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Zhao, De Gao, and Qiang Li. "Optimization of Vehicle-Borne Radar Antenna Pedestal Based on Modified NSGA-II." Advanced Materials Research 945-949 (June 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, and Zhiyong Wang. "Optimization of a Multi-Energy Complementary Distributed Energy System Based on Comparisons of Two Genetic Optimization Algorithms." Processes 9, no. 8 (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, and Kai-Xiong Lv. "Performance Optimization of Flywheel Motor by Using NSGA-2 and AKMMP." IEEE Transactions on Magnetics 54, no. 6 (2018): 1–7. http://dx.doi.org/10.1109/tmag.2017.2784401.
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Islam, Q. N. U., S. M. Abdullah, and M. A. Hossain. "Optimized Controller Design for an Islanded Microgrid using Non-dominated Sorting Sine Cosine Algorithm (NSSCA)." Engineering, Technology & Applied Science Research 10, no. 4 (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, and 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, no. 41 (2018): 26405–13. http://dx.doi.org/10.1039/c8cp05284k.
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Sonata, Fifin, and 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, no. 2 (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, and Edgar Galván. "Evolutionary Multi-Objective Energy Production Optimization: An Empirical Comparison." Mathematical and Computational Applications 25, no. 2 (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, and Hong-Xia Chen. "A New Multiobjective Time-Cost Trade-Off for Scheduling Maintenance Problem in a Series-Parallel System." Mathematical Problems in Engineering 2021 (June 30, 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, and Xia Liu. "Multi-Objective Optimal Operations Based on Improved NSGA-II for Hanjiang to Wei River Water Diversion Project, China." Water 11, no. 6 (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, and De Hua Chen. "Investigation on the Multi-Objective Optimization of Supercritical Airfoil Based on Nondominated Sorting Genetic Algorithm." Applied Mechanics and Materials 444-445 (October 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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Eftekharian, Seyedeh, Mohammad Shojafar, and Shahaboddin Shamshirband. "2-Phase NSGA II: An Optimized Reward and Risk Measurements Algorithm in Portfolio Optimization." Algorithms 10, no. 4 (2017): 130. http://dx.doi.org/10.3390/a10040130.
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Ajith Arul Daniel, S., R. Kumar, S. VijayAnanth, and R. Pugazhenthi. "Multi-objective optimization of drilling of Al5059-SiC-2%MoS2 composites using NSGA-II." Materials Today: Proceedings 22 (2020): 853–57. http://dx.doi.org/10.1016/j.matpr.2019.11.031.
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Mirzaie-Nodoushan, Fahimeh, Omid Bozorg-Haddad, and Hugo A. Loáiciga. "Optimal design of groundwater-level monitoring networks." Journal of Hydroinformatics 19, no. 6 (2017): 920–29. http://dx.doi.org/10.2166/hydro.2017.044.
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Abstract Groundwater monitoring plays a significant role in groundwater management. This study presents an optimization method for designing groundwater-level monitoring networks. The proposed design method was used in the Eshtehard aquifer, in central Iran. Three scenarios were considered to optimize the locations of the observation wells: (1) designing new monitoring networks, (2) redesigning existing monitoring networks, and (3) expanding existing monitoring networks. The kriging method was utilized to determine groundwater levels at non-monitoring locations for preparing the design data base. The optimization of the groundwater monitoring network had the objectives of (1) minimizing the root mean square error and (2) minimizing the number of wells. The non-dominated sorting genetic algorithm (NSGA-II) was applied to optimize the network. Inverse distance weighting interpolation was used in NSGA-II to estimate the groundwater levels while optimizing network design. Results of the study indicate that the proposed method successfully optimizes the design of groundwater monitoring networks that achieve accuracy and cost-effectiveness.
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Chaube, A., L. Benyoucef, and M. K. Tiwari. "An adapted NSGA-2 algorithm based dynamic process plan generation for a reconfigurable manufacturing system." Journal of Intelligent Manufacturing 23, no. 4 (2010): 1141–55. http://dx.doi.org/10.1007/s10845-010-0453-9.
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Rajković, Miloš, Nenad Zrnić, Nenad Kosanić, Matej Borovinšek, and Tone Lerher Lerher. "A multi-objective optimization model for minimizing investment expenses, cycle times and CO2 footprint of an automated storage and retrieval systems." Transport 34, no. 3 (2019): 275–86. http://dx.doi.org/10.3846/transport.2019.9686.
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A new optimization model of Automated Storage and Retrieval Systems (AS/RS) containing three objective and four constraint functions is presented in this paper. Majority of the researchers and publications in material handling field had performed optimization of different decision variables, but with single objective function only. Most common functions are: minimum travel time, maximum throughput capacity, minimum cost, maximum energy efficiency, etc. To perform the simultaneous optimization of objective functions (minimum: “investment expenses”, “cycle times”, “CO 2 footprint”) the Non-dominated Sorting Genetic Algorithm II (NSGA II) was used. The NSGA II is a tool for finding the Pareto optimal solutions on the Pareto line. Determining the performance of the system is the main goal of our model. Since AS/RS are not flexible in terms of layout and organizational changes once the system is up and running, the proposed model could be a very helpful tool for the warehouse planners in the early stages of warehouse design
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Prajapati, Amarjeet. "A comparative study of many-objective optimizers on large-scale many-objective software clustering problems." Complex & Intelligent Systems 7, no. 2 (2021): 1061–77. http://dx.doi.org/10.1007/s40747-021-00270-8.
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AbstractOver the past 2 decades, several multi-objective optimizers (MOOs) have been proposed to address the different aspects of multi-objective optimization problems (MOPs). Unfortunately, it has been observed that many of MOOs experiences performance degradation when applied over MOPs having a large number of decision variables and objective functions. Specially, the performance of MOOs rapidly decreases when the number of decision variables and objective functions increases by more than a hundred and three, respectively. To address the challenges caused by such special case of MOPs, some large-scale multi-objective optimization optimizers (L-MuOOs) and large-scale many-objective optimization optimizers (L-MaOOs) have been developed in the literature. Even after vast development in the direction of L-MuOOs and L-MaOOs, the supremacy of these optimizers has not been tested on real-world optimization problems containing a large number of decision variables and objectives such as large-scale many-objective software clustering problems (L-MaSCPs). In this study, the performance of nine L-MuOOs and L-MaOOs (i.e., S3-CMA-ES, LMOSCO, LSMOF, LMEA, IDMOPSO, ADC-MaOO, NSGA-III, H-RVEA, and DREA) is evaluated and compared over five L-MaSCPs in terms of IGD, Hypervolume, and MQ metrics. The experimentation results show that the S3-CMA-ES and LMOSCO perform better compared to the LSMOF, LMEA, IDMOPSO, ADC-MaOO, NSGA-III, H-RVEA, and DREA in most of the cases. The LSMOF, LMEA, IDMOPSO, ADC-MaOO, NSGA-III, and DREA, are the average performer, and H-RVEA is the worst performer.
