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Literatura académica sobre el tema "NSGA-2"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 1 de febrero de 2022

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Artículos de revistas sobre el tema "NSGA-2"

Zhao, De Gao y Qiang Li. "Optimization of Vehicle-Borne Radar Antenna Pedestal Based on Modified NSGA-II". Advanced Materials Research 945-949 (junio de 2014): 2241–47. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.2241.

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This paper deals with application of Non-dominated Sorting Genetic Algorithm with elitism (NSGA-II) to solve multi-objective optimization problems of designing a vehicle-borne radar antenna pedestal. Five technical improvements are proposed due to the disadvantages of NSGA-II. They are as follow: (1) presenting a new method to calculate the fitness of individuals in population; (2) renewing the definition of crowding distance; (3) introducing a threshold for choosing elitist; (4) reducing some redundant sorting process; (5) developing a self-adaptive arithmetic cross and mutation probability. The modified algorithm can lead to better population diversity than the original NSGA-II. Simulation results prove rationality and validity of the modified NSGA-II. A uniformly distributed Pareto front can be obtained by using the modified NSGA-II. Finally, a multi-objective problem of designing a vehicle-borne radar antenna pedestal is settled with the modified algorithm.

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Liu, Changrong, Hanqing Wang, Yifang Tang y Zhiyong Wang. "Optimization of a Multi-Energy Complementary Distributed Energy System Based on Comparisons of Two Genetic Optimization Algorithms". Processes 9, n.º 8 (10 de agosto de 2021): 1388. http://dx.doi.org/10.3390/pr9081388.

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The development and utilization of low-carbon energy systems has become a hot topic of energy research in the international community. The construction of a multi-energy complementary distributed energy system (MCDES) is researched in this paper. Based on the multi-objective optimization theory, the planning optimization of an MCDES is studied, and a three-dimensional objective-optimization model is constructed by considering the constraints of the objective function and decision variables. Aiming at the optimization problem of building terminals for the MCDES studied in the paper, two genetic optimization algorithms—Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Non-Dominated Sorting Genetic Algorithm III (NSGA-III)—are used for calculation based on an example analysis. The constraint conditions of practical problems were added to the existing algorithms. Combined with the comparison of the solution quality and the optimal compromise solution of the two algorithms, a multi-decision method is proposed to obtain the optimal solution based on the Pareto optimal frontier of the two algorithms. Finally, the optimal decision scheme of the example is determined and the effectiveness and reliability of the optimization model are verified. Under the application of the MCDES optimization model studied in this paper, the iteration speed and hypervolume index of NSGA-III are found to be better than those of NSGA-II. The values of the life cycle cost and life cycle carbon emission objectives after the optimization of NSGA-III are indicated as 2% and 14% lower, respectively, than those of NSGA-II. The primary energy efficiency of NSGA-III is shown to be 20% higher than that of NSGA-II. According to the optimal decision, the energy operation strategies of the example MCDES with each typical day in the four seasons indicate that good integrated energy application and low-carbon operation performance are shown during the four-seasons operation process. The consumption of renewable energy is significant, which effectively reduces the application of high-grade energy. Thus, the theoretical guidance and engineering application reference are provided for MCDES design planning and operation optimization.

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Bu, Jian-Guo, Xu-Dong Lan, Ming Zhou y Kai-Xiong Lv. "Performance Optimization of Flywheel Motor by Using NSGA-2 and AKMMP". IEEE Transactions on Magnetics 54, n.º 6 (junio de 2018): 1–7. http://dx.doi.org/10.1109/tmag.2017.2784401.

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Islam, Q. N. U., S. M. Abdullah y M. A. Hossain. "Optimized Controller Design for an Islanded Microgrid using Non-dominated Sorting Sine Cosine Algorithm (NSSCA)". Engineering, Technology & Applied Science Research 10, n.º 4 (16 de agosto de 2020): 6052–56. http://dx.doi.org/10.48084/etasr.3468.

