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Статті в журналах з теми "Hybrid Evolution Algorithms"
Ahandani, Morteza Alinia, and Hosein Alavi-Rad. "Hybridizing Shuffled Frog Leaping and Shuffled Complex Evolution Algorithms Using Local Search Methods." International Journal of Applied Evolutionary Computation 5, no. 1 (January 2014): 30–51. http://dx.doi.org/10.4018/ijaec.2014010103.
Повний текст джерелаKaelo, P., and M. M. Ali. "Differential evolution algorithms using hybrid mutation." Computational Optimization and Applications 37, no. 2 (March 6, 2007): 231–46. http://dx.doi.org/10.1007/s10589-007-9014-3.
Повний текст джерелаKumar, N. Suresh, and Pothina Praveena. "Evolution of hybrid distance based kNN classification." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (June 1, 2021): 510. http://dx.doi.org/10.11591/ijai.v10.i2.pp510-518.
Повний текст джерелаKrishna, R. V. V., and S. Srinivas Kumar. "Hybridizing Differential Evolution with a Genetic Algorithm for Color Image Segmentation." Engineering, Technology & Applied Science Research 6, no. 5 (October 23, 2016): 1182–86. http://dx.doi.org/10.48084/etasr.799.
Повний текст джерелаAbi, Soufiane, and Bachir Benhala. "An optimal design of current conveyors using a hybrid-based metaheuristic algorithm." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (December 1, 2022): 6653. http://dx.doi.org/10.11591/ijece.v12i6.pp6653-6663.
Повний текст джерелаKang, Yan, Zhong Min Wang, Ying Lin, and Xiang Yun Guo. "A Hybrid Differential Evolution Scheduling Algorithm to Heterogeneous Distributed System." Applied Mechanics and Materials 631-632 (September 2014): 271–75. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.271.
Повний текст джерелаAbdel-Basset, Mohamed, Reda Mohamed, Waleed Abd Abd Elkhalik, Marwa Sharawi, and Karam M. Sallam. "Task Scheduling Approach in Cloud Computing Environment Using Hybrid Differential Evolution." Mathematics 10, no. 21 (October 31, 2022): 4049. http://dx.doi.org/10.3390/math10214049.
Повний текст джерелаGhosh, Tarun Kumar, and Sanjoy Das. "A Novel Hybrid Algorithm Based on Firefly Algorithm and Differential Evolution for Job Scheduling in Computational Grid." International Journal of Distributed Systems and Technologies 9, no. 2 (April 2018): 1–15. http://dx.doi.org/10.4018/ijdst.2018040101.
Повний текст джерелаIbrahim, Abdelmonem M., and Mohamed A. Tawhid. "A hybridization of differential evolution and monarch butterfly optimization for solving systems of nonlinear equations." Journal of Computational Design and Engineering 6, no. 3 (October 25, 2018): 354–67. http://dx.doi.org/10.1016/j.jcde.2018.10.006.
Повний текст джерелаBrévilliers, Mathieu, Julien Lepagnot, Lhassane Idoumghar, Maher Rebai, and Julien Kritter. "Hybrid differential evolution algorithms for the optimal camera placement problem." Journal of Systems and Information Technology 20, no. 4 (November 12, 2018): 446–67. http://dx.doi.org/10.1108/jsit-09-2017-0081.
Повний текст джерелаДисертації з теми "Hybrid Evolution Algorithms"
Kafafy, Ahmed. "Hybrid Evolutionary Metaheuristics for Multiobjective Decision Support." Thesis, Lyon 1, 2013. http://www.theses.fr/2013LYO10184/document.
