52,223 results
Search Results
2. Multi-objective optimization design of a circular core paper sandwich panel.
- Author
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Jiang, Xiawang, Zhang, Shihao, Yu, Minggong, and Sun, Delin
- Subjects
- *
SANDWICH construction (Materials) , *FURNITURE manufacturing , *PARETO optimum , *COMPRESSIVE strength , *GENETIC algorithms - Abstract
Ensuring sufficient mechanical performance while enabling lightweight design is critical for utilizing paper sandwich panels in the furniture industry. To design lightweight sandwich panels that balance mechanical properties and cost, this study developed a circular core paper sandwich panel (CCPSP) and investigated its structural efficiency using multi-objective optimization. The response surface method (RSM) based on Box–Behnken design was utilized to establish mathematical models relating the paper tube spacing, inner diameter, and height to the out-of-plane compressive strength, density, and cost. The resulting models effectively revealed the coupled effects of the parameters on the responses. Subsequently, the models were optimized using the non-dominated sorting genetic algorithm II (NSGA-II) to find the Pareto optimal trade-offs between maximizing compressive strength while minimizing its density and cost. The optimization solution resulted in an optimal set of paper tube geometries that maximized the structural efficiency of CCPSP. Overall, lower tube height conferred superior structural efficiency, while tube spacing and diameter were constrained. The results highlight the potential of CCPSP as an efficient and sustainable material for furniture manufacturing, enabled by multi-objective optimization of its structure. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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3. Angle of the Perforation Line to Optimize Partitioning Efficiency on Toilet Papers.
- Author
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Vieira, Joana Costa, Vieira, André Costa, Ribeiro, Marcelo L., Fiadeiro, Paulo T., and Costa, Ana Paula
- Subjects
- *
TOILET paper , *GENETIC algorithms , *ANGLES - Abstract
Currently, tissue product producers try to meet consumers' requirements to retain their loyalty. In perforated products, such as toilet paper, these requirements involve the paper being portioned along the perforation line and not outside of it. Thus, it becomes necessary to enhance the behavior of the perforation line in perforated tissue papers. The current study aimed to verify if the perforation line for 0° (the solution found in commercial perforated products) is the best solution to maximize the perforation efficiency. A finite element (FE) simulation was used to validate the experimental data, where the deviations from the experiments were 5.2% for the case with a 4 mm perforation length and 8.8% for a perforation of 2 mm, and optimize the perforation efficiency using the genetic algorithm while considering two different cases. In the first case, the blank distance and the perforation line angle were varied, with the best configuration being achieved with a blank distance of 0.1 mm and an inclination angle of 0.56°. For the second case, the blank distance was fixed to 1.0 mm and the only variable to be optimized was the inclination angle of the perforation line. It was found that the best angle inclination was 0.67°. In both cases, it was verified that a slight inclination in the perforation line will favor partitioning and therefore the perforation efficiency. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
4. Synergistic effect of cellulo-xylanolytic and laccase enzyme consortia for improved deinking of waste papers.
- Author
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Gupta, Guddu Kumar, Kapoor, Rajeev Kumar, Chhabra, Deepak, Bhardwaj, Nishi Kant, and Shukla, Pratyoosh
- Subjects
- *
ARTIFICIAL neural networks , *WASTE paper , *FUNGAL enzymes , *HYDROPHOBIC compounds , *GENETIC algorithms , *XYLANASES - Abstract
• Enhanced cellulo-xylanolytic consortium production from Hypocrea lixii GGRK4 using MOGA-ANN is reported. • The production of CMCase (9.43 fold) and xylanase (4.40 fold) higher than un-optimized process. • The improved deinking efficiency and brightness is reported for photocopier paper and newspaper. • The physical strength of the waste papers were enhanced whereas double fold property was decreased proving its reusability. • A significant fungal enzyme consortium preparation for improved waste paper deinking achieved. This study reports the cellulo-xylanolytic cocktail production from Hypocrea lixii GGRK4 using multi-objective genetic algorithm-artificial neural network tool, resulting in 8.32 ± 1.07 IU/mL, 51.53 ± 3.78 IU/mL activity of CMCase and xylanase, respectively with more than 85 % residual activity at 60 °C and pH 6.0. Interestingly, metal ions viz. K+ and Ca2+ stimulated the enzyme activity, whereas Fe2+ and Cu2+ reduced the activity. Significant amounts of hydrophobic compounds, chromophores, and phenolics were released after wastepapers deinking. The deinking efficiency of 73.60 ± 2.45 % and 38.60 ± 1.34 % was obtained for photocopier paper and newspaper, respectively, whereas brightness of 89.90 ± 2.10 % ISO and 44.90 ± 1.63 % ISO was reported for both types of waste papers. The physical strength of deinked photocopier paper and newspapers, i.e., tensile index (3.10 and 0.50 %), tearing index (7.10 and 4.83 %), and burst factor (8.61) were enhanced whereas double fold property was decreased proving wastepaper reusability. This consortium showed effective and significant enzymatic deinking efficiency for recycled wastepapers. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
5. COLLABORATIVE PRODUCTION SCHEDULING WITH MULTI-ENTERPRISE IDLE CAPACITY SHARING.
- Author
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Liao, Y. G., Zhang, H., Ren, N., and Wang, T. Y.
- Subjects
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DIGITAL twins , *ELECTRONIC paper , *MANUFACTURING processes , *DIGITAL computer simulation , *GENETIC algorithms - Abstract
This paper proposes a digital twin enhanced approach for optimizing collaborative production scheduling in multi-enterprise manufacturing systems. A multi-objective model is developed incorporating practical constraints such as limited time windows, different production capacities, and transportation considerations. To solve the model, an Improved Non-dominated Sorting Genetic Algorithm (INSGA-II) is designed with specialized operators and strategies. The digital twin simulation is enriched with Multi-objective Decision Making based on Interaction Structures (MDIS) to obtain higher-quality solutions. Experiments demonstrate that the MDIS digital twin approach reduces manufacturing lead times and costs while improving utilization and quality compared to standard methods. This research provides an effective optimization framework to leverage cloud manufacturing resources across organizations. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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6. Modelling and Simulation of Intelligent English Paper Generating Based on SSA-GA.