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Gupta, Rahul Dev, Pardeep Gupta, and Rajesh Khanna. "Parametric optimization of USM parameters by Taguchi and NSGA-II for the development of µ-channels on pure titanium." Grey Systems: Theory and Application 10, no. 2 (2020): 173–92. http://dx.doi.org/10.1108/gs-01-2020-0007.
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PurposeThis paper consolidates and presents the results of a work conducted to fabricate micro-channels on titanium grade-2 material by ultrasonic machining process (USM). In this research, the effects of important USM parameters, namely, kind of abrasives and its size, concentration of slurry, USM power rating and feed rate, have been probed on micro-channels quality for average surface roughness and process throughput in the form of material removal rate.Design/methodology/approachMultiple micro-channels on commercially pure titanium (i.e. Ti grade-2) have been fabricated in a single pass by employing micro-tool based USM process. Taguchi-based L18 (mixed level) OA has been selected for experimental design. Analysis of variance (ANOVA) study and regression modeling have also been done. Non-Dominated Sorting Genetic Algorithm (NSGA-II) has been used for process optimization to get optimum values of material removal rate (MRR) and surface roughness (SR).FindingsThe influence of important USM variables on SR and MRR have been investigated, and NSGA-II-based multi-response optimization has been done. The best surface roughness values obtained via NSGA-II solution for SiC and B4C are 0.354 µm and 1.303 µm, respectively. Scanned electron microscopic investigation proves the fabrication of micro-channels with smooth surfaces, and minimum burrs and other defects. The material removed from the surface was due to ductile fractures.Originality/valueMiniaturization is a modern trend these days to solve many precision, scientific and industrial problems. To manufacture precise micro-products, shapes and features, advanced and micro-machining processes can play a very prominent role. Micro-channels are typical micro-features required in micro-fluidic applications like micro heat exchangers and micro-pumps. Exhaustive review of existing research work indicated that precision micromachining of various materials can be effectively performed using USM, though not much work has been undertaken to explore the feasibility of multiple micro-channels in a single run using USM. The current work fulfills the gap, where multiple micro-channels on commercially pure titanium (i.e. Ti grade-2) have been fabricated in a single pass by employing micro-tool-based USM process.
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Liu, Mingcong, Shaobo Yang, Hongyu Li, Jiayi Xu, and Xingfei Li. "Energy Consumption Analysis and Optimization of the Deep-Sea Self-Sustaining Profile Buoy." Energies 12, no. 12 (2019): 2316. http://dx.doi.org/10.3390/en12122316.
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In order to reduce the energy consumption of deep-sea self-sustaining profile buoy (DSPB) and extend its running time, a stage quantitative oil draining control mode has been proposed in this paper. System parameters have been investigated including oil discharge resolution (ODR), judgment threshold of the floating speed and frequency of oil draining on the energy consumption of the system. The single-objective optimization model with the total energy consumption of DSPB’s ascent stage as the objective function has been established by combining the DSPB’s floating kinematic model. At the same time, as the static working current of the DSPB can be further optimized, a multi-objective energy consumption optimization model with the floating time and the energy consumption of the oil pump motor as objective functions has been established. The non-dominated sorted genetic algorithm-II (NSGA-II) has been employed to optimized the energy consumption model in the ascent stage of the DSPB. The results showed that the NSGA-II method has a good performance in the energy consumption optimization of the DSPB, and can reduce the dynamic energy consumption in the floating process by 28.9% within 2 h considering the increase in static energy consumption.
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Zhu, Jian Rong, Yi Zhuang, Jing Li, and Wei Zhu. "Virtual Machines Scheduling Algorithm Based on Multi-Objective Optimization in Cloud Computing." Advanced Materials Research 1046 (October 2014): 508–11. http://dx.doi.org/10.4028/www.scientific.net/amr.1046.508.
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How to reduce energy consumption while improving utility of datacenter is one of the key technologies in the cloud computing environment. In this paper, we use energy consumption and utility of data center as objective functions to set up a virtual machine scheduling model based on multi-objective optimization VMSA-MOP, and design a virtual machine scheduling algorithm based on NSGA-2 to solve the model. Experimental results show that compared with other virtual machine scheduling algorithms, our algorithm can obtain relatively optimal scheduling results.
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Li, Li, Yanhong Chen, Zhifeng Lin, et al. "Association of pre-pregnancy body mass index with adverse pregnancy outcome among first-time mothers." PeerJ 8 (October 14, 2020): e10123. http://dx.doi.org/10.7717/peerj.10123.
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Background Studies have reported an increased risk of adverse pregnancy outcome associated with pre-pregnancy body mass index (BMI). However, the data on such associations in urban areas of southern Chinese women is limited, which drive us to clarify the associations of pre-pregnancy BMI and the risks of adverse pregnancy outcomes (preterm birth (PTB) and low birth weight (LBW)) and maternal health outcomes (gestational hypertension and cesarean delivery). Methods We performed a hospital-based case-control study including 3,864 Southern Chinese women who gave first birth to a live singleton infant from January 2015 to December 2015. PTB was stratified into three subgroups according to gestational age (extremely PTB, very PTB and moderate PTB). Besides, we combined birth weight and gestational age to dichotomise as being small for gestational age (SGA, less than the tenth percentile of weight for gestation) and non-small for gestational age (NSGA, large than the tenth percentile of weight for gestation), gestational week was also classified into categories of term, 34-36 week and below 34 week.. We then divided newborns into six groups: (1) term and NSGA; (2) 34–36 week gestation and NSGA; (3) below 34 week gestation and NSGA; (4) term and SAG; (5) 34–36 week gestation and SAG; (6) below 34 week gestation and SAG. Adjusted logistic regression models was used to estimate the odds ratios of adverse outcomes. Results Underweight women were more likely to give LBW (AOR = 1.44, 95% CI [1.11–1.89]), the similar result was seen in term and SAG as compared with term and NSAG (AOR = 1.78, 95% CI [1.45–2.17]), whereas underweight was significantly associated with a lower risk of gestational hypertension (AOR = 0.45, 95% CI [0.25–0.82) and caesarean delivery (AOR = 0.74, 95% CI [0.62–0.90]). The risk of extremely PTB is relatively higher among overweight and obese mothers in a subgroup analysis of PTB (AOR = 8.12, 95% CI [1.11–59.44]; AOR = 15.06, 95% CI [1.32–172.13], respectively). Both maternal overweight and obesity were associated with a greater risk of gestational hypertension (AOR = 1.71, 95% CI [1.06–2.77]; AOR = 5.54, 95% CI [3.02–10.17], respectively) and caesarean delivery (AOR = 1.91, 95% CI [1.53–2.38]; AOR = 1.85, 95% CI [1.21–2.82], respectively). Conclusions Our study suggested that maternal overweight and obesity were associated with a significantly higher risk of gestational hypertension, caesarean delivery and extremely PTB. Underweight was correlated with an increased risk of LBW and conferred a protective effect regarding the risk for gestational hypertension and caesarean delivery for the first-time mothers among Southern Chinese.