Повний текст джерелаMany real-world decision making problems consist of several conflicting objectives, the solutions of which is called the Pareto-optimal set. Hybrid metaheuristics proved their efficiency in solving these problems. They tend to enhance search capabilities by incorporating different metaheuristics. Thus, we are concerned with developing new hybrid schemes by incorporating different strategies with exploiting the pros and avoiding the drawback of the original ones. First, HEMH is proposed in which the search process includes two phases DMGRASP obtains an initial set of efficient solutions in the 1st phase. Then, greedy randomized path-relinking with local search or reproduction operators explore the non-visited regions. The efficient solutions explored over the search are collected. Second, a comparative study is developed to study the hybridization of different metaheuristics with MOEA/D. The 1st proposal combines adaptive discrete differential Evolution with MOEA/D. The 2nd combines greedy path-relinking with MOEA/D. The 3rd and the 4th proposals combine both of them in MOEA/D. Third, an improved version of HEMH is presented. HEMH2 uses inverse greedy to build its initial population. Then, differential evolution and path-relink improves these solutions by investigating the non-visited regions in the search space. Also, Pareto adaptive epsilon concept controls the archiving process. Motivated by the obtained results, HESSA is proposed to solve continuous problems. It adopts a pool of search strategies, each of which has a specified success ratio. A new offspring is generated using a randomly selected one. Then, the success ratios are adapted according to the success of the generated offspring. The efficient solutions are collected to act as global guides. The proposed algorithms are verified against the state of the art MOEAs using a set of instances from literature. Results indicate that all proposals are competitive and represent viable alternatives
Naldi, Murilo Coelho. "Agrupamento híbrido de dados utilizando algoritmos genéticos." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-07112006-080351/.
Повний текст джерелаClustering techniques have been obtaining good results when used in several data analysis problems, like, for example, gene expression data analysis. However, the same clustering technique used for the same data set can result in different ways of clustering the data, due to the possible initial clustering or the use of different values for the free parameters. Thus, the obtainment of a good clustering can be seen as an optimization process. This process tries to obtain good clustering by selecting the best values for the free parameters. For being global search methods, Genetic Algorithms have been successfully used during the optimization process. The goal of this research project is to investigate the use of clustering techniques together with Genetic Algorithms to improve the quality of the clusters found by clustering algorithms, mainly the k-means. This investigation was carried out using as application the analysis of gene expression data, a Bioinformatics problem. This dissertation presents a bibliographic review of the issues covered in the project, the description of the methodology followed, its development and an analysis of the results obtained.
Ghoman, Satyajit Sudhir. "A Hybrid Optimization Framework with POD-based Order Reduction and Design-Space Evolution Scheme." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/23113.
Повний текст джерелаThe first part of this dissertation describes the development of a conceptual Multi-Fidelity Multi-Strategy and Multi-Disciplinary Design Optimization Environment (M3 DOE), in context of aircraft wing optimization. M3 DOE provides the user a capability to optimize configurations with a choice of (i) the level of fidelity desired, (ii) the use of a single-step or multi-step optimization strategy, and (iii) combination of a series of structural and aerodynamic analyses. The modularity of M3 DOE allows it to be a part of other inclusive optimization frameworks. The M3 DOE is demonstrated within the context of shape and sizing optimization of the wing of a Generic Business Jet aircraft. Two different optimization objectives, viz. dry weight minimization, and cruise range maximization are studied by conducting one low-fidelity and two high-fidelity optimization runs to demonstrate the application scope of M3 DOE.
The second part of this dissertation describes the development of an innovative hybrid optimization framework that extends the robustness of M3 DOE by employing a proper orthogonal decomposition-based design-space order reduction scheme combined with the evolutionary algorithm technique. The POD method of extracting dominant modes from an ensemble of candidate configurations is used for the design-space order reduction. The snapshot of candidate population is updated iteratively using evolutionary algorithm technique of fitness-driven retention. This strategy capitalizes on the advantages of evolutionary algorithm as well as POD-based reduced order modeling, while overcoming the shortcomings inherent with these techniques. When linked with M3 DOE, this strategy offers a computationally efficient methodology for problems with high level of complexity and a challenging design-space. This newly developed framework is demonstrated for its robustness on a non-conventional supersonic tailless air vehicle wing shape optimization problem.
Ph. D.
Caetano, Samuel Sabino. "O uso de algoritmos evolutivos para a formação de grupos na aprendizagem colaborativa no contexto corporativo." Universidade Federal de Goiás, 2013. http://repositorio.bc.ufg.br/tede/handle/tede/3195.