- Author
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Han, Limin, Gao, Hong, and Zhai, Rongjie
- Subjects
GENETIC algorithms ,SEARCH algorithms ,MATHEMATICAL models ,SIMULATION methods & models - Abstract
To enhance the quality and efficiency of computer-enabled generation of papers for Test for English Majors Band 8 (TEM-8), a paper generation model supported by sparrow search algorithm-genetic algorithm was studied. First, a simplified test paper generation mathematical model was set up after analyzing and studying types and characteristics of TEM-8 tasks. In the model, quantity, type, difficulty, discrimination degree, scores, exposure, and answering time of test questions were taken into consideration. To enhance the optimizing effect of the genetic algorithm for searching test questions, the traditional genetic algorithm was improved by introducing the sparrow search algorithm into the model to achieve a better crossover rate, variance rate, optimization precision, and speed of the genetic algorithm. A new sparrow search-genetic algorithm (SSA-GA) was designed, and the optimizing effect of SSA-GA was verified to be ideal through optimizing six standard test functions. Then, SSA-GA was applied to conduct experimentation with test paper generation, and comparison with traditional genetic algorithms was also made. The values of best and average fitness of SSA-GA were better than those of the traditional genetic algorithm (GA) in the paper generation. Exposure rate and success rate in TEM-8 paper generation of SSA-GA were higher than those of traditional GA in TEM-8 paper generation. Results showed that the studied SSA-GA could implement test paper generation with higher speed and better quality. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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7. Editorial: Special Issue on Selected Papers from the 33rd Annual IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2021).
- Author
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Tsihrintzis, George A., Virvou, Maria, and Hatzilygeroudis, Ioannis
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ARTIFICIAL intelligence ,DEEP learning ,CONFERENCES & conventions ,GENETIC algorithms ,PROGRAMMING languages ,GENE regulatory networks - Published
- 2023
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8. Automatic Question Paper Pattern Generation using GA Approach.
- Author
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PAUL, DIMPLE VALAYIL
- Subjects
CONSTRAINED optimization ,GENETIC algorithms ,GENERATIONS ,QUESTIONING - Abstract
This paper focuses on question paper template generation and its use in dynamic generation of examination question paper. Question paper template generation is a constrained based optimization problem. Choosing an efficient, scientific and rational algorithm to generate a template is the key to dynamic examination question paper generation system. By using Genetic Algorithm (GA) and educational taxonomies, this paper analyses the initial population generation, does chromosome encoding, applies genetic manipulations and experimentally proves that the generated question paper templates are best suited for the dynamic examination paper generation system. This new approach outperforms traditional algorithms that randomly generate examination papers in terms of their topic coverage, learning domains and marks distribution. [ABSTRACT FROM AUTHOR]
- Published
- 2020
9. Performance modeling and optimization for the stock preparation unit of a paper plant using genetic algorithm
- Author
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Khanduja, Rajiv and Tewari, P.C.
- Published
- 2013
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10. Performance modeling and optimization for the stock preparation unit of a paper plant using genetic algorithm
- Author
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Khanduja, Rajiv, Tewari, P.C., and Chauhan, R.S.
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- 2011
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11. Integrating process optimization with energy-efficiency scheduling to save energy for paper mills.
- Author
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Zeng, Zhiqiang, Hong, Mengna, Li, Jigeng, Man, Yi, Liu, Huanbin, Li, Zeeman, and Zhang, Huanhuan
- Subjects
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ENERGY consumption , *PAPER mills , *DRYING , *ENERGY conservation , *GENETIC algorithms - Abstract
With the surging energy price and environmental concerns, measures to improve energy efficiency have attracted increasing concerns of the manufacture sector, especially energy-intensive manufacturing industries such as tissue paper mills. Energy-efficiency scheduling, as a novel energy-efficient method, has attracted the attention of an increasing number of researchers in recent years. Drying process is the most energy-intensive production process in tissue paper mills, which has a great energy-saving potential. This paper aims to reduce the energy costs for the tissue paper mill, consisting of processing energy cost and set-up energy cost, through integrating drying process optimization with energy-efficient scheduling. First, the energy cost model and the scheduling model were built. Then, the energy cost of the drying process of every job in a given scheduling problem was optimized using particle swarm optimization (PSO). Afterwards, the energy cost was further optimized using energy-efficiency scheduling. In addition, a hybrid non-dominated sorting genetic algorithm II (NSGA-II) was utilized to solve the energy-efficiency scheduling problem. Finally, several real scheduling problems from a real tissue paper mill were addressed using the proposed approach to demonstrate its effectiveness in energy saving. The experiment result showed that there is a great energy-saving potential in the drying process, accounting for up to 12.53% of the total energy consumption. Moreover, the maximum energy saving ratio of the proposed approach could reach 9.03%. On the whole, the proposed approach can provide a new energy-saving method for tissue paper mills or other manufacturing industries. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
12. A Review Paper on Different Application of Genetic Algorithm for Mobile Ad-hoc Network (MANET).
- Author
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Jinfa Shi, Habib, Misbah, and Hai Yan
- Subjects
GENETIC algorithms ,ROUTING algorithms ,SEARCH algorithms ,NATURAL selection ,RADIO waves ,AD hoc computer networks - Abstract
A Genetic algorithm is a search algorithm depends on the methodology of natural selection and natural genetics. A Mobile Ad hoc network (MANET) is a type of wireless nodes (Devices) which are free to move anywhere in the network without any constraints. The nodes which are in range can communicate each other through radio waves and those who are not in range use any routing algorithm for communication. In this review paper, we focus on the problems of MANET that has been solved by applying GA for it and highlights the characteristic and challenges of MANET in the literature. More specifically, we present the summary of review papers and basic solutions that use and in the last, we present some future direction. Consequently, we concluded that modification in Fitness function (Evaluation function) according to the problem is the base of Genetic algorithm and variation in algorithm parameters can give solutions in a reasonable time. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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13. Bio-inspired computing tools and applications: position paper
- Author
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Gedeon, Tom
- Published
- 2017
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14. Parameter analysis and optimization of paper feeding devices to minimize jamming and simultaneous feeding of multiple pages.