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Bu, Jian-guo, Ming Zhou, Xu-dong Lan, and Kai-xiong Lv. "Optimization for Airgap Flux Density Waveform of Flywheel Motor Using NSGA-2 and Kriging Model Based on MaxPro Design." IEEE Transactions on Magnetics 53, no. 8 (2017): 1–7. http://dx.doi.org/10.1109/tmag.2017.2702758.
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Fu, Jisi, Ping-An Zhong, Bin Xu, Feilin Zhu, Juan Chen, and Jieyu Li. "Comparison of Transboundary Water Resources Allocation Models Based on Game Theory and Multi-Objective Optimization." Water 13, no. 10 (2021): 1421. http://dx.doi.org/10.3390/w13101421.
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Transboundary water resources allocation is an effective measure to resolve water-related conflicts. Aiming at the problem of water conflicts, we constructed water resources allocation models based on game theory and multi-objective optimization, and revealed the differences between the two models. We compare the Pareto front solved by the AR-MOEA method and the NSGA-II method, and analyzed the difference between the Nash–Harsanyi Leader–Follower game model and the multi-objective optimization model. The Huaihe River basin was selected as a case study. The results show that: (1) The AR-MOEA method is better than the NSGA-II method in terms of the diversity metric (Δ); (2) the solution of the asymmetric Nash–Harsanyi Leader–Follower game model is a non-dominated solution, and the asymmetric game model can obtain the same water resources allocation scheme of the multi-objective optimal allocation model under a specific preference structure; (3) after the multi-objective optimization model obtains the Pareto front, it still needs to construct the preference information of the Pareto front for a second time to make the optimal solution of a multi-objective decision, while the game model can directly obtain the water resources allocation scheme at one time by participating in the negotiation. The results expand the solution method of water resources allocation models and provide support for rational water resources allocation.
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Czajkowska, Anna M., and Tiku T. Tanyimboh. "Water distribution network optimization using maximum entropy under multiple loading patterns." Water Supply 13, no. 5 (2013): 1265–71. http://dx.doi.org/10.2166/ws.2013.119.
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This paper proposes a maximum entropy-based multi-objective genetic algorithm approach for the design optimization of water distribution networks (WDNs). The novelty is that in contrast to previous research involving statistical entropy the algorithm can handle multiple operating conditions. We used NSGA II and EPANET 2 and wrote a subroutine that calculates the entropy value for any given WDN configuration. The proposed algorithm is demonstrated by designing a six-loop network that is well known from previous entropy studies. We used statistical entropy to include reliability in the design optimization procedure in a computationally efficient way.
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Wang, Ning-Ning, Jie Dong, Yin-Hua Deng, et al. "ADME Properties Evaluation in Drug Discovery: Prediction of Caco-2 Cell Permeability Using a Combination of NSGA-II and Boosting." Journal of Chemical Information and Modeling 56, no. 4 (2016): 763–73. http://dx.doi.org/10.1021/acs.jcim.5b00642.
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Aguilar-Rivera, Anton. "A GPU fully vectorized approach to accelerate performance of NSGA-2 based on stochastic non-domination sorting and grid-crowding." Applied Soft Computing 88 (March 2020): 106047. http://dx.doi.org/10.1016/j.asoc.2019.106047.
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Vonk, E., Y. P. Xu, M. J. Booij, and D. C. M. Augustijn. "Quantifying the robustness of optimal reservoir operation for the Xinanjiang-Fuchunjiang Reservoir Cascade." Water Supply 16, no. 1 (2015): 79–86. http://dx.doi.org/10.2166/ws.2015.116.
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In this research we investigate the robustness of the common implicit stochastic optimization (ISO) method for dam reoperation. As a case study, we focus on the Xinanjiang-Fuchunjiang reservoir cascade in eastern China, for which adapted operating rules were proposed as a means to reduce the impact of climate change and socio-economic developments. The optimizations were based on five different water supply and demand scenarios for the future period from 2011 to 2040. Main uncertainties in the optimization can be traced back to correctness of the assumed supply and demand scenarios and the quality and tuning of the applied optimization algorithm. To investigate the robustness of proposed operation rules, we (1) compare cross-scenario performance of all obtained Pareto-optimal rulesets and (2) investigate whether different metaheuristic optimization algorithms lead to the same results. For the latter we compare the originally used genetic algorithm (Nondominated Sorting Genetic Algorithm II, NSGA-II) with a particle swarm optimization algorithm (MOPSO). Reservoir performance was measured using the shortage index (SI) and mean annual energy production (MAEP) as main indicators. It is found that optimal operating rules, tailored to a specific scenario, deliver at most 2.4% less hydropower when applied to a different scenario, while the SI increases at most with 0.28. NSGA-II and MOPSO are shown to yield approximately the same Pareto-front for all scenarios, even though small differences can be observed.
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Qiao, Zong Liang, Lei Zhang, Feng Qi Si та Zhi Gao Xu. "Multi-Objective Optimum Design for Wave-Plate Demister Based on NSGA-ΙΙ Algorithm". Advanced Materials Research 864-867 (грудень 2013): 1163–67. http://dx.doi.org/10.4028/www.scientific.net/amr.864-867.1163.
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For optimizing the structure design of the wave-plate demister vanes in wet flue gas desulfurization system (WFGD) of power plants, the characteristics models of removal efficiency and pressure drop were established by using least squares support vector machine (LSSVM) based on numerical simulation results. The highest relative error between the predicted output and measured value is 2%, it proves the modeling is good for the prediction. Based on the characteristics models, a multi-objective optimization model was established. It used the structural parameters as the optimal variables and the demister characteristics as the objective function. This optimization model was solved by non dominated sorting genetic algorithm (NSGA-II). The simulation data show that the Multi-objective optimum method can get more effective results compared to the weight coefficient method.