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Increasingly, learning in groups has become present in school environments. This fact is also part of the organizations, when considers learning in the workplace. Conscious of the importance of group learning at the workplace (CSCL@Work) emerges as an application area. In Computer Supported Collaborative Learning(CSCL), researchers have been struggling to maximize the performance of groups by techniques for forming groups. Is that why this study developed three (3) algorithmic approaches to formation of intraheterogeneous and inter-homogeneous groups, as well as a model proposed in this work in which integrates dichotomous functional characteristics and preferred roles. We made an algorithm that generates random groups, a Canonical Genetic Algorithm and Hybrid Genetic Algorithm. We obtained the input data of the algorithm by a survey conducted at the Court of the State of Goiás to identify dichotomous functional characteristics, and after we categorize these characteristics, based on the data found and the model proposed group formation. Starting at real data provided of employees whom participated in a course by Distance Education (EaD), we apply the model and we obtained the input data related to functional features. As regards the favorite roles, we assigned randomly values to the employees aforementioned, from a statistical statement made by Belbin into companies in the United Kingdom. Then, we executed the algorithms in three test cases, one considering the preferred papers and functional characteristics, while the other two separately considering each of these perspectives. Based on the results obtained, we found that the hybrid genetic algorithm outperforms the canonical genetic algorithm and random generator.
A aprendizagem em grupos tem se tornado realidade cada vez mais presente nos ambientes de ensino. Esta realidade também faz parte das organizações quando considera-se a aprendizagem no contexto do trabalho. Cientes da importância da aprendizagem em grupo no ambiente de trabalho, uma nova abordagem, denominada CSCL@Work, surge como uma aplicação da área Aprendizagem Colaborativa Apoiada pelo Computador, no inglês, Computer Supported Collaborative Learning (CSCL), no ambiente de trabalho. Em CSCL, pesquisadores tem se esforçado cada vez mais para maximizar o desempenho dos grupos através de técnicas para formação de grupos. Por isso neste trabalho desenvolvemos 3 (três) abordagens algorítmicas para formação de grupos intra-heterogêneos e inter-homogêneos, a partir de um modelo proposto nesta pesquisa, que integra características funcionais dicotômicas e papéis preferidos. Confeccionamos um algoritmo que gera grupos aleatoriamente, um algoritmo genético canônico e um algoritmo genético híbrido. Para obter os dados de entrada do algoritmo, realizamos uma pesquisa no Tribunal de Justiça do Estado de Goiás para identificar características funcionais dicotômicas, categorizamos estas características, com base nos dados encontrados e no modelo de formação de grupos proposto. A partir de dados reais fornecidos de funcionários que participaram de um curso por Educação a Distância (EaD), aplicamos o modelo e obtivemos os dados de entrada relativos às características funcionais. Quanto aos papéis preferidos, atribuímos os valores aleatoriamente aos funcionários mencionados, partindo de um levantamento estatístico feito por Belbin em empresas no Reino Unido. Em seguida, executamos os algoritmos em três casos de testes, um considerando as características funcionais e papéis preferidos, e os outros dois considerando separadamente cada uma destas perspectivas. A partir dos resultados obtidos, constatamos que o algoritmo genético híbrido obtém resultados superiores ao algoritmo genético canônico e método aleatório.
Inclan, Eric. "The Development of a Hybrid Optimization Algorithm for the Evaluation and Optimization of the Asynchronous Pulse Unit." FIU Digital Commons, 2014. http://digitalcommons.fiu.edu/etd/1582.
Повний текст джерелаBurdelis, Mauricio Alexandre Parente. "Ajuste de taxas de mutação e de cruzamento de algoritmos genéticos utilizando-se inferências nebulosas." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-14082009-180444/.