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In order to cope with the increasing energy demand, microgrids emerged as a potential solution which allows the designer a lot of flexibility. The optimization of the controller parameters of a microgrid ensures a stable and environment friendly operation. Non-dominated Sorting Sine Cosine Algorithm (NSSCA) is a hybrid of Sine Cosine Algorithm and Non-dominated Sorting technique. This algorithm is applied to optimize the control parameters of a microgrid which incorporates both static and dynamic load. The obtained results are compared with the results of the established Non-dominated Sorting Genetic Algorithm-II (NSGA-II) in order to justify the proposal of the NSSCA. The average time needed to converge in NSSCA is 7.617s whereas NSGA-II requires an average of 10.660s. Moreover, the required number of iterations for NSSCA is 2 which is significantly less in comparison to the 12 iterations in NSGA-II.

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Han, Woo Gyu, Woon Bae Park, Satendra Pal Singh, Myoungho Pyo y Kee-Sun Sohn. "Determination of possible configurations for Li0.5CoO2 delithiated Li-ion battery cathodes via DFT calculations coupled with a multi-objective non-dominated sorting genetic algorithm (NSGA-III)". Physical Chemistry Chemical Physics 20, n.º 41 (2018): 26405–13. http://dx.doi.org/10.1039/c8cp05284k.

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Sonata, Fifin y Dede Prabowo Wiguna. "Analisis Perbandingan Aggregat Of Function (AOF) dengan Non-Dominated Sorting Genetic Algorithm (NSGA-II) dalam Menentukan Optimasi Multi-Objective pada Penjadwalan Mesin Produksi Flow Shop". Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) 17, n.º 2 (29 de agosto de 2018): 158. http://dx.doi.org/10.53513/jis.v17i2.39.

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Penjadwalan mesin produksi dalam dunia industri memiliki peranan penting sebagai bentuk pengambilan keputusan. Salah satu jenis sistem penjadwalan mesin produksi adalah sistem penjadwalan mesin produksi tipe flow shop. Dalam penjadwalan flow shop, terdapat sejumlah pekerjaan (job) yang tiap-tiap job memiliki urutan pekerjaan mesin yang sama. Optimasi penjadwalan mesin produksi flow shop berkaitan dengan penyusunan penjadwalan mesin yang mempertimbangkan 2 objek yaitu makespan dan total tardiness. Optimasi kedua permasalahan tersebut merupakan optimasi yang bertolak belakang sehingga diperlukan model yang mengintegrasikan permasalahan tersebut dengan optimasi multi-objective A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimazitaion : NSGA-II. Dalam penelitian ini akan dibandingkan 2 buah metode yaitu Aggregat Of Function (AOF) dengan NSGA-II agar dapat terlihat nilai solusinya. Penyelesaian penjadwalan mesin produksi flow shop dengan algoritma NSGA-II untuk membangun jadwal dengan meminimalkan makespan dan total tardiness.Tujuan yang ingin dicapai adalah mengetahui bahwa model yang dikembangkan akan memberikan solusi penjadwalan mesin produksi flow shop yang efisien berupa solusi pareto optimal yang dapat memberikan sekumpulan solusi alternatif bagi pengambil keputusan dalam membuat penjadwalan mesin produksi yang diharapkan. Solusi pareto optimal yang dihasilkan merupakan solusi optimasi multi-objective yang optimal dengan trade-off terhadap seluruh objek, sehingga seluruh solusi pareto optimal sama baiknya.

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Vargas-Hákim, Gustavo-Adolfo, Efrén Mezura-Montes y Edgar Galván. "Evolutionary Multi-Objective Energy Production Optimization: An Empirical Comparison". Mathematical and Computational Applications 25, n.º 2 (16 de junio de 2020): 32. http://dx.doi.org/10.3390/mca25020032.

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This work presents the assessment of the well-known Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and one of its variants to optimize a proposed electric power production system. Such variant implements a chaotic model to generate the initial population, aiming to get a better distributed Pareto front. The considered power system is composed of solar, wind and natural gas power sources, being the first two renewable energies. Three conflicting objectives are considered in the problem: (1) power production, (2) production costs and (3) CO2 emissions. The Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is also adopted in the comparison so as to enrich the empirical evidence by contrasting the NSGA-II versions against a non-Pareto-based approach. Spacing and Hypervolume are the chosen metrics to compare the performance of the algorithms under study. The obtained results suggest that there is no significant improvement by using the variant of the NSGA-II over the original version. Nonetheless, meaningful performance differences have been found between MOEA/D and the other two algorithms.