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Eswari, J. Satya, and Shadab Ahmad. "TRIPLE OBJECTIVE OPTIMIZATION OF CYTOTOXIC POTENCY OF HUMAN CARCINOMA CELL LINES OF ULVA FASCIATA DELILE (A MARINE MACRO ALGA) USING NON-SORTING GENETIC ALGORITHM." International Journal of Pharmacy and Pharmaceutical Sciences 8, no. 9 (2016): 79. http://dx.doi.org/10.22159/ijpps.2016.v8i9.12003.
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<p><strong>Objective: </strong>The main purpose of our work was to arrive at an acceptable model for optimizing the cytotoxic potency of<em> Ulva fasciata </em>Delile extract on human carcinoma cell lines of which can provide believable indications as compared to experimental results.</p><p><strong>Methods: </strong>The experimental result for cytotoxic potency of a methanolic extract of the <em>Ulva fasciata </em>Delile (MEUF) with a 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay against human colon carcinoma (HT-29), human hepatocyte carcinoma (Hep-G2), and human breast carcinoma (MCF-7) cell lines was used to carry out a multi-objective (triple objective) optimization. Thirty non-dominating solutions were considered for analyses of absorbance (y<sub>1</sub>), % cell survival (y<sub>2</sub>) and% cell inhibition (y<sub>3</sub>) data.</p><p><strong>Results: </strong>The model developed using non-dominated sorting genetic algorithm (NSGA) was compared with data obtained experimentally and the results were found to be significant. This method has distinct advantages over other methods which relied heavily on statistical-regression-models, in the sense that it does triple-objective optimization. The resulted in obtaining solutions which were not only significant or believable, but it also corroborated well with experimental results. Thus the solutions obtained during optimization provided the necessary data for generating a successful model.</p><strong>Conclusion: </strong>The solutions obtained by NSGA method helped to build an acceptable model for optimizing the cytotoxic potency of <em>Ulva fasciata </em>Delile on human carcinoma cell lines.
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Oraei Zare, S., B. Saghafian, and A. Shamsai. "Multi-objective optimization for combined quality–quantity urban runoff control." Hydrology and Earth System Sciences 16, no. 12 (2012): 4531–42. http://dx.doi.org/10.5194/hess-16-4531-2012.
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Abstract. Urban development affects the quantity and quality of urban surface runoff. In recent years, the best management practices (BMPs) concept has been widely promoted for control of both quality and quantity of urban floods. However, means to optimize the BMPs in a conjunctive quantity/quality framework are still under research. In this paper, three objective functions were considered: (1) minimization of the total flood damages, cost of BMP implementation and cost of land-use development; (2) reducing the amount of TSS (total suspended solid) and BOD5 (biological oxygen demand), representing the pollution characteristics, to below the threshold level; and (3) minimizing the total runoff volume. The biological oxygen demand and total suspended solid values were employed as two measures of urban runoff quality. The total surface runoff volume produced by sub-basins was representative of the runoff quantity. The construction and maintenance costs of the BMPs were also estimated based on the local price standards. Urban runoff quantity and quality in the case study watershed were simulated with the Storm Water Management Model (SWMM). The NSGA-II (Non-dominated Sorting Genetic Algorithm II) optimization technique was applied to derive the optimal trade off curve between various objectives. In the proposed structure for the NSGA-II algorithm, a continuous structure and intermediate crossover were used because they perform better as far as the optimization efficiency is concerned. Finally, urban runoff management scenarios were presented based on the optimal trade-off curve using the k-means method. Subsequently, a specific runoff control scenario was proposed to the urban managers.
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Shankar, K., and Akshay S. Baviskar. "Improved hybrid Strength Pareto Evolutionary Algorithms for multi-objective optimization." International Journal of Intelligent Computing and Cybernetics 11, no. 1 (2018): 20–46. http://dx.doi.org/10.1108/ijicc-12-2016-0063.
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Purpose The purpose of this paper is to design an improved multi-objective algorithm with better spread and convergence than some current algorithms. The proposed application is for engineering design problems. Design/methodology/approach This study proposes two novel approaches which focus on faster convergence to the Pareto front (PF) while adopting the advantages of Strength Pareto Evolutionary Algorithm-2 (SPEA2) for better spread. In first method, decision variables corresponding to the optima of individual objective functions (Utopia Point) are strategically used to guide the search toward PF. In second method, boundary points of the PF are calculated and their decision variables are seeded to the initial population. Findings The proposed methods are tested with a wide range of constrained and unconstrained multi-objective test functions using standard performance metrics. Performance evaluation demonstrates the superiority of proposed algorithms over well-known existing algorithms (such as NSGA-II and SPEA2) and recent ones such as NSLS and E-NSGA-II in most of the benchmark functions. It is also tested on an engineering design problem and compared with a currently used algorithm. Practical implications The algorithms are intended to be used for practical engineering design problems which have many variables and conflicting objectives. A complex example of Welded Beam has been shown at the end of the paper. Social implications The algorithm would be useful for many design problems and social/industrial problems with conflicting objectives. Originality/value This paper presents two novel hybrid algorithms involving SPEA2 based on: local search; and Utopia point directed search principles. This concept has not been investigated before.
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Modrak, Vladimir, Ranjitharamasamy Sudhakara Pandian, and Shanmugakani Senthil Kumar. "Parametric Study of Wire-EDM Process in Al-Mg-MoS2 Composite Using NSGA-II and MOPSO Algorithms." Processes 9, no. 3 (2021): 469. http://dx.doi.org/10.3390/pr9030469.
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Al-Mg-based composite is used in producing a variety of components. To improve the machinability of the composite, MoS2 is added. For characterizing the machining of the Al-Mg-based composite, different wt.% (2, 4, and 6) of MoS2 are added as reinforcement. Wire Electrical Discharge Machining (WEDM) process is performed to analyze the kerf width and surface roughness. Due to the complex nature of the WEDM process, the necessity for its optimization through the use of innovative methods is well-proven in the process of research. Evolutionary algorithms, specifically genetic algorithm based on NSGA-II and Multiple Objective Particle Swarm Optimization (MOPSO), are used for optimizing kerf width and surface roughness. For assessing the impact of current, pulse on time, and gap voltage on kerf width and surface roughness, an analysis of the selected WEDM process parameters is performed. MOPSO takes lesser iterations as compared to NSGA-II in giving nearly the same optimal fronts for achieving low kerf width and surface roughness. The 10–12 A of current, 50–57 µs of pulse on time, and 30–33 V of gap voltage are used for the WEDM process based on the Pareto-optimal solutions and better performance is achieved on the samples. In addition, the supplementary DOE method is applied to determine the relationship between factors affecting a process and the response. The analysis revealed that current has played a major part in the governance of kerf width and surface roughness over pulse on time and gap voltage for Al-Mg-MoS2 composite.