Повний текст джерелаThis work addressed a proposal of the application of Fuzzy Systems to adjust parameters of Genetic Algorithms, during execution time. This application attempts to improve the performance of Genetic Algorithms by diminishing, at the same time: the average number of necessary generations for a Genetic Algorithm to find the desired global optimum value, as well as diminishing the number of executions of a Genetic Algorithm that are not capable of finding the desired global optimum value even for high numbers of generations. For that purpose, the results of many experiments with Genetic Algorithms were analyzed; addressing instances of the Function Minimization and the Travelling Salesman problems, under different parameter configurations. With the results obtained from these experiments, a model was proposed, for the exchange of parameter values of Genetic Algorithms, in execution time, by using Fuzzy Systems, in order to improve the performance of the system, minimizing both of the measures previously cited.
Hoc, Tran Duc, and 陳德學·. "Hybrid Multiple Objective Differential Evolution Algorithms for Optimizing Resource Trade-offs of Project Scheduling." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/4qebpn.
Повний текст джерела國立臺灣科技大學
營建工程系
103
Construction management everywhere faces problems and challenges. Resource scheduling is a crucial part of project planning of any management companies. Successful tradeoff optimization resource scheduling problems within the project scope is necessary to maximize overall company benefits. This study investigated the potential use of various advanced techniques to improve multiple objective Differential Evolution. Three hybrid multiple objective Differential Evolution (MODE) algorithms that integrate chaotic maps, opposition-based learning technique and Artificial Bee Colony are introduced to solve the resource scheduling problems. Firstly, chaotic initialized adaptive multiple objective Differential Evolution (CAMODE) model is presented. CAMODE utilizes the advantages of chaotic sequences for generating an initial population and an external elitist archive to store non-dominated solutions found during the evolutionary process. In order to maintain the exploration and exploitation capabilities during various phases of optimization process, an adaptive mutation operation is introduced. Secondly, opposition-based Multiple Objective Differential Evolution (OMODE) model is presented. OMODE employs an opposition-based learning technique for population initialization and for generation jumping. Opposition numbers are used to improve the exploration and convergence performance of the optimization process. Finally, a new hybrid multiple-objective artificial bee colony with differential evolution (MOABCDE) model is proposed. The proposed algorithm integrates crossover operations from differential evolution (DE) with the original artificial bee colony (ABC) in order to balance the exploration and exploitation phases of the optimization process. Numerous real construction case studies including time-cost-quality tradeoff, time-cost tradeoffs in resource-constrained, time-cost-labor utilization tradeoff and time-cost-environment impact tradeoff problems are used to demonstrate the proposed models. The proposed models are validated by comparing with current widely used multiple objective algorithms, including the non-dominated sorting genetic algorithm (NSGA-II), the multiple objective particle swarm optimization (MOPSO), the multiple objective differential evolution (MODE), and the multiple objective artificial bee colony (MOABC) and previous works via comparison indicators and hypothesis test. Experimental results obtained from the proposed models confirm that using the newly established models can be a highly beneficial for decision-makers when solving various problems in the field of construction management.
Ishak, Mohd Yusoff. "Predictive modelling of eutrophication and algal bloom formation in tropical lakes." Thesis, 2012. http://hdl.handle.net/2440/78097.
Повний текст джерелаThesis (Ph.D.) -- University of Adelaide, School of Earth and Environmental Sciences, 2012
Lin, Chi-Huai, and 林淇淮. "Security-Constrained Economic Operation of Power Systems Using Hybrid Differential Evolution Algorithm." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/72823003419374894860.
Повний текст джерела國立中正大學
電機工程研究所
92
With the trend of deregulation of electric powe industries, the demand of quality of electricity supply rises.The electric power utility always pursues stable and reliable electricity supply to satisfy the requirements of consumers in a way of economic and secure operation for the power system.This thesis is an attempt to explore the security-constrained economic operation of power systems. The problem of security-constrained economic operation of power systems is an optimization problem, which looks for both the economic power generation and the appropriate reactive power compensation. Real power outputs of generation units are economically dispatched to reach a minimum generation cost, whereas reactive power outputs of generation units and capacitor banks are appropriately dispatched to compensate the reactive power requirement of the system and control the bus voltage as well as line flows. The problem under study is an optimization problem and is to be solved using the hybrid differential evolution (HDE) method. HDE is computationally simple, which provides robust-search capability in a huge solution space. It employs two additional operations than the previous differential evolution (DE), these two operations are the acceleration technique and the migration technique. The acceleration technique can increase the convergence speed and the migration technique can avoid falling into a local solution and achieve the optimal solution. Application of the proposed approach is demonstrated and verified using two application systems including a 9-bus and a 26-bus systems.