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Tavassoli, Leyla Sadat, Reza Massah, Arsalan Montazeri, Mirpouya Mirmozaffari, Guang-Jun Jiang y Hong-Xia Chen. "A New Multiobjective Time-Cost Trade-Off for Scheduling Maintenance Problem in a Series-Parallel System". Mathematical Problems in Engineering 2021 (30 de junio de 2021): 1–13. http://dx.doi.org/10.1155/2021/5583125.

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In this paper, a modified model of Nondominated Sorting Genetic Algorithm 2 (NSGA-II), which is one of the Multiobjective Evolutionary Algorithms, is proposed. This algorithm is a new model designed to make a trade-off between minimizing the cost of preventive maintenance (PM) and minimizing the time taken to perform this maintenance for a series-parallel system. In this model, the limitations of labor and equipment of the maintenance team and the effects of maintenance issues on manufacturing problems are also considered. In the mathematical model, finding the appropriate objective functions for the maintenance scheduling problem requires all maintenance costs and failure rates to be integrated. Additionally, the effects of production interruption during preventive maintenance are added to objective functions. Furthermore, to make a better performance compared with a regular NSGA-II algorithm, we proposed a modified algorithm with a repository to keep more unacceptable solutions. These solutions can be modified and changed with the proposed mutation algorithm to acceptable solutions. In this algorithm, modified operators, such as simulated binary crossover and polynomial mutation, will improve the algorithm to generate convergence and uniformly distributed solutions with more diverse solutions. Finally, by comparing the experimental solutions with the solutions of two Strength Pareto Evolutionary Algorithm 2 (SPEA2) and regular NSGA-II, MNSGA-II generates more efficient and uniform solutions than the other two algorithms.

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Wu, Lianzhou, Tao Bai, Qiang Huang, Jian Wei y Xia Liu. "Multi-Objective Optimal Operations Based on Improved NSGA-II for Hanjiang to Wei River Water Diversion Project, China". Water 11, n.º 6 (2 de junio de 2019): 1159. http://dx.doi.org/10.3390/w11061159.

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It is important to investigate the laws of reservoir multi-objective optimization operations, because it can obtain the best benefits from inter-basin water transfer projects to mitigate water shortage in intake areas. Given the multifaceted demands of the Hanjiang to Wei River Water Diversion Project, China (referred hereafter as “the Project”), an easy-to-operate multi-objective optimal model based on simulation is built and applied to search the multi-objective optimization operation rules between power generation and energy consumption. The Project includes two reservoirs connected by a water transfer tunnel. One is Huangjinxia, located in the mainstream of Hanjiang with abundant inflow but no regulation ability, and the other is Sanhekou, located in the tributary of Hanjiang with multi-year regulation ability but less water. The layout of the Project increases the difficulty of reservoir joint optimization operations. Therefore, an improved Non-dominated Sorting Genetic Algorithm-II (I-NSGA-II) with a feasible search space is proposed to solve the model based on long-term series data. The results show that: (1) The validated simulation model is helpful to obtain Pareto front curves to reveal the rules between power generation and energy consumption. (2) Choosing a reasonable search step size to build a feasible search space based on simulation results for the I-NSGA-II can help find more optimized solutions. Considering the influence of the initial populations of the algorithm and limited computing ability of computers, the qualified rate of Pareto points solved by I-NSGA-II are superior to NSGA-II. (3) According to the characteristics of the Project, water transfer ratio threshold value of two reservoirs are quantified for maximize economic benefits. Moreover, the flood season is a critical operation period for the Project, in which both reservoirs should supply more water to intake areas to ensure the energy balanced of the entire system. The findings provide an easy-to-operate multi-objective operation model with the I-NSGA-II that can easily be applied in optimal management of inter-basin water transfer projects by relevant authorities.