- Author
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Kim, H and Lee, J
- Subjects
PRINTING equipment ,PAPER ,ARTIFICIAL neural networks ,GENETIC algorithms ,MULTIDISCIPLINARY design optimization ,REGRESSION analysis - Abstract
This article investigates an optimal design for a paper-feeding system that minimizes both jamming and simultaneous feeding of multiple papers, hereafter referred to as the multi-feeding rate. A total of 11 design parameters for the paper transfer device, the paper separation device, and the paper guide path are selected and analysed in this study. A test jig for feeding and transferring papers is manufactured to obtain experimental data for use in parameter analysis and design optimization. Back-propagation neural network-based causality analysis is employed to extract five dominant variables among 11 design parameters, and the results of causality analysis are compared with sensitivity results obtained from the analysis of means in the context of experimental design. Five-variable, second-order polynomial based approximate meta-models for jam rate and multi-feeding rate are then constructed, and numerical optimization is performed using NSGA-II, a non-dominated sorting genetic algorithm. Finally, two numerical Pareto optimal solutions are verified via experimental testing. [ABSTRACT FROM AUTHOR]
- Published
- 2011
- Full Text
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15. System closure in pulp and paper mills: network analysis by genetic algorithm
- Author
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Shafiei, S., Domenech, S., Koteles, R., and Paris, J.
- Subjects
- *
PAPER industry , *PRODUCTION engineering , *GENETIC algorithms , *LINEAR programming - Abstract
An approach based on the characterisation of individual operations by water demands and sources has been utilised to study an operating integrated pulp and paper mill. The search for novel whitewater network configurations as a function of specified process constraints and objective functions was done by means of a genetic algorithm coupled with linear programming. Potential solutions are derived from an initial superstructure representation which embodies all possible source and demand connections by an evolutive process akin to natural selection and adaptation. Various objective functions were investigated. Innovative strategies have been identified and are being considered for implementation. [Copyright &y& Elsevier]
- Published
- 2004
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16. Optimal Feature Selection through Search-Based Optimizer in Cross Project.
- Author
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Faiz, Rizwan bin, Shaheen, Saman, Sharaf, Mohamed, and Rauf, Hafiz Tayyab
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FEATURE selection ,RANDOM forest algorithms ,GENETIC algorithms ,INDEPENDENT variables ,FILTER paper - Abstract
Cross project defect prediction (CPDP) is a key method for estimating defect-prone modules of software products. CPDP is a tempting approach since it provides information about predicted defects for those projects in which data are insufficient. Recent studies specifically include instructions on how to pick training data from large datasets using feature selection (FS) process which contributes the most in the end results. The classifier helps classify the picked-up dataset in specified classes in order to predict the defective and non-defective classes. The aim of our research is to select the optimal set of features from multi-class data through a search-based optimizer for CPDP. We used the explanatory research type and quantitative approach for our experimentation. We have F1 measure as our dependent variable while as independent variables we have KNN filter, ANN filter, random forest ensemble (RFE) model, genetic algorithm (GA), and classifiers as manipulative independent variables. Our experiment follows 1 factor 1 treatment (1F1T) for RQ1 whereas for RQ2, RQ3, and RQ4, there are 1 factor 2 treatments (1F2T) design. We first carried out the explanatory data analysis (EDA) to know the nature of our dataset. Then we pre-processed our data by removing and solving the issues identified. During data preprocessing, we analyze that we have multi-class data; therefore, we first rank features and select multiple feature sets using the info gain algorithm to get maximum variation in features for multi-class dataset. To remove noise, we use ANN-filter and get significant results more than 40% to 60% compared to NN filter with base paper (all, ckloc, IG). Then we applied search-based optimizer i.e., random forest ensemble (RFE) to get the best features set for a software prediction model and we get 30% to 50% significant results compared with genetic instance selection (GIS). Then we used a classifier to predict defects for CPDP. We compare the results of the classifier with base paper classifier using F1-measure and we get almost 35% more than base paper. We validate the experiment using Wilcoxon and Cohen's d test. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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17. Genetic algorithm modeling of European Union firms' competitive advantage
- Author
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Dias, Alexandre Teixeira, Martins, Henrique Cordeiro, Santos, Valdeci Ferreira, Verga Matos, Pedro, and Morais, Greiciele Macedo
- Published
- 2024
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18. Customized shading solutions for complex building façades: the potential of an innovative cement-textile composite material through a performance-based generative design
- Author
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Zani, Andrea, Speroni, Alberto, Mainini, Andrea Giovanni, Zinzi, Michele, Caldas, Luisa, and Poli, Tiziana
- Published
- 2024
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19. Vision‐based facial oil blotting paper counting.
- Author
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Sato, Junya, Yamada, Takayoshi, Ito, Kazuaki, and Akashi, Takuya
- Subjects
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PETROLEUM , *GENETIC algorithms , *IMAGE processing , *ELECTRICAL engineers - Abstract
In the production of facial oil blotting papers, a certain number of papers must be counted before packaging. Currently, the papers are counted by hand, and this is hard work. Also, there are risks of adhesion of dust and wrinkles. In order to solve these problems, we propose a vision‐based approach. After the papers, which are arranged by shifting, are captured, the proposed image processing steps detect the boundaries of the papers. By counting the detected boundaries, the number of papers can be counted. Since the parameters for the proposed image processing are optimized by genetic algorithm, prior setting by a user is not necessary. For the experiments, an image dataset was constructed with six types of facial oil blotting papers. The proposed method achieved higher F‐measure than in other related works. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
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20. Design on Algorithm of Automatic Test Papers Generation for Examination System of Electric Energy Measurement.
- Author
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Yuan-Bin, Chen and Jie, Dai
- Abstract
With various kinds of intelligent metering equipment coming into service, there has been an urgent need for a set of examination system in the electric power industry to check the staff's level of measuring electric energy. In this paper, we design and implement a random test paper generation algorithm for this examination system, and analyze the experiment's data, based on the practical requirement of a training system for the electric energy measurement in a certain electric company. At the same time, in order to get a set of test papers to satisfy the given conditions, this paper discusses how to use optimized genetic algorithm to generate test papers from the question bank. This paper introduce fishnet algorithm to generate test papers automatically for getting a better, more fair and more objective test paper. [ABSTRACT FROM PUBLISHER]
- Published
- 2012
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21. Energy optimization in a pulp and paper mill cogeneration facility
- Author
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Marshman, D.J., Chmelyk, T., Sidhu, M.S., Gopaluni, R.B., and Dumont, G.A.