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Dey, Suman Kumar, Deba Prasad Dash, and Mousumi Basu. "Multi-Objective Economic Environmental Dispatch of Variable Hydro-Wind-Thermal Power System." International Journal of Applied Metaheuristic Computing 12, no. 2 (2021): 16–35. http://dx.doi.org/10.4018/ijamc.2021040102.
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This article presents a multi-objective economic environmental/emission dispatch (EED) of variable head hydro-wind-thermal power system. The combination of NOx emission, SO2 emission, and fuel cost are minimized for non-smooth hydrothermal plants while satisfying various operational constraints like non-smooth fuel cost, penalty coefficient, and wind power uncertainty. The objectives—cost, NOx emission, and SO2 emission—are optimized at the same time. In this research, the non-dominated sorting genetic algorithm-II (NSGA-II) has been employed for solving the given problem where the total cost, NOx emission level, and SO2 emission level are optimized at the same time while satisfying all the operational constraints. The simulation results that are obtained by applying the two test systems on the proposed scheme have been evaluated against strength pareto evolutionary algorithm 2 (SPEA 2).
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Li, Xingjiang, Hanyun Yin, and Fuhai Yan. "Routing optimization of the emergency supplies distribution vehicles using NSGA-II algorithm: a case study." MATEC Web of Conferences 325 (2020): 03002. http://dx.doi.org/10.1051/matecconf/202032503002.
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In recent years, emergencies, including natural disasters and other public disasters, have seriously threatened the lives and property security of people all over the world. In order to save more people’s lives and reduce the losses caused by disasters, many researchers have carried out intensive study on the distribution of emergency supplies. This paper first studies Location-Routing Problem(LRP) of alternative logistics centers and material demand points, and constructs a multi-objective integer programming model based on the actual situation. The model consists of two objectives: (1) the minimum total transportation time; (2) the maximum total emergency material satisfaction. Then an algorithm is introduced to solve the above model: NSGA-II. Finally, the emergency materials distribution in Hubei Province is taken as an example to verify the applicability and effectiveness of the above method and the models.
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Guardado, J. L., F. Rivas-Davalos, J. Torres, S. Maximov, and E. Melgoza. "An Encoding Technique for Multiobjective Evolutionary Algorithms Applied to Power Distribution System Reconfiguration." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/506769.
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Network reconfiguration is an alternative to reduce power losses and optimize the operation of power distribution systems. In this paper, an encoding scheme for evolutionary algorithms is proposed in order to search efficiently for the Pareto-optimal solutions during the reconfiguration of power distribution systems considering multiobjective optimization. The encoding scheme is based on the edge window decoder (EWD) technique, which was embedded in the Strength Pareto Evolutionary Algorithm 2 (SPEA2) and the Nondominated Sorting Genetic Algorithm II (NSGA-II). The effectiveness of the encoding scheme was proved by solving a test problem for which the true Pareto-optimal solutions are known in advance. In order to prove the practicability of the encoding scheme, a real distribution system was used to find the near Pareto-optimal solutions for different objective functions to optimize.
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Manoharan, Neelamegam, Subhransu Sekhar Dash, Kurup Sathy Rajesh, and Sidhartha Panda. "Automatic Generation Control by Hybrid Invasive Weed Optimization and Pattern Search Tuned 2-DOF PID Controller." International Journal of Computers Communications & Control 12, no. 4 (2017): 533. http://dx.doi.org/10.15837/ijccc.2017.4.2751.
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A hybrid invasive weed optimization and pattern search (hIWO-PS) technique is proposed in this paper to design 2 degree of freedom proportionalintegral- derivative (2-DOF-PID) controllers for automatic generation control (AGC) of interconnected power systems. Firstly, the proposed approach is tested in an interconnected two-area thermal power system and the advantage of the proposed approach has been established by comparing the results with recently published methods like conventional Ziegler Nichols (ZN), differential evolution (DE), bacteria foraging optimization algorithm (BFOA), genetic algorithm (GA), particle swarm optimization (PSO), hybrid BFOA-PSO, hybrid PSO-PS and non-dominated shorting GA-II (NSGA-II) based controllers for the identical interconnected power system. Further, sensitivity investigation is executed to demonstrate the robustness of the proposed approach by changing the parameters of the system, operating loading conditions, locations as well as size of the disturbance. Additionally, the methodology is applied to a three area hydro thermal interconnected system with appropriate generation rate constraints (GRC). The superiority of the presented methodology is demonstrated by presenting comparative results of adaptive neuro fuzzy inference system (ANFIS), hybrid hBFOA-PSO as well as hybrid hPSO-PS based controllers for the identical system.
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Pérez-Peló, Sergio, Jesús Sánchez-Oro, Ana Dolores López-Sánchez, and Abraham Duarte. "A Multi-Objective Parallel Iterated Greedy for Solving the p-Center and p-Dispersion Problem." Electronics 8, no. 12 (2019): 1440. http://dx.doi.org/10.3390/electronics8121440.
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This paper generalizes the iterated greedy algorithm to solve a multi-objective facility location problem known as the Bi-objective p-Center and p-Dispersion problem ( B p C D ). The new algorithm is coined as Multi-objective Parallel Iterated Greedy (MoPIG) and optimizes more than one objective at the same time. The B p C D seeks to locate p facilities to service or cover a set of n demand points, and the goal is to minimize the maximum distance between facilities and demand points and, at the same time, maximize the minimum distance between all pairs of selected facilities. Computational results demonstrate the effectiveness of the proposed algorithm over the evolutionary algorithms NSGA-II, MOEA/D, and the Strength Pareto Evolutionary Algorithm 2 (SPEA2), comparing them with the optimal solution found by the ϵ -constraint method.
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Mashwani, Wali Khan. "Comprehensive Survey of the Hybrid Evolutionary Algorithms." International Journal of Applied Evolutionary Computation 4, no. 2 (2013): 1–19. http://dx.doi.org/10.4018/jaec.2013040101.