You, Ming-Sian, and 尤銘賢. "Cloud based Hybrid Evolution Algorithm for NP-Complete Pattern in Nurse Scheduling Problem." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/vv9bu5.
Повний текст джерела國立虎尾科技大學
資訊工程系碩士班
104
In this thesis, the Cloud based Hybrid evolution algorithm for NP-Complete Pattern in Nurse Scheduling Problem (NSP) is proposed as the Software Computing as a Service (SCaaS). Due to the low birth rate, the human resource becomes the limited resource for job assignment. To find the optimal solution for staff scheduling becomes an important issue. The proposed system follows the definition of NSP and recognizes the possible problem of NP-Complete Pattern. Only the pattern is recognized as the NSP optimal problem, the proposed system can find the optimal solution. Then, the different types of evolutionary algorithm in evolution steps are integrated. Based on the proposed Feedback Assistance method, the suitable evolution steps of the evolutionary algorithm can be dynamically decided and executed. Similar to the Tasktracker and Jobtracker in cloud, all the computing load can be divided and distributed. The simulation results show that the proposed hybrid evolution algorithm can find the optimal solution with about 50% less evolution generations.
Книги з теми "Hybrid Evolution Algorithms"
Bäck, Thomas. Evolutionary Algorithms in Theory and Practice. Oxford University Press, 1996. http://dx.doi.org/10.1093/oso/9780195099713.001.0001.
Повний текст джерелаЧастини книг з теми "Hybrid Evolution Algorithms"
Mo, W., S. U. Guan, and Sadasivan K. Puthusserypady. "A Novel Hybrid Algorithm for Function Optimization: Particle Swarm Assisted Incremental Evolution Strategy." In Hybrid Evolutionary Algorithms, 101–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73297-6_5.
Повний текст джерелаAranguren, Itzel, Arturo Valdivia, and Marco A. Pérez. "Segmentation of Magnetic Resonance Brain Images Through the Self-Adaptive Differential Evolution Algorithm and the Minimum Cross-Entropy Criterion." In Applications of Hybrid Metaheuristic Algorithms for Image Processing, 311–50. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40977-7_14.
Повний текст джерелаNoghanian, Sima, Abas Sabouni, Travis Desell, and Ali Ashtari. "Global Optimization: Differential Evolution, Genetic Algorithms, Particle Swarm, and Hybrid Methods." In Microwave Tomography, 39–61. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0752-6_3.
Повний текст джерелаFefelova, Iryna, Andrey Fefelov, Volodymyr Lytvynenko, Róża Dzierżak, Iryna Lurie, Nataliia Savina, Mariia Voronenko, and Svitlana Vyshemyrska. "Protein Tertiary Structure Prediction with Hybrid Clonal Selection and Differential Evolution Algorithms." In Advances in Intelligent Systems and Computing, 673–88. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26474-1_47.
Повний текст джерелаFu, Wenlong, Mark Johnston, and Mengjie Zhang. "Hybrid Particle Swarm Optimisation Algorithms Based on Differential Evolution and Local Search." In AI 2010: Advances in Artificial Intelligence, 313–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17432-2_32.
Повний текст джерелаZhao, Zhan-Fang, Kun-Qi Liu, Xia Li, You-Hua Zhang, and Shu-Lin Wang. "Research on Hybrid Evolutionary Algorithms with Differential Evolution and GUO Tao Algorithm Based on Orthogonal Design." In Lecture Notes in Computer Science, 78–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14922-1_11.
Повний текст джерелаSaif, Faten A., Rohaya Latip, M. N. Derahman, and Ali A. Alwan. "Hybrid Meta-heuristic Genetic Algorithm: Differential Evolution Algorithms for Scientific Workflow Scheduling in Heterogeneous Cloud Environment." In Proceedings of the Future Technologies Conference (FTC) 2022, Volume 3, 16–43. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18344-7_2.