- Subjects
- *
PAPER mills , *PULP mills , *ENERGY management , *ELECTRIC power production , *PROFIT margins , *HEURISTIC , *DYNAMIC programming , *ENERGY development , *GENETIC algorithms - Abstract
Abstract: Alarmingly low pulp prices in early 2009 left pulp and paper mills across North America desperate for any way to improve thin profit margins. One solution that continues to gain popularity among the industry is improved energy management systems for cogeneration systems, which use steam for two purposes – to provide heat for the pulping process and to generate electricity for sale to regional providers. This paper presents an energy optimization algorithm for use in a pulp and paper mill cogeneration system. The algorithm is applicable to a number of popular mill configurations, power sale contracts, and fuel purchasing scenarios. The method is also extended to address weather-dependent cooling limitations encountered by a mill cogeneration facility, in which case an iterative solution is proposed in order to maintain convexity of the optimization problem. Results are presented in the form of three case studies. [ABSTRACT FROM AUTHOR]
- Published
- 2010
- Full Text
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22. Exploring prestigious citations sourced from top universities across disciplines.
- Author
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Luo, Feiheng, Sun, Aixin, Erdt, Moijsola, Raamkumar, Aravind Sesagiri, and Theng, Yin‐Leng
- Subjects
CITATION analysis ,UNIVERSITY rankings ,ALTMETRICS ,BIBLIOMETRICS ,GENETIC algorithms - Abstract
ABSTRACT There have been many studies on the factors influencing paper citation counts. A number of studies have focused on the citing papers, and corresponding methods were proposed to measure the prestige of citations based on the journal impact factors, the total citation counts and the PageRank algorithm values. However, there are drawbacks to these methods. In this paper, we propose a novel method to identify prestigious citations from the affiliation of the citing paper. Specifically, if the authors of the citing paper are affiliated with a prestigious university, the citing paper could be counted as a prestigious citation. As a pilot, we used the top 200 universities on the QS World University Rankings 2015 to identify the prestigious universities so that the prestigious citations, named as QS citations, were identified accordingly. Experimental results validated that QS citations have more important impact on the cited papers than other citations. Papers with QS citations have better performance across most disciplines not only in total citation counts, but also in altmetrics such as the Altmetric Attension Score and Mendeley reader counts. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
23. Multiple interaction strategies for mobile robots based on improved dynamic window approach in unknown dynamic environments
- Author
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He, Li, Zhang, Shuai, Zhang, Heng, and Yuan, Liang
- Published
- 2024
- Full Text
- View/download PDF
24. An integrated approach based on the decision-theoretic rough set for resilient-sustainable supplier selection and order allocation.
- Author
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Zhao, Pengyun, Ji, Shoufeng, and Xue, Yaoting
- Subjects
ROUGH sets ,SUPPLIERS ,SUPPLY chains ,GENETIC algorithms ,GROUP decision making ,PAPER industry - Abstract
Purpose: The purpose of this paper is to propose an innovative integration method based on decision-theoretic rough set and the extended VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) methods to address the resilient-sustainable supplier selection and order allocation (SS/OA) problem. Design/methodology/approach: Specifically, a two-stage approach is designed in this paper. First, the decision-theoretic rough set is employed to calculate the rough number for coping with the subjective uncertainty of data and assigning the weights for a resilient-sustainable evaluation criterion. On this basis, the supplier resilient-sustainable performance is ranked in combination with the extended VIKOR method. Second, a novel multi-objective optimization model is proposed that applies an improved genetic algorithm to select the resilient-sustainable supplier and allocate the corresponding order quantity under a multi-tier supplier network. Findings: The results reveal that joint consideration of resilience and sustainability is essential in the SS/OA process. The method proposed in this study based on decision-theoretic rough sets and the extended VIKOR method can handle imprecise information flexibly, reduce information loss and obtain acceptable solutions for decision-makers. Numerical cases validate that this integrated approach can combine resilience and sustainability for effective and efficient SS/OA. Practical implications: This paper provides industry managers with a new perspective on SS/OA from a resilience and sustainability perspective as a basis for best practices for industry resilience and sustainability. The proposed method helps to evaluate the resilient-sustainable performance of potential suppliers, which is applicable to solving real-world SS/OA problems and has important practical implications for the resilient-sustainable development of supply chains. Originality/value: The two interrelated priorities of resilience and sustainability have emerged as key strategic challenges in SS/OA issues. This paper is the first study of this issue that uses the proposed integrated approach. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
25. A genetic algorithm-based design model to provide reduced risk areas for housing interiors
- Author
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Garip, Seniye Banu, Güzelci, Orkan Zeynel, Garip, Ervin, and Kocabay, Serkan
- Published
- 2024
- Full Text
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26. Assessing university students' perception of academic quality using machine learning
- Author
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Guillén Perales, Alberto, Liébana-Cabanillas, Francisco, Sánchez-Fernández, Juan, and Herrera, Luis Javier
- Published
- 2024
- Full Text
- View/download PDF
27. Research on Photovoltaic Energy Storage System and Supply-Side Power Dispatch Model in Paper Mill.
- Author
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Ying Gao, Jigeng Li, Mengna Hong, and Yi Man
- Subjects
PHOTOVOLTAIC power systems ,ENERGY storage ,PAPER mills ,GENETIC algorithms ,OPERATING costs - Abstract
With the increased awareness of environmental protection and the establishment of the spot market of electric power, renewable energy plays a more and more important role in energy structure adjustment. But the intermittence and instability of renewable energy often result in energy waste. Micro-grid energy storage can effectively improve the utilization rate, economy and reliability of renewable energy. To ensure the economical operation of the micro-grid system and improve the load curve, a two-stage optimal dispatch model was established based on non-dominated sorting genetic algorithm (NSGA-II) and deep Q-value reinforcement learning algorithm (DQN). In the first stage, to get the appropriate battery capacity, the minimum comprehensive operation cost and the minimum capacity fading of battery in the cycle life were selected as the optimization objectives. The optimal solution was calculated by using the improved non-dominated sorting genetic algorithm (NSGA-II). In the second stage, the DQN Algorithm was used to propose a scheduling strategy. Compared with the GA method, the DQN method was able to reduce the operating cost from 0.5829% to 1.2294%. The results showed that the method was feasible and effective. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