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Multiobjective evolutionary algorithm based on decomposition (MOEA/D) and an improved non-dominating sorting multiobjective genetic algorithm (NSGA-II) is two well known multiobjective evolutionary algorithms (MOEAs) in the field of evolutionary computation. This paper mainly reviews their hybrid versions and some other algorithms which are developed for solving multiobjective optimization problems (MOPs. The mathematical formulation of a MOP and some basic definitions for tackling MOPs, including Pareto optimality, Pareto optimal set (PS), Pareto front (PF) are provided in Section 1. Section 2 presents a brief introduction to hybrid MOEAs. The authors present literature review in subsections. Subsection 2.1 provides memetic multiobjective evolutionary algorithms. Subsection 2.2 presents the hybrid versions of well-known Pareto dominance based MOEAs. Subsection 2.4 summarizes some enhanced Versions of MOEA/D paradigm. Subsection 2.5 reviews some multimethod search approaches dealing optimization problems.
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Meziane, Mohammed El Amine, and Noria Taghezout. "Predictive Reactive Approach for Energy-Aware Scheduling and Control of Flexible Manufacturing Processes." International Journal of Information Systems and Supply Chain Management 11, no. 4 (2018): 43–62. http://dx.doi.org/10.4018/ijisscm.2018100103.
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Manufacturing processes are responsible for a considerable amount of global energy consumption and world CO2 emissions. Reducing energy consumption during manufacturing is considered one of the most important strategies in contributing to the green supply chain. In this context, the authors propose a new predictive-reactive approach to control energy consumption during manufacturing processes. In addition to forecasting the energy needs, the proposed approach controls the uncertainty of energy volatility and limits energy waste during manufacturing processes. With the integration of this economic-environmental manufacturing efficiency in supply chains, and controlling uncertainty, this approach positively contributes to green and agile supply chains. A multi-objective genetic algorithm (NSGA-2) is proposed as a predictive method, and a new reactive method is developed to dynamically control the energy consumption throughout the peak energy consumption in real time. The approach was tested on the AIP-PRIMECA benchmark, which reflects a real production cell.
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Tran Thien, Huan, Cao Van Kien, and Ho Pham Huy Anh. "Optimized stable gait planning of biped robot using multi-objective evolutionary JAYA algorithm." International Journal of Advanced Robotic Systems 17, no. 6 (2020): 172988142097634. http://dx.doi.org/10.1177/1729881420976344.
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This article proposes a new stable biped walking pattern generator with preset step-length value, optimized by multi-objective JAYA algorithm. The biped robot is modeled as a kinetic chain of 11 links connected by 10 joints. The inverse kinematics of the biped is applied to derive the specified biped hip and feet positions. The two objectives related to the biped walking stability and the biped to follow the preset step-length magnitude have been fully investigated and Pareto optimal front of solutions has been acquired. To demonstrate the effectiveness and superiority of proposed multi-objective JAYA, the results are compared to those of MO-PSO and MO-NSGA-2 optimization approaches. The simulation and experiment results investigated over the real small-scaled biped HUBOT-4 assert that the multi-objective JAYA technique ensures an outperforming effective and stable gait planning and walking for biped with accurate preset step-length value.
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Bai, Tao, Xia Liu, Yan-ping HA, et al. "Study on the Single-Multi-Objective Optimal Dispatch in the Middle and Lower Reaches of Yellow River for River Ecological Health." Water 12, no. 3 (2020): 915. http://dx.doi.org/10.3390/w12030915.
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Given the increasingly worsening ecology issues in the lower Yellow River, the Xiaolangdi reservoir is chosen as the regulation and control target, and the single and multi-objective operation by ecology and power generation in the lower Yellow River is studied in this paper. This paper first proposes the following three indicators: the ecological elasticity coefficient (f1), the power generation elasticity coefficient (f2), and the ecological power generation profit and loss ratio (k). This paper then conducts a multi-target single dispatching study on ecology and power generation in the lower Yellow River. A genetic algorithm (GA) and an improved non-dominated genetic algorithm (NSGA-II) combining constraint processing and feasible space search techniques were used to solve the single-objective model with the largest power generation and the multi-objective optimal scheduling model considering both ecology and power generation. The calculation results show that: (1) the effectiveness of the NSGA-Ⅱcombined with constraint processing and feasible spatial search technology in reservoir dispatching is verified by an example; (2) compared with the operation model of maximizing power generation, the power generation of the target model was reduced by 0.87%, the ecological guarantee rate was increased by 18.75%, and the degree of the impact of ecological targets on the operating results was quantified; (3) in each typical year, the solution spatial distribution and dimensions of the single-target and multi-target models of change are represented by the Pareto-front curve, and a multi-objective operation plan is generated for decision makers to choose; (4) the f1, f2, and k indicators are selected to analyze the sensitivity of the five multi-objective plans and to quantify the interaction between ecological targets and power generation targets. Ultimately, this paper discusses the conversion relationship and finally recommends the best equilibrium solution in the multi-objective global equilibrium solution set. The results provide a decision-making basis for the multi-objective dispatching of the Xiaolangdi reservoir and have important practical significance for further improving the ecological health of the lower Yellow River.
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Morais, Hugo, Tiago Sousa, Rui Castro, and Zita Vale. "Multi-Objective Electric Vehicles Scheduling Using Elitist Non-Dominated Sorting Genetic Algorithm." Applied Sciences 10, no. 22 (2020): 7978. http://dx.doi.org/10.3390/app10227978.
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The introduction of electric vehicles (EVs) will have an important impact on global power systems, in particular on distribution networks. Several approaches can be used to schedule the charge and discharge of EVs in coordination with the other distributed energy resources connected on the network operated by the distribution system operator (DSO). The aggregators, as virtual power plants (VPPs), can help the system operator in the management of these distributed resources taking into account the network characteristics. In the present work, an innovative hybrid methodology using deterministic and the elitist nondominated sorting genetic algorithm (NSGA-II) for the EV scheduling problem is proposed. The main goal is to test this method with two conflicting functions (cost and greenhouse gas (GHG) emissions minimization) and performing a comparison with a deterministic approach. The proposed method shows clear advantages in relation to the deterministic method, namely concerning the execution time (takes only 2% of the time) without impacting substantially the obtained results in both objectives (less than 5%).
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Lai, Yung-Liang, and Jehn-Ruey Jiang. "Pricing Resources in LTE Networks through Multiobjective Optimization." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/394082.