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Liu, Da Wei, Xin Peng, Xin Xu y De Hua Chen. "Investigation on the Multi-Objective Optimization of Supercritical Airfoil Based on Nondominated Sorting Genetic Algorithm". Applied Mechanics and Materials 444-445 (octubre de 2013): 357–62. http://dx.doi.org/10.4028/www.scientific.net/amm.444-445.357.

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This paper aimed to investigate the multi-objective optimization method of supercritical airfoil. To achieve the optimal design of supercritical airfoil Rae2822, an improved NSGA-2 (Nondominated Sorting Genetic Algorithm) method was utilized, while the cross-operator and adaptive-variation operator were introduced to improve the convergence speed of the algorithm. During the optimization, the airfoil parametric modeling was achieved based on the Bezier-Bernstein method, and the objective function was obtained through solving the N-S equations. Considering the parallel computation characteristics of the algorithm, the computation was conducted in large-scale Linux computer system to reduce the solving time. Optimization results showed that the undominate solution with high quality obtained through the NSGA-2 method distributed evenly, which provided the designer a wider choosing space. It was also showed that the multi-objective optimization method presented in this paper was feasible and reliable.

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Tesis sobre el tema "NSGA-2"

Gurram, Karthik y Maheshwar Reddy Chappidi. "A Search-Based Approach for Robustness Testing of Web Applications". Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18459.

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Context: This thesis deals with the robustness testing of web applications on a different web browser using a Selenium WebDriver to automate the browser. To increase the efficiency of this automation testing, we are using a robustness method. Robustness method is a process of testing the behaviour of a system implementation under exceptional execution conditions to check if it still fulfils some robustness requirements. These robustness tests often apply random algorithms to select the actions to be executed on web applications. The search-based technique was used to automatically generate effective test cases, consisting of initial conditions and fault sequences. The success criteria in most cases: "if it does not crash or hang application, then it is robust". Problem: Software testing consumes a lot of time, labour-intensive to write test cases and expensive in a software development life cycle. There was always a need for software testing to decrease the testing time. Manual testing requires a lot of effort and hard work if we measure in terms of person per month [1]. To overcome this problem, we are using a search-based approach for robustness testing of web applications which can dramatically reduce the human effort, time and the costs related to testing. Objective: The purpose of this thesis is to develop an automated approach to carry out robustness testing of web applications focusing on revealing defects related to a sequence of events triggered by a web system. To do so, we will employ search-based techniques (e.g., NSGA-II algorithm [1]). The main focus is on Ericsson Digital BSS systems, with a special focus on robustness testing. The main purpose of this master thesis is to investigate how automated robustness testing can be done so that the effort of keeping the tests up to date is minimized when the functionality of the application changes. This kind of automation testing is well depended on the structure of the product being tested. In this thesis, the test object was structured in a way, which made the testing method simple for fault revelation and less time-consuming. Method: For this approach, a meta-heuristic search-based genetic algorithm is used to make efficiency for robustness testing of the web application. In order to evaluate the effectiveness of this proposed approach, the experimental procedure is adapted. For this, an experimental testbed is set up. The effectiveness of the proposed approach is measured by two objectives: Fault revelation, Test sequence length. The effectiveness is also measured by evaluating the feasible cost-effective output test cases. i Results:The results we collected from our approach shows that by reducing the test sequence length we can reduce the time consuming and by using the NSGA-2 algorithm we found as many faults as we can when we tested on web applications in Ericsson. Conclusion: The attempt of testing of web applications, was partly succeeded. This kind of robustness testing in our approach was strongly depended on the algorithm we are using. We can conclude that by using these two objectives, we can reduce the cost of testing and time consuming.

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Kršák, Martin. "Optimalizace procesů v logistice s podporou vizualizace". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-403163.

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The master thesis aims to design, implement, and compare algorithms that optimize processes in logistics, mainly in the planning phase. Heuristics and approximation genetic algorithms will find an near-optimal solution to NP-hard problem, such as the traveling salesman problem, with a delay less than several hours. The role of this algorithm is to plan an efficient route for garbage trucks that collect and distribute large-scale waste to waste yards in a specific city. The goal of the optimization is to minimize the shipping costs.