28. Meta-heuristics for sustainable supply chain management: a review.
- Author
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Faramarzi-Oghani, Sohrab, Dolati Neghabadi, Parisa, Talbi, El-Ghazali, and Tavakkoli-Moghaddam, Reza
- Subjects
METAHEURISTIC algorithms ,SUPPLY chain management ,SUSTAINABILITY ,GENETIC algorithms ,SUPPLY chains ,PRECISION farming - Abstract
Due to the complexity and the magnitude of optimisation models that appeared in sustainable supply chain management (SSCM), the use of meta-heuristic algorithms as competent solution approaches is being increased in recent years. Although a massive number of publications exist around SSCM, no extant paper explicitly investigates the role of meta-heuristics in the sustainable (forward) supply chain. To fill this gap, a literature review is provided on meta-heuristic algorithms applied in SSCM by analyzing 160 rigorously selected papers published by the end of 2020. Our statistical analysis ascertains a considerable growth in the number of papers in recent years and reveals the contribution of 50 journals in forming the extant literature. The results also show that in the current literature the use of hybrid meta-heuristics is overtaking pure meta-heuristics, the genetic algorithm (GA) and the non-dominated sorting GA (NSGA-II) are the most-used single- and multi-objective algorithms, the aspects of sustainability are mostly addressed in connection with product distribution and routing of vehicles as pivotal operations in supply chain management, and last but not least, the economic-environmental category of sustainability has been further noticed by the scholars. Finally, a detailed discussion of findings and recommendations for future research are provided. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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29. Research on Algorithms based on Web Self-adaptive Study and Intelligent Test Paper Construction and their Applications.
- Author
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Liu, Ying, Wang, Limin, Huang, Lihua, Han, Xuming, Gu, Zhenshan, and Sang, Juan
- Subjects
WEB services ,ADAPTIVE computing systems ,INTELLIGENT agents ,BERNOULLI numbers ,LAW of large numbers ,GENETIC algorithms ,TEST design - Abstract
Abstract: A novel system based on Bernoulli Theorem of Large Number Law and the genetic algorithms was designed and realized in this paper, which had many advantages such as self-adaptive study for difficulty coefficient of item pool and intelligent test paper construction etc. At present, the system has applied in the examination of paperless computer tests of Jinlin university of finance and economics and some satisfactory results have been also obtained. [Copyright &y& Elsevier]
- Published
- 2012
- Full Text
- View/download PDF
30. Test Paper Generating Method Based on Genetic Algorithm.
- Author
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Yan, Li, Shuhong, Li, and Xiurong, Li
- Subjects
CONTINUING education ,GENETIC algorithms ,PARAMETER estimation ,COMPARATIVE studies ,EDUCATIONAL technology ,COMBINATORIAL optimization - Abstract
Abstract: The continuation education is very important for people who have left school to work to increase their competence and skills. To avoid the disadvantages of the common test paper generating methods, genetic algorithm is used to generate the test paper automatically. The concrete design process of test paper generating based on genetic algorithm is discussed in this paper, and some corresponding parameters setting have been compared and defined. The application results demonstrated that the genetic algorithm was an effective tool in the test paper generating. [Copyright &y& Elsevier]
- Published
- 2012
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- View/download PDF
31. Reasearch on Shared Intelligent Test Paper Generating Algorithm Based on Multi Branches Tree.
- Author
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Zhang, Yajuan, Ye, Maogong, Li, Hongbao, and Fu, Hui
- Subjects
GENETIC algorithms ,ARTIFICIAL intelligence ,MATHEMATICAL optimization ,TREE graphs ,DIFFERENTIAL evolution ,INFORMATION technology - Abstract
Abstract: The study first summarizes the characteristics of various intelligent algorithms such as improved genetic algorithm, differential evolution algorithm and ant colony algorithm adopted in test paper generation, and then proposes the parallel evolution of swarm based on ideas of shared intelligent algorithm and dynamic multi branches tree algorithm, so as to improve searching speed and achieve the effect of short-time optimization. During forming optimal individuals, classified training and repeated recognition by virtue of dynamic multi branches tree can not only avoid premature appearance but also get strong convergence. In addition, when the constraints change, the existing knowledge can be inherited. Facts have shown that this algorithm has certain theoretical significance and reference value to the development of intelligent test paper generation algorithm. [Copyright &y& Elsevier]
- Published
- 2011
- Full Text
- View/download PDF
32. The Application of Optimized Particle Swarm Algorithm in Non-paper Examination.
- Author
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Liang, Zhou, Lixin, Ke, Wu, Kaijun, Jianmin, Gong, and Jian, Hua
- Abstract
To deal with non-paper test composition algorithm impact on exam quality, we proposed the test-sheet composition algorithms. By comparing a variety of existing intelligent algorithms in the application of test-sheet composition, we identify the shortcomings of existing algorithms, such as the "premature" of algorithm due to the poor local search ability and the low convergence rate, etc. PSO algorithm has no crossover, mutation operators. It directly provides the speed, position update formula, and completes the assessment with the help of the fitness function of iterations. The principles and mechanisms of algorithm are simpler. On the basis of standard PSO algorithm, we proposed a Binary Particle Swarm Optimize (BPSO) algorithm based on probability. Bayes formula was used to overcome the human factors impacting on algorithm convergence speed. The algorithm validity has been shown in the simulation experiment with Java. [ABSTRACT FROM PUBLISHER]
- Published
- 2012
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33. Spinning joint scheduling strategy and its optimization method based on data and empirical knowledge.