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The LTE technology offers versatile mobile services that use different numbers of resources. This enables operators to provide subscribers or users with differential quality of service (QoS) to boost their satisfaction. On one hand, LTE operators need to price the resources high for maximizing their profits. On the other hand, pricing also needs to consider user satisfaction with allocated resources and prices to avoid “user churn,” which means subscribers will unsubscribe services due to dissatisfaction with allocated resources or prices. In this paper, we study the pricing resources with profits and satisfaction optimization (PRPSO) problem in the LTE networks, considering the operator profit and subscribers' satisfaction at the same time. The problem is modelled as nonlinear multiobjective optimization with two optimal objectives: (1) maximizing operator profit and (2) maximizing user satisfaction. We propose to solve the problem based on the framework of the NSGA-II. Simulations are conducted for evaluating the proposed solution.
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Vijayakumar, K. "Multiobjective Optimization Methods for Congestion Management in Deregulated Power Systems." Journal of Electrical and Computer Engineering 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/962402.
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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.
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Swathi, V., K. Srinivasa Raju, Murari R. R. Varma, and S. Sai Veena. "Automatic calibration of SWMM using NSGA-III and the effects of delineation scale on an urban catchment." Journal of Hydroinformatics 21, no. 5 (2019): 781–97. http://dx.doi.org/10.2166/hydro.2019.033.
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Abstract The study aims at calibration of the storm water management model (SWMM) with non-dominated sorting genetic algorithm-III (NSGA-III) for urban catchment in Hyderabad, India. The SWMM parameters calibrated were Manning's roughness coefficient (N), depression storage for pervious and impervious areas (DP and Di), sub-catchment width (W), curve number (CN), drying time (dry) of soil and percentage of imperviousness (I). The efficacy of calibration was evaluated by comparing the observed and simulated peak flows and runoff using goodness-of-fit indices. The calibration takes into consideration eight event rainfalls resulting in eight calibrated sets. Weights of goodness-of-fit indices were estimated and the best calibrated set was further validated for five continuous rainfalls/runoffs. Simulated runoff volume and peak runoff over the five continuous rainfalls deviated by 7–22% and 2–20% with respect to observed data. Results indicated that parameters calibrated for an event rainfall could be used for continuous rainfall-runoff modelling. The effect of catchment delineation scale on runoff was also studied. The study indicated that output of the model was sensitive to variation in parameter values of infiltration and imperviousness.
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Guney, Kerim, and Ali Durmus. "Pattern Nulling of Linear Antenna Arrays Using Backtracking Search Optimization Algorithm." International Journal of Antennas and Propagation 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/713080.
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An evolutionary method based on backtracking search optimization algorithm (BSA) is proposed for linear antenna array pattern synthesis with prescribed nulls at interference directions. Pattern nulling is obtained by controlling only the amplitude, position, and phase of the antenna array elements. BSA is an innovative metaheuristic technique based on an iterative process. Various numerical examples of linear array patterns with the prescribed single, multiple, and wide nulls are given to illustrate the performance and flexibility of BSA. The results obtained by BSA are compared with the results of the following seventeen algorithms: particle swarm optimization (PSO), genetic algorithm (GA), modified touring ant colony algorithm (MTACO), quadratic programming method (QPM), bacterial foraging algorithm (BFA), bees algorithm (BA), clonal selection algorithm (CLONALG), plant growth simulation algorithm (PGSA), tabu search algorithm (TSA), memetic algorithm (MA), nondominated sorting GA-2 (NSGA-2), multiobjective differential evolution (MODE), decomposition with differential evolution (MOEA/D-DE), comprehensive learning PSO (CLPSO), harmony search algorithm (HSA), seeker optimization algorithm (SOA), and mean variance mapping optimization (MVMO). The simulation results show that the linear antenna array synthesis using BSA provides low side-lobe levels and deep null levels.
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Wunderlich, Mark, Pietro Presicce, Fu-Sheng Chou, Claire Chougnet, Julio Aliberti, and James C. Mulloy. "Enhanced Myeloid and T Cell Development and Improved Functionality of Cord Blood Xenografts In the NSGS Mouse." Blood 116, no. 21 (2010): 3729. http://dx.doi.org/10.1182/blood.v116.21.3729.3729.
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Abstract Abstract 3729 The advent of NOD/SCID mouse strains lacking interleukin-2 receptor gamma (IL2RG) function (NSG and NOG mice) has greatly improved efforts to develop xenograft models of human hematopoiesis. Notably, these mice exhibit markedly reduced adaptive and innate immunity and are well suited for stable engraftment of human CD34+ cells. Importantly, these mice also allow for the development of human T cells, setting them apart from other immunodeficient strains. As a result, NSG and NOG mice are increasingly used for the building of model systems to study HIV infection, graft versus host disease, and immunity. While significant improvements have been realized, IL2RG knockouts lack robust myeloid components, and lymphoid function is likely to be suboptimal due to problems with B cell differentiation defects and delayed appearance of T cells. Recently, we have generated an NSG mouse strain with transgenic expression of several human myelo-supportive cytokines (SCF, GM-CSF, and IL-3), the NSGS mouse. In our initial characterization and study of this novel strain, we found a significant improvement in engraftment of AML cell lines and patient samples relative to NSG mice. In the current study we have extended these findings to include xenografts obtained using umbilical cord blood CD34+ cells (UCB). We have found the NSGS mouse to be equal to the NSG as a host for long-term stable engraftment of these cells when saturating cells doses are used, and superior when limiting numbers of CD34+ cells are injected. While CD34+ levels are much lower in established grafts in primary NSGS recipients compared to NSG mice, possibly as a result of continual mobilization of these cells by the cytokines, human cells were readily detected in secondary NSGS recipients, indicating maintenance of a primitive stem/progenitor cell compartment in vivo. Bone marrow of NSGS mice was predominantly composed of human myeloid cells, while the NSG mice show primarily CD19+ B cells. In contrast, the lineage of the human cells in the peripheral blood were very similar in these two strains, with both showing a gradual switch from myeloid to B cell dominance between weeks 3 and 7 post engraftment. Surprisingly, human T cells were found in the PB of transplanted adult NSGS mice as early as 8 weeks post engraftment, a full 8 weeks before T cells were detectable in NSG mice engrafted with the same UCB sample in parallel. These CD3+ cells presumably develop from the CD34+ stem cells and not from contaminating CD3+ T cells, since FACS-sorted CD3−CD34+ UCB samples produced the same result. Furthermore, normal donor human T cells did not exhibit any advantage in engraftment, cycling, or expansion in NSGS mice when compared to NSG mice in models of GVHD. Characterization of the T cells generated from human CD34+ xenografts revealed CD4+ and CD8+ subpopulations with phenotypes resembling activated, naïve, and memory T cell subsets. CD3+ spleen cells cultured ex vivo were responsive to activation by PHA/IL-2 stimulation and were susceptible to HIV-1 infection. Finally, humanized NSGS mice immunized with a toxoplasmosis extract were able to mount a response to virulent toxoplasmosis infection sufficient to significantly prolong survival while humanized NSG mice or non-humanized NSGS did not. This difference could not be attributed simply to T cell levels, because several of the NSG mice had comparable CD3+ populations at the time of exposure to antigen and subsequent challenge. While T cells are likely to be required for a response to toxoplasmosis challenge, the increased myeloid and dendritic cell populations generated in the NSGS mouse may prove to be equally critical for the functionality of UCB CD34+ xenografts. Disclosures: No relevant conflicts of interest to declare.