Повний текст джерелаLi, Xia, Kunqi Liu, Lixiao Ma, and Huanzhe Li. "A Concurrent-Hybrid Evolutionary Algorithms with Multi-child Differential Evolution and Guotao Algorithm Based on Cultural Algorithm Framework." In Advances in Computation and Intelligence, 123–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16493-4_13.
Повний текст джерелаChang, Le, and Jiaben Yang. "MEBRL: Memory-Evolution-Based Reinforcement Learning Algorithm of MAS." In Hybrid Information Systems, 449–58. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1782-9_32.
Повний текст джерелаMollinetti, Marco Antônio Florenzano, Daniel Leal Souza, Rodrigo Lisbôa Pereira, Edson Koiti Kudo Yasojima, and Otávio Noura Teixeira. "ABC+ES: Combining Artificial Bee Colony Algorithm and Evolution Strategies on Engineering Design Problems and Benchmark Functions." In Hybrid Intelligent Systems, 53–66. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27221-4_5.
Повний текст джерелаТези доповідей конференцій з теми "Hybrid Evolution Algorithms"
Kromer, Pavel, Václav Snasel, Jan Platos, and Ajith Abraham. "Optimization of Turbo Codes by Differential Evolution and Genetic Algorithms." In 2009 Ninth International Conference on Hybrid Intelligent Systems. IEEE, 2009. http://dx.doi.org/10.1109/his.2009.289.
Повний текст джерелаT. Basokur, A., and I. Akca. "Hybrid Genetic Algorithms Derived from the Evolution Theories." In 4th Congress of the Balkan Geophysical Society. European Association of Geoscientists & Engineers, 2005. http://dx.doi.org/10.3997/2214-4609-pdb.26.o10-02.
Повний текст джерелаNogueira de Sousa, Gustavo, and Omar Andres Carmona Cortes. "On a Cooperative Hybrid Algorithm Based on Harmony Search and Differential Evolution for Numerical Optimization." In Computer on the Beach. Itajaí: Universidade do Vale do Itajaí, 2020. http://dx.doi.org/10.14210/cotb.v11n1.p214-220.
Повний текст джерелаOlatunji, Obafemi, Stephen Akinlabi, Nkosinathi Madushele, Paul Adedeji, and Samuel Fatoba. "Evolution Algorithms and Biomass Properties Prediction: A Review." In ASME 2019 Power Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/power2019-1826.
Повний текст джерелаLi, Ling-po, and Ling Wang. "Hybrid algorithms based on harmony search and differential evolution for global optimization." In the first ACM/SIGEVO Summit. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1543834.1543871.
Повний текст джерелаChen, Xianqi, Wen Yao, Yong Zhao, Xiaoqian Chen, Jun Zhang, and Yazhong Luo. "The Hybrid Algorithms Based on Differential Evolution for Satellite Layout Optimization Design." In 2018 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2018. http://dx.doi.org/10.1109/cec.2018.8477969.
Повний текст джерелаCheng, Shuo, and Mian Li. "Multi-Objective Robust Optimization Using Differential Evolution and Sequential Quadratic Programming." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12293.
Повний текст джерелаUstun, Deniz, and Ali Akdagli. "A study on the performance of the hybrid optimization method based on artificial bee colony and differential evolution algorithms." In 2017 International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2017. http://dx.doi.org/10.1109/idap.2017.8090346.
Повний текст джерелаAbubakar, Abba A., Abul Fazal M. Arif, Khaled S. Al-Athel, and S. Sohail Akhtar. "Prediction of Residual Stress and Damage in Thermal Spray Coatings Using Hybrid Computational Approach." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-86504.
Повний текст джерелаGalski, Roberto Luiz, Heitor Patire Ju´nior, Fabiano Luis de Sousa, Jose´ Nivaldo Hinckel, Pedro Lacava, and Fernando Manuel Ramos. "GEO + ES Hybrid Optimization Algorithm Applied to the Parametric Thermal Model Estimation of a 200N Hydrazine Thruster." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47584.
Повний текст джерела