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Libros sobre el tema "NSGA-2"

Pkg: Pocket Nsg Skills and Fund of Nsg Vol. 1 and Vol. 2 2e. Davis Company, F. A., 2011.

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Wilkinson, Judith y F. A. Davis Company Staff. Pkg: Fund of Nsg Vol 1 and 2 2e + Pkt Nsg Skills + Skills Video Streaming. Davis Company, F. A., 2013.

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Lewis y Winningham. Ms Nsg 2 Vol 5e & Crit Thk Ms Set. Mosby International, 2000.

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F. A. Davis Company Staff y Richard Defendini. Pkg: Pkt Nsg Skills, Fund of Nsg Vol. 1 and Vol. 2 2e and Procedure Checklist 2e. Davis Company, F. A., 2011.

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Wilkinson, Judith y F. A. Davis Company Staff. Pkg: Fund of Nsg Vol 1 and 2 2e + Pkt Nsg Skills + Proc Checklist 2e + Skills Video Streaming. Davis Company, F. A., 2013.

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Davis, F. A. Pkg: Fund of Nsg Vol 1 and 2 2e and Davis Edge Funds for RN and Pkt Nsg Skills. Davis Company, F. A., 2014.

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Davis, F. A. Pkg: Fund of Nsg Vol 1 and 2 2e and Davis Edge Funds for RN and Pkt Nsg Skills and Proc Cklst 2e. Davis Company, F. A., 2014.

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Pkg: Fund of Nsg Vol 1 and 2 2e and Davis Edge Funds for RN and Pkt Nsg Skills and Skills Videos DVD 2e. Davis Company, F. A., 2014.

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Davis, F. A. Pkg: Fund of Nsg Vol 1 and 2 2e and Davis Edge Funds for RN and Pkt Nsg Skills and Skills Videos Unlimited Streaming 2e. Davis Company, F. A., 2014.

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Davis, F. A. Pkg: Fund of Nsg Vol 1 and 2 3e and RN Skills Video Access Card Unlimited Access and Proc Checklist 3e and Pkt Nsg Skills. Davis Company, F. A., 2015.

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Capítulos de libros sobre el tema "NSGA-2"

Girsang, Abba Suganda, Sfenrianto y Jarot S. Suroso. "Multi-objective Using NSGA-2 for Enhancing the Consistency-Matrix". En Proceedings of Second International Conference on Electrical Systems, Technology and Information 2015 (ICESTI 2015), 123–29. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-287-988-2_13.

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Pham, Huy-Tuan, Van-Khien Nguyen, Khac-Huy Nguyen, Quang-Khoa Dang, Trung-Kien Hoang y Son-Minh Pham. "Optimization Design of a 2-DOF Compliant Parallel Mechanism Using NSGA-II Algorithm for Vibration-Assisted Milling". En Springer Proceedings in Materials, 529–39. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45120-2_43.

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Ranjan, Soumya y Sudhansu Kumar Mishra. "Multi-objective Design Optimization of Three-Phase Induction Motor Using NSGA-II Algorithm". En Computational Intelligence in Data Mining - Volume 2, 1–8. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2208-8_1.

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Baun, Lisa. "Teil 2: Beihilfe zu NSG". En Beihilfe zu NS-Gewaltverbrechen, 254–431. Nomos Verlagsgesellschaft mbH & Co. KG, 2019. http://dx.doi.org/10.5771/9783845296746-254.

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Das, Sarat Kumar. "Multi-Objective Optimization of Slope Stability Using Wedge Analysis and Genetic Algorithm". En Advances in Computational Intelligence and Robotics, 221–39. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-4766-2.ch010.

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Slope stability of different waste containment systems is a matter of serious concern due to its impact on air, land, and water pollution, affecting human and aquatic lives. It has been observed that most of the waste containment slope failures are translational failure. In this chapter, the slope stability analysis of the waste containment is discussed with translational failure (wedge analysis) in single and multi-objective optimization framework using genetic algorithm (GA). Non-dominated sorting genetic algorithm II (NSGA-II) is found to efficient in developing the Pareto front in terms of factor of safety (FOS), height of embankment, and volume of the failed slope. The FOS decreased with increase in height of the slope and the volume of the slope also increased. The optimized slope in terms of different slope angle and with seismic coefficients is also discussed. Such a study will help the professional in deciding the height of the slope as per the FOS in a specified seismic zone.

