 35-45 ◽  
Author(s):  
Faten Ben Aicha ◽  
Faouzi Bouani ◽  
Mekki Ksouri
Keyword(s):  
Genetic Algorithm ◽  
Predictive Control ◽  
Mimo System ◽  
Weighting Factor ◽  
Nsga Ii ◽  
Weighted Sum Method ◽  
Prediction Horizon ◽  
Predictive Controller ◽  
Pareto Method ◽  
The Cost

Predictive control of MIMO processes is a challenging problem which requires the specification of a large number of tuning parameters (the prediction horizon, the control horizon and the cost weighting factor). In this context, the present paper compares two strategies to design a supervisor of the Multivariable Generalized Predictive Controller (MGPC), based on multiobjective optimization. Thus, the purpose of this work is the automatic adjustment of the MGPC synthesis by simultaneously minimizing a set of closed loop performances (the overshoot and the settling time for each output of the MIMO system). First, we adopt the Weighted Sum Method (WSM), which is an aggregative method combined with a Genetic Algorithm (GA) used to minimize a single criterion generated by the WSM. Second, we use the Non- Dominated Sorting Genetic Algorithm II (NSGA-II) as a Pareto method and we compare the results of both the methods. The performance of the two strategies in the adjustment of multivariable predictive control is illustrated by a simulation example. The simulation results confirm that a multiobjective, Pareto-based GA search yields a better performance than a single objective GA.

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