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Preis, Ami, and Avi Ostfeld. "Multiobjective contaminant response modeling for water distribution systems security." Journal of Hydroinformatics 10, no. 4 (2008): 267–74. http://dx.doi.org/10.2166/hydro.2008.061.
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Following the events of 9/11/2001 in the US, the world public awareness to possible terrorist attacks on water supply systems has increased significantly. The security of drinking water distribution systems has become a foremost concern around the globe. Water distribution systems are spatially diverse and thus are inherently vulnerable to intentional contamination intrusions. In this study, a multiobjective optimization evolutionary model for enhancing the response against deliberate contamination intrusions into water distribution systems is developed and demonstrated. Two conflicting objectives are explored: (1) minimization of the contaminant mass consumed following detection, versus (2) minimization of the number of operational activities required to contain and flush the contaminant out of the system (i.e. number of valves closure and hydrants opening). Such a model is aimed at directing quantitative response actions in opposition to the conservative approach of entire shutdown of the system until flushing and cleaning is completed. The developed model employs the multiobjective Non-Dominated Sorted Genetic Algorithm–II (NSGA-II) scheme, and is demonstrated using two example applications.
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Pandiarajan, K., and C. K. Babulal. "Fuzzy ranking based non-dominated sorting genetic algorithm-II for network overload alleviation." Archives of Electrical Engineering 63, no. 3 (2014): 367–84. http://dx.doi.org/10.2478/aee-2014-0027.
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Abstract This paper presents an effective method of network overload management in power systems. The three competing objectives 1) generation cost 2) transmission line overload and 3) real power loss are optimized to provide pareto-optimal solutions. A fuzzy ranking based non-dominated sorting genetic algorithm-II (NSGA-II) is used to solve this complex nonlinear optimization problem. The minimization of competing objectives is done by generation rescheduling. Fuzzy ranking method is employed to extract the best compromise solution out of the available non-dominated solutions depending upon its highest rank. N-1 contingency analysis is carried out to identify the most severe lines and those lines are selected for outage. The effectiveness of the proposed approach is demonstrated for different contingency cases in IEEE 30 and IEEE 118 bus systems with smooth cost functions and their results are compared with other single objective evolutionary algorithms like Particle swarm optimization (PSO) and Differential evolution (DE). Simulation results show the effectiveness of the proposed approach to generate well distributed pareto-optimal non-dominated solutions of multi-objective problem
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Scaria, George, Laura Bendzick, Leonard Shultz, and Dan S. Kaufman. "Development Of a Preclinical Human Xenograft Model For Myelodysplastic Syndrome In Immunodeficient Mice Expressing Human Growth Factors." Blood 122, no. 21 (2013): 1534. http://dx.doi.org/10.1182/blood.v122.21.1534.1534.
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Abstract Myelodysplastic syndromes (MDS) are a diverse class of hematopoietic disorders, defined by progressive bone marrow failure, myeloid dysplasia, and an increased risk of acute myeloid leukemia (AML). The development of treatments for MDS has been hampered by the lack of an adequate in vivo model of disease. Mouse xenograft models for MDS have been challenging, as samples from patients with MDS have exhibited poor engraftment when transplanted into immunodeficient mice, with little evidence of persistent disease in recipient mice. Here, we tested a novel xenograft mouse model of MDS using an immunodeficient mouse strain that constitutively expresses human hematopoietic growth factors, SCF, GM-SCF and IL-3 (SGM3). These mice demonstrate improved engraftment of human hematopoietic cells from MDS patient samples. We have used bone marrow from patients diagnosed with MDS with blast counts ranging from 2-15% and differing cytogenetic profiles. Unfractionated bone marrow cells we injected into both NOD/LtSz-scid IL2RG-/- (NSG) and NOD/LtSz-scid IL2RG-/- SGM3 (NSGS) mice. These studies demonstrate significantly improved engraftment in the NSGS mice compared to NSG mice. Specifically, 1 out of 4 MDS patient samples demonstrated engraftment in NSG mice, but 3 out of 4 MDS samples demonstrated engraftment in NSGS mice. In NSG mice, we noted approximately 5-6% of human CD45+ cells in peripheral blood at 6 weeks after injection in one MDS sample, but subsequent to that, there was a drop to less than 1 percent of human CD45+ cells detected at 9 and 12 weeks post injection. The NSGS mice demonstrated more robust engraftment up to 15% human CD45+ cells in the peripheral blood at the 6 week time point in some samples, but subsequently fall to 1-2 % at 12 weeks post injection. This demonstrates that the NSGS strain of mice demonstrates improved xenograft efficiency of MDS bone marrow samples. Surprisingly, higher levels of blasts in the patient samples did not necessarily correspond to increased human CD45+ cell engraftment in the NSGS mice. This suggests the NSGS mice do model the MDS patient characteristics rather than overt leukemia. These results support our hypothesis that constitutive expression of human growth factors can promote engraftment of hematopoietic cells from patients with MDS. These studies will allow us to now use of this NSGS mouse model to test novel drug and/or immunotherapy approaches against human MDS in an in vivo mouse model system. Disclosures: No relevant conflicts of interest to declare.
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Hemmat Esfe, Mohammad, Peyman Razi, Mohammad Hadi Hajmohammad, et al. "Optimization, modeling and accurate prediction of thermal conductivity and dynamic viscosity of stabilized ethylene glycol and water mixture Al 2 O 3 nanofluids by NSGA-II using ANN." International Communications in Heat and Mass Transfer 82 (March 2017): 154–60. http://dx.doi.org/10.1016/j.icheatmasstransfer.2016.08.015.
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