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Roy, Supriyo, J. Paulo Davim y Kaushik Kumar. "Optimization of Process Parameters Using Taguchi Coupled Genetic Algorithm". En Mathematical Concepts and Applications in Mechanical Engineering and Mechatronics, 67–93. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1639-2.ch004.

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In the era of mass manufacturing, Material removal rate and Surface Roughness are of primary concern even in manufacturing using contemporary CNC machines. In this work, L27 Orthogonal Array of Taguchi method is selected for three parameters (Depth of cut, Feed and Speed) with three different levels to optimize the turning parameters for Material Removal Rate and Surface Roughness on an EMCO Concept Turn 105 CNC lathe for machining SAE 1020 material with carbide tool. The MRR and SR are observed as the objective to develop the combination of optimum cutting parameters. The objectives were optimized using Taguchi, Grey Taguchi and NSGA-II. The result from these techniques was compared to identify the optimal values of cutting parameters for maximum MRR, minimum SR and best combination of both. This study also produced a predictive equation for determining MRR and SR for a given set of parameters outside the considered values. Thus, with the proposed optimal parameters it is possible to increase the efficiency of machining process and decrease production cost in CNC Lathe.

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Actas de conferencias sobre el tema "NSGA-2"

Hashmi, Khayyam, Amal Alhosban, Erfan Najmi, Zaki Malik y Rezgui. "Automated Web service quality component negotiation using NSGA-2". En 2013 ACS International Conference on Computer Systems and Applications (AICCSA). IEEE, 2013. http://dx.doi.org/10.1109/aiccsa.2013.6616502.

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Junxi, Zheng, Cao Junhai y An Binlai. "Optimization Modeling and Decision Making of Equipment Maintenance Resource Scheduling Based on NSGA-2 Algorithm". En the 2019 3rd International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3319921.3319971.

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Li, Wenhui, Xuyang Wang, Pingliang Yuan, Ying Li, Qian Qu, Bo Xiao y Xinzhe Lan. "Research on multi link data diversion of power wireless heterogeneous network based on improved nsga-2". En HP3C'21: 2021 5th International Conference on High Performance Compilation, Computing and Communications. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3471274.3471286.

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Shi, Yu y Rolf D. Reitz. "Assessment of Multi-Objective Genetic Algorithms With Different Niching Strategies and Regression Methods for Engine Optimization and Design". En ASME 2009 Internal Combustion Engine Division Spring Technical Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/ices2009-76015.

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In previous study [1] the Non-dominated Sorting Genetic Algorithm II (NSGA II) [2] performed better than other popular Multi-Objective Genetic Algorithms (MOGA) in engine optimization that sought optimal combinations of the piston bowl geometry, spray targeting, and swirl ratio. NSGA II is further studied in this paper using different niching strategies that are applied to the objective-space and design-space, which diversify the optimal objectives and design parameters accordingly. Convergence and diversity metrics are defined to assess the performance of NSGA II using different niching strategies. It was found that use of the design niching achieved more diversified results with respect to design parameters, as expected. Regression was then conducted on the design datasets that were obtained from the optimizations with two niching strategies. Four regression methods, including K-nearest neighbors (KN), Kriging (KR), Neural Networks (NN), and Radial Basis Functions (RBF), were compared. The results showed that the dataset obtained from optimization with objective niching provided a more fitted learning space for the regression methods. The KN, KR, outperformed the other two methods with respect to the prediction accuracy. Furthermore, a log transformation to the objective-space improved the prediction accuracy for the KN, KR, and NN methods but not the RBF method. The results indicate that it is appropriate to use a regression tool to partly replace the actual CFD evaluation tool in engine optimization designs using the genetic algorithm. This hybrid mode saves computational resources (processors) without losing optimal accuracy. A Design of Experiment (DoE) method (the Optimal Latin Hypercube method) was also used to generate a dataset for the regression processes. However, the predicted results were much less reliable than results that were learned using the dynamically increasing datasets from the NSGA II generations. Applying the dynamical learning strategy during the optimization processes allows computationally expensive CFD evaluations to be partly replaced by evaluations using the regression techniques. The present study demonstrates the feasibility of applying the hybrid mode to engine optimization problems, and the conclusions can also extend to other optimization studies (numerical or experimental) that feature time-consuming evaluations and have highly non-linear objective-spaces.