- Author
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Zhang, Sudao, Xue, Wenliang, Gao, Yongshan, Kong, Weijian, and He, Shanshan
- Subjects
PRODUCTION losses ,ELECTRONIC paper ,SCHEDULING ,GENETIC algorithms ,FACTORIES - Abstract
Spinning end breakage is a major factor limiting the efficiency of the spinning process, and this paper proposes a digital method of spinning joint management. Based on the broken ends data collected by a single spindle monitoring system and guided by the empirical knowledge obtained from a factory investigation, a genetic algorithm-based spinning joint scheduling model is built with the minimum spinning machine idle time as the optimization objective. Three different heuristic rules are introduced in generating the initial population, and their relationship with the distribution of broken ends is discussed; to curb the potential efficiency loss, the broken ends are classified by the data obtained from the single spindle monitoring, and the priority joint task is introduced in the model. The experimental results show that, compared with the traditional S-tour, the model with heuristic rule 2 can reduce the machine idle time by 43% on average, and the priority-based model can reduce it by 42% on average. They both have comparable optimization capabilities, but the priority-based model avoids more serious production loss and is the superior choice. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
34. Fuzzy Programming of Dual Recycling Channels of Sustainable Multi-objective Waste Electrical and Electronic Equipment (WEEE) based on Triple Bottom Line (TBL) Theory.
- Author
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Guo, Jianquan, Tang, Bingzi, Huo, Qingqing, Liang, Chengji, and Gen, Mitsuo
- Subjects
- *
ELECTRONIC waste , *PAPER recycling , *PARTICLE swarm optimization , *PROBLEM solving , *GENETIC algorithms - Abstract
As the increasing trend of global consumption of waste electrical and electronic equipment (WEEE), the recycling of WEEE has been thought highly of. In order to solve the problems of high economic cost, environmental detrimental caused by improper recycling, ignorance of social benefits of recycling, uncertain recycling amount and single recycling channel in recycling process of WEEE, this paper proposes a sustainable multi-objective WEEE double recycling channel fuzzy programming model based on triple bottom line (TBL) theory. First of all, TBL is used to develop sustainable multi-objective recycling models concerning economy, environment and society. Secondly, the model is fuzzed. This process includes two steps: the first step is to take WEEE recycling quantity as a triangular fuzzy parameter, and use fuzzy chance constraint method to transform fuzzy constraint into an equivalent clear condition; the second step is to fuzzify suboptimal objective in the model by defining objective membership degree, and transform multi-objective optimization problem into a single-objective optimization problem based on the maximum satisfaction. Finally, taking a recycling enterprise in Shantou, China, as an example, genetic algorithm (GA) and particle swarm optimization (PSO) are comparatively used to solve a calculation case related to the model and the contribution of the model is verified by comparing economic cost, environmental impact and social benefits of the dual recycling channels. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
35. Quality evaluation of water disclosure of Chinese papermaking enterprises based on accelerated genetic algorithm.
- Author
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He, Chaoran, Shen, Juqin, Xu, Jiawei, Sun, Fuhua, and Wang, Bing
- Subjects
WATER quality ,ENVIRONMENTAL reporting ,GENETIC algorithms ,WATER management ,DISCLOSURE ,PAPERMAKING - Abstract
As the carrier of enterprise water resources management disclosure, water information disclosure is a means of expression of enterprises' environmental responsibility. First, a corporate water information disclosure quality evaluation index system and evaluation method are established, and with the help of the projection tracing method of accelerated genetic algorithm, 27 paper companies in China are selected as a sample and the disclosure quality level is analyzed empirically. Then, the analysis is carried out in terms of three changes in vertical trends, horizontal trends and changes in laws, regulations and policies, and the results show that Chinese paper and paper product enterprises have low quality of water information disclosure, weak disclosure content and low voluntary disclosure. Finally, feasible suggestions are made based on the evaluation of disclosure issues. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
36. A new hybrid multivariate grey model based on genetic algorithms optimization and its application in forecasting oil products demand
- Author
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Sapnken, Flavian Emmanuel
- Published
- 2023
- Full Text
- View/download PDF
37. A technical note on the paper “hGA: Hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems”.
- Author
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Derhami, Shahab and Smith, Alice E.
- Subjects
GENETIC algorithms ,FUZZY systems ,RULE-based programming ,DIMENSIONAL analysis ,PROBLEM solving ,INTEGER programming - Abstract
This paper provides a corrected formulation to the mixed integer programming model proposed by Aydogan et al. (2012) [1] . They proposed a genetic algorithm to learn fuzzy rules for a fuzzy rule-based classification system and developed a Mixed Integer Programming model (MIP) to prune the generated rules by selecting the best set of rules to maximize predictive accuracy. However, their proposed MIP formulation contains errors, which are described in this technical note. We develop corrections and improvements to the original formulation and test it with non-parametric statistical tests on the same data sets used to evaluate the original model. The statistical analysis shows that the results of the correction formulation are significantly different from the original model. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
38. Comparative Corrigendum note on papers “Fuzzy adaptive GA for multi-objective assembly line balancing” continued “Modified GA for different types of assembly line balancing with fuzzy processing times”: differences and...
- Author
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Tarimoradi, Mosahar, Alavidoost, M.H., and Zarandi, M.H. Fazel
- Subjects
FUZZY logic ,GENETIC algorithms ,ASSEMBLY line balancing ,COMPARATIVE studies ,PROBLEM solving - Abstract
In the very few recently published paper by Alavidoost et al. [1] , they proposed a novel fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problem, in continue of their previous presented work by Alavidoost et al. [2] , as a modification on genetic algorithm for assembly line balancing with fuzzy processing times. Despite the fact that both of them are well-written, and completely discussed their contributions, this note looks forward to collate these papers together. Likewise provides the correct order of the figures in [1] matching with their corresponding caption. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
39. 2820. Republished Paper. Multiple damage detection and localization in beam-like and complex structures using co-ordinate modal assurance criterion combined with firefly and genetic algorithms.