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Nath, Rahul, Amit K. Shukla, Pranab K. Muhuri y Q. M. Danish Lohani. "NSGA-II based energy efficient scheduling in real-time embedded systems for tasks with deadlines and execution times as type-2 fuzzy numbers". En 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2013. http://dx.doi.org/10.1109/fuzz-ieee.2013.6622578.

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Wilding, Paul R., Nathan R. Murray y Matthew J. Memmott. "Design Optimization of PERCS in RELAP5 Using Parallel Processing and a Multi-Objective Non-Dominated Sorting Genetic Algorithm". En 2018 26th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/icone26-82389.

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Multi-objective optimization is a powerful tool that has been successfully applied to many fields but has seen minimal use in the design and development of nuclear power plant systems. When applied to design, multi-objective optimization involves the manipulation of key design parameters in order to develop optimal designs. These design parameters include continuous and/or discrete variables and represent the physical design specifications. They are modified across a specific design space to accomplish a number of set objective functions, representing the goals for both system design and performance, which conflict and cannot be combined into a single objective function. In this paper, a non-dominated sorting genetic algorithm (NSGA) and parallel processing in Python 3 were used to optimize the design of the passive endothermic reaction cooling system (PERCS) model developed in RELAP5/MOD 3.3. This system has been proposed as a retrofit to currently-operating light water reactors (LWR) and is designed to remove decay heat from the reactor core via the endothermic decomposition of magnesium carbonate (MgCO3) and natural circulation of the reactor coolant. The PERCS design is currently a shell-and-tube heat exchanger, with the coolant flowing through the tube side and MgCO3 on the shell side. During a station blackout (SBO), the PERCS initially keeps the reactor core outlet temperature from exceeding 635 K and then reduces it to below 620 K for 30 days. The optimization of the PERCS was performed with three different objectives: (1) minimization of equipment costs, (2) minimization of deviation of the core outlet temperature during a SBO from its normal operation steady-state value, and (3) minimization of fractional consumption of MgCO3, a metric that is measurable and directly related to the operating time of the PERCS. The manipulated parameters of the optimization include the radius of the PERCS shell, the pitch, hydraulic diameter, thickness and length of the PERCS tubes, and the elevation of the PERCS with respect to the reactor core. The NSGA methodology works by creating a population of PERCS options with varying design parameters. Using the evolutionary concepts of selection, reproduction, mutation, and survival of the fittest, the NSGA method repeatedly generates new PERCS options and gets rid of less fit ones. In the end, the result was a Pareto front of PERCS designs, each thermodynamically viable and optimal with respect to the three objectives. The Pareto front of options as a whole represents the optimized trade-off between the objectives.

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Sorkhabi, Sami Yamani Douzi, David A. Romero, Gary Kai Yan, Michelle Dao Gu, Joaquin Moran, Michael Morgenroth y Cristina H. Amon. "Multi-Objective Energy-Noise Wind Farm Layout Optimization Under Land Use Constraints". En ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-37063.

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Recently, the environmental impact of wind farms has been receiving increasing attention. As land is more extensively exploited for onshore wind farms, they are more likely to be in proximity with human dwellings, infrastructure (e.g. roads, transmission lines) and environmental features (e.g. rivers, lakes, forests). As a result of regulatory constraints, this proximity causes significant portions of the wind farm terrain to become unusable for turbine placement. In this work, we present a constrained, continuous-variable model for layout optimization that takes noise and energy as objective functions, based on Jensen’s wake model and ISO-9613-2 noise calculations. A multi-objective genetic algorithm (NSGA-II) is used to solve the optimization problem, considering a set of land use constraints, which are handled with static and dynamic penalty functions. A set of test cases with different number of turbines and percentages of land availability are solved. Results from this bi-objective optimization model illustrate how the severity of the land use constraints affects the trade-off between energy generation and noise production.

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Cravero, Carlo, Paolo Macelloni y Giuseppe Briasco. "Three-Dimensional Design Optimization of Multistage Axial Flow Turbines Using a RSM Based Approach". En ASME Turbo Expo 2012: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/gt2012-68040.

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The problem of the automatic design optimization for multistage axial flow turbines is considered and a design strategy based on a 3D Navier-Stokes solver and a RSM (Response Surface Method) approach is described. A multi-objective optimization code based on non-dominated sorting genetic algorithm (NSGA-2) is used to drive the optimization process in order to maximize the specific power while keeping the massflow rate constrained. In the present work the meridional channel is kept unchanged while for each blade the spanwise distribution of the profile restaggering is considered together with the inclusion of compound lean. The performance from the multistage turbine for the optimization loop are obtained from surrogate models built through a set of artificial neural networks. The neural networks are trained and tested using large DoEs and are not updated during the optimization process. This aspect is considered important to guarantee that the optimization converges to an optimum. The use of the 3D flow solver with coarse meshes in order to validate large DoEs in short times is discussed in some details. The above strategy has been applied to a four stage axial turbine from the open literature.

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Jovanova, Jovana, Mary Frecker, Reginald F. Hamilton y Todd A. Palmer. "Target Shape Optimization of Functionally Graded Shape Memory Alloy Compliant Mechanism". En ASME 2016 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/smasis2016-9070.

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Nickel Titanium (NiTi) shape memory alloys (SMAs) exhibit shape memory and/or superelastic properties, enabling them to demonstrate multifunctionality by engineering microstructural and compositional gradients at selected locations. This paper focuses on the design optimization of NiTi compliant mechanisms resulting in single-piece structures with functionally graded properties, based on user-defined target shape matching approach. The compositionally graded zones within the structures will exhibit an on demand superelastic effect (SE) response, exploiting the tailored mechanical behavior of the structure. The functional grading has been approximated by allowing the geometry and the superelastic properties of each zone to vary. The superelastic phenomenon has been taken into consideration using a standard nonlinear SMA material model, focusing only on 2 regions of interest: the linear region of higher Young’s modulus of elasticity and the superelastic region with significantly lower Young’s modulus of elasticity. Due to an outside load, the graded zones reach the critical stress at different stages based on their composition, position and geometry, allowing the structure morphing. This concept has been used to optimize the structures’ geometry and mechanical properties to match a user-defined target shape structure. A multi-objective evolutionary algorithm (NSGA II - Non-dominated Sorting Genetic Algorithm) for constrained optimization of the structure’s mechanical properties and geometry has been developed and implemented.

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Kwong, Wing Yin, Peter Y. Zhang, David Romero, Joaquin Moran, Michael Morgenroth y Cristina Amon. "Wind Farm Layout Optimization Considering Energy Generation and Noise Propagation". En ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71478.

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Wind farm design deals with the optimal placement of turbines in a wind farm. Past studies have focused on energy-maximization, cost-minimization or revenue-maximization objectives. As land is more extensively exploited for onshore wind farms, wind farms are more likely to be in close proximity with human dwellings. Therefore governments, developers, and landowners have to be aware of wind farms’ environmental impacts. After considering land constraints due to environmental features, noise generation remains the main environmental/health concern for wind farm design. Therefore, noise generation is sometimes included in optimization models as a constraint. Here we present continuous-location models for layout optimization that take noise and energy as objective functions, in order to fully characterize the design and performance spaces of the optimal wind farm layout problem. Based on Jensen’s wake model and ISO-9613-2 noise calculations, we used single- and multi-objective genetic algorithms (NSGA-II) to solve the optimization problem. Preliminary results from the bi-objective optimization model illustrate the trade-off between energy generation and noise production by identifying several key parts of Pareto frontiers. In addition, comparison of single-objective noise and energy optimization models show that the turbine layouts and the inter-turbine distance distributions are different when considering these objectives individually. The relevance of these results for wind farm layout designers is explored.

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