- Author
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Khatir, Abdelwahhab, Tehami, Mohamed, Khatir, Samir, and Abdel Wahab, Magd
- Subjects
- *
CONTINUUM damage mechanics , *GENETIC algorithms , *COMBINATORIAL optimization , *LOCALIZATION (Mathematics) , *MATHEMATICAL optimization - Abstract
Damage detection and localization in civil engineering constructions using dynamic analysis has become an important topic in recent years. This paper presents a methodology based on non-destructive detection, localization and quantification of multiple damages in simple and continuous beams, and a more complex structure, namely two-dimensional frame structure. The proposed methodology makes used of Firefly Algorithm and Genetic Algorithm as optimization tools and the Coordinate Modal Assurance Criterion as an objective function. The results show that the proposed combination of Coordinate Modal Assurance Criterion and Firefly Algorithm or Genetic Algorithm can be easily used to identify multiple local structural damages in complex structures. However, the convergence rate becomes slower for the case of multiple damages compared to the case of single damage. The effect of noise on the algorithm is further investigated. It is found that the proposed technique is able to detect the damage location and its severity with high accuracy in the presence of noise, although the convergence rate became slower than in the case when no noise is present. It is also found that the convergence rate of Firefly Algorithm is much faster than that of Genetic Algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
40. A hybrid meta-heuristic scheduler algorithm for optimization of workflow scheduling in cloud heterogeneous computing environment
- Author
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Noorian Talouki, Reza, Hosseini Shirvani, Mirsaeid, and Motameni, Homayun
- Published
- 2022
- Full Text
- View/download PDF
41. Research on Digital Steganography and Image Synthesis Model Based on Improved Wavelet Neural Network.
- Author
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Li, Xujie, Yao, Rujing, and Lee, Jonghan
- Subjects
DIGITAL images ,CRYPTOGRAPHY ,FEEDFORWARD neural networks ,ARTIFICIAL neural networks ,ELECTRONIC paper ,LINEAR network coding ,GENETIC algorithms - Abstract
Network compression coding technology is a research hotspot in the field of digital steganography and image synthesis. How to improve image quality while achieving short compression time is a problem currently faced. Based on the improved wavelet neural network theory, this paper constructs a digital steganography and image synthesis model. The model first tracks the contour of the digit to be recognized, then equalizes and resamples the contour to make it translation-invariant and scaling-invariant, and then uses multi-wavelet neural network clusters to stretch the contour shell to obtain orders of magnitude multi-resolution and its average, and finally, these shell coefficients are fed into a feedforward neural network cluster to identify this handwritten digit, solving the problem of multi-resolution decomposition of contour shells while having a high sampling rate. In the simulation process, the classification model that a single pixel is a text/non-text pixel is trained on the original gray value of the gray pixel and its neighboring pixels, and the classified text pixels are connected to a text area through an adaptive MeanShift method. The experimental results show that it is feasible to use multi-wavelet features for handwritten digit recognition. The model combines the neural network and the genetic algorithm, making full use of the advantages of both, so that the new algorithm has the learning ability and robustness of the neural network. The compression ratio after compression by ordinary wavelet coding, PSNR, MSE, and compression time are 8.4, 25 dB, 210, and 7 s, respectively. The values are 11.7, 24 dB, 207, and 11 s, respectively. At the same time, the peak signal-to-noise ratio is higher and the mean square error is lower, that is, the compression quality is better, and the accuracy of digital steganography and image synthesis is effectively improved. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
42. Determination of moisture diffusion coefficient in transformer paper using thermogravimetric analysis
- Author
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García, Diego F., García, Belén, Burgos, Juan Carlos, and García-Hernando, Néstor
- Subjects
- *
DIFFUSION , *THERMOGRAVIMETRY , *PAPER in electrical insulation , *GENETIC algorithms , *MOISTURE measurement , *TEMPERATURE - Abstract
Abstract: This paper presents the methodology used and the results obtained in the determination of moisture diffusion coefficient of non-impregnated transformer insulating paper. In order to establish the diffusion coefficient, drying curves of paper samples were obtained by means of thermogravimetric experiments. The diffusion coefficient parameters were found by applying an optimization process based on genetic algorithms. The error function between measured and simulated curves was determined, and the parameters achieving the best correspondence between measured and estimated values were obtained. As a result, a new equation for the diffusion coefficient of non-impregnated insulating paper is proposed, depending on average moisture concentration, temperature and insulation thickness. The proposed coefficient was validated through experimental cases finding a good agreement between the experimental drying curves and those obtained by simulation using the diffusion coefficient. The proposed diffusion coefficient can be used for the determination of the time required to dry power transformers in factory. [Copyright &y& Elsevier]
- Published
- 2012
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- View/download PDF
43. A GA-Based Neural Fuzzy System for Modeling a Paper Mill Wastewater Treatment Process.
- Author
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Huang, Mingzhi, Wan, Jinquan, Ma, Yongwen, Zhang, Huiping, Wang, Yan, Wei, Chaohai, Liu, Hongbin, and Yoo, ChangKyoo
- Subjects
- *
GENETIC algorithms , *FUZZY systems , *PAPER mills , *WASTEWATER treatment , *PARAMETER estimation , *COAGULATION , *SUSPENSIONS (Chemistry) - Abstract
A genetic algorithm-based neural fuzzy system (GA-NFS) was presented for studying the coagulation process of wastewater treatment in a paper mill. In order to adapt the system to a variety of operating conditions and acquire a more flexible learning ability, the GA-NFS was employed to model the nonlinear relationships between the effluent concentration of pollutants and the chemical dosages, and a hybrid learning algorithm divided into two stages was proposed for parameters learning. During the first learning stage, a genetic algorithm was used to optimize the structure of GA-NFS and the membership function of each fuzzy term due to its capability of parallel and global search. On the basis of an optimized training stage, the back-propagation algorithm (BP algorithm) was chosen to update the parameters of GA-NFS to improve the system precision. The GA-NFS proves to be very effective in modeling coagulation perform and performs better than adaptive-network-based fuzzy inference system (ANFIS). RMSE, MAPE, and Rbetween the predicted and observed values for GA-NFS were only 0.01099, 2.3337, and 0.9375, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2011
- Full Text
- View/download PDF
44. CROSS-BORDER E-COMMERCE LOGISTICS OPTIMIZATION ALGORITHM FOR COLLABORATION BETWEEN THE INTERNET OF THINGS AND LOGISTICS.
- Author
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SHILING PAN and JUAN CHENG
- Subjects
OPTIMIZATION algorithms ,CROSS-border e-commerce ,INTERNET of things ,GENETIC algorithms ,LOGISTICS ,HOME computer networks - Abstract
This paper proposes the shortest path optimization algorithm for domestic and overseas e-commerce logistics based on a bilateral search method. This paper uses the logistics distribution route optimization algorithm based on the shortest path to set the collaborative parameters. Then, it builds an adaptive optimization model for the grid planning of domestic and overseas e-commerce logistics path. The route is optimized. Then, the PSO and genetic algorithm are integrated to establish the logistics path planning model of domestic and overseas e-commerce. The superiority of the proposed route optimization algorithm in domestic and overseas e-commerce logistics distribution is verified through simulation experiments. This algorithm has high spatial positioning efficiency and high transportation efficiency. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
45. RESEARCH ON INTELLIGENT ALGORITHM TO GENERATING TEST PAPER BASED ON GENETIC ALGORITHM.
- Author
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JIN Hanjun, WANG Xiaorong, ZHANG Yaokun, and WANG Yanlin
- Subjects
GENETIC algorithms ,COMPUTER algorithms ,GAUSSIAN distribution ,EXAMINATIONS ,ELECTRONIC information resource searching - Published
- 2005
46. Artificial intelligence applied to investment in variable income through the MACD (moving average convergence/divergence) indicator
- Author
-
Agudelo Aguirre, Alberto Antonio, Duque Méndez, Néstor Darío, and Rojas Medina, Ricardo Alfredo
- Published
- 2021
- Full Text
- View/download PDF
47. Downlink Scheduling via Genetic Algorithms for Multiuser Single-Carrier and Multicarrier MIMO Systems With Dirty Paper Coding.
- Author
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Elliott, Robert C. and Krzymieñ, Witold A.
- Subjects
- *
MIMO systems , *BIT rate , *WIRELESS communications , *COMPUTER input-output equipment , *FREQUENCY-deviation meters , *BROADBAND communication systems , *QUALITY of service , *GENETIC algorithms , *COMPUTER scheduling , *COMPUTATIONAL complexity , *ORTHOGONAL frequency division multiplexing - Abstract
Multiple-input-multiple-output (MIMO) systems are of interest for meeting the expected demand for higher data rates and lower delays in future wireless packet data systems. In such systems, it is optimal to simultaneously transmit to multiple users compared with a single user in a single-input-single-output system. In addition, multicarrier systems are of interest to combat frequency-selective fading that is experienced over the larger bandwidth that these future broadband systems will use. The use of dirty paper coding further complicates the matter, because the order in which the users are encoded will affect the rates that they can achieve. A well-designed cross-layer scheduling algorithm must take into account the multiple dimensions of this resource-allocation problem and other quality-of-service (QoS) parameters to fully exploit the communications channel. The scheduling problem is often expressed in terms of optimizing some utility function. Unfortunately, the search space for this optimization problem is extremely large, which prohibits an optimal exhaustive search. To this end, we investigate the use of genetic algorithms to reduce the complexity of the scheduling. This paper builds upon prior work that implements scheduling via genetic algorithms in the context of single-carrier systems using zero-forcing beamforming (ZFB). In this paper, we investigate how the genetic algorithm can be adapted to account for the effect of encoding order on the scheduling and how the scheduling can be extended to a multicarrier system. In particular, we investigate the maximum throughput and proportionally fair scheduling criteria. We demonstrate that the performance of the genetic algorithm is near optimal compared with an exhaustive search at a greatly reduced computational complexity. Furthermore, in the case of a multicarrier orthogonal frequency-division multiplexing (OFDM) system, an increase in capacity is shown relative to the single-carrier case. [ABSTRACT FROM AUTHOR]
- Published
- 2009
- Full Text
- View/download PDF
48. Optimizing the scheduling of crew deployments in repetitive construction projects under uncertainty
- Author
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Hassan, Abbas, El-Rayes, Khaled, and Attalla, Mohamed
- Published
- 2021
- Full Text
- View/download PDF
49. A Survey of Algorithms for Paper-reviewer Assignment Problem.
- Author
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Kolasa, Tomasz and Krol, Dariusz
- Subjects
GENETIC algorithms ,HEURISTIC algorithms ,COMBINATORIAL optimization ,ARTIFICIAL intelligence ,SET theory - Abstract
This paper analyzes some details of artificial intelligence algorithms to paper-reviewer assignment problem. In particular, the study on most common algorithms like genetic algorithm (GA), ant colony optimization (ACO) and tabu-search (TS) is made and the performance of these algorithms in paper-reviewer assignment problem is tested. Moreover, two hybrid methods that efficiently combine the above-mentioned algorithms are proposed: the ACO-GA and GA-TS algorithms. To measure the performance of these algorithms, extensive computational experiments are conducted. Evaluation using different data sets shows that the proposed algorithms are effective and achieve good results. [ABSTRACT FROM AUTHOR]
- Published
- 2011
- Full Text
- View/download PDF
50. Use of genetic algorithms with multivariate regression for determination of gelatine in historic papers based on FT-IR and NIR spectral data
- Author
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Cséfalvayová, L., Pelikan, M., Kralj Cigić, I., Kolar, J., and Strlič, M.
- Subjects
- *
GENETIC algorithms , *GELATIN , *NEAR infrared spectroscopy , *FOURIER transform infrared spectroscopy , *RAG paper , *REGRESSION analysis , *QUANTITATIVE research , *LEAST squares - Abstract
Abstract: Quantitative non-destructive analysis of individual constituents of historic rag paper is crucial for its effective preservation. In this work, we examine the potentials of mid- and near-infrared spectroscopy, however, in order to fully utilise the selectivity inherent to spectroscopic multivariate measurements, genetic algorithms were used to select spectral data derived from information-rich FT-IR or UV–vis-NIR measurements to build multivariate calibration models based on partial least squares regression, relating spectra to gelatine content in paper. A selective but laborious chromatographic method for the quantification of hydroxyproline (HYP) has been developed to provide the reference data on gelatine content. We used 9-fluorenylmethyl chloroformate (FMOC) to derivatise HYP, which was subsequently determined using reverse-phase liquid chromatographic separation and fluorimetric detection. In this process, the sample is consumed, which is why the method can only be used as a reference method. The sampling flexibility afforded by small-size field-portable spectroscopic instrumentation combined with chemometric data analysis, represents an attractive addition to existing analytical techniques for cultural heritage materials. [ABSTRACT FROM AUTHOR]
- Published
- 2010
- Full Text
- View/download PDF
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