リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。

リケラボ 全国の大学リポジトリにある学位論文・教授論文を一括検索するならリケラボ論文検索大学・研究所にある論文を検索できる

リケラボ 全国の大学リポジトリにある学位論文・教授論文を一括検索するならリケラボ論文検索大学・研究所にある論文を検索できる

大学・研究所にある論文を検索できる 「Research on GM-LSTM Hybrid Model for Tourism Prediction Based on One Belt and One Road」の論文概要。リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。
リケラボ

コピーが完了しました

URLをコピーしました

論文の公開元へ論文の公開元へ
書き出し

Research on GM-LSTM Hybrid Model for Tourism Prediction Based on One Belt and One Road

鄭 舒心 富山大学

2020.09.28

概要

The value and significance of the ancient road of silk are fully analyzed, and the cultural connotation and the role of promoting economic development of One Belt and One Road proposed by China are summarized in this thesis. Due to the rich tourism resources along the silk road: high grade, good quality, complete types, strong attraction, with the characteristics of "cultural heritage and natural scenery concurrence, ancient culture and modern civilization coexist". The integration of culture and tourism has strengthened exchanges and mutual learning among different cultures and promoted the integration of cultural and tourism resources around the world. With the support of modern transportation, a unique tourism economic belt has been formed. Therefore, to promote and jointly build the high-quality One Belt and One Road through tourism development, will further break regional and industrial barriers, further promote inter-civilization as well as economic and cultural exchanges and strengthen cooperation in technological innovation, infrastructure and transportation construction, and establish an international division of labor system featuring mutual trust, rationality and win-win results, and make industrial structures complementary and mutually beneficial, and break new ground of regional cooperation and the integration of the economy in Asia and Europe. Therefore, the development of "One Belt And One Road" tourism has great market potential and great economic and cultural value.

Along with the development in the tourism industry circle, the study of tourism and management put forward higher and more precise requirements. Artificial neural network is a pure computing model based on human brain tissue structure, succeeded in solving many practical problems in the modern computer, and it also has a outstanding performance in the study of the tourism problems, provides a good theoretical basis of the management and decision-making of the tourism. Since tourism data are in a small data set with observation data discreteness and nonlinearity, there are big limitations to prediction mode.

The following models are introduced in this thesis. The grey model, utilizing differential equations to characterize the complex system and making short-term prediction; the rolling window scheme for grey model; increasing the forecasting accuracy; LSTM, solving the convergence problems faced by traditional neural networks for the time sequence forecast, a kind of recurrent neural network (RNN); RBFNN model, Two new hybrid nonlinear dynamic prediction methods based on the neural network of radial basis function and iterative nonlinear filter; ARIMA model, well-establishing time series model for tourist forecasting; and GM-LSTM hybrid model, integrating the first-order grey model and LSTM neural network with a rolling mechanism. So two groups of models with good performance in tourism prediction are selected: the models related to neural network and classical traditional models. For convenience of comparison, three models are selected from each group: RBFNN with IEKF and IUKF, LSTM and SDNM for the models related to neural network, and classic traditional models including ARIMA, GM and Rolling GM. Then, the same data set and the same simulation software are used for calculation to predict the arrivals to Xi’an from the countries along the OBOR. After the evaluation results, the best performance models of each group was screened out. Then, it is compared with the GM - LSTM model proposed in this thesis.

On the basis of studying the classical model and the neural network model, this thesis uses the mobile window technology combined with GM to obtain rolling - GM, which is used to predict the development trend of the data. Then, the LSTM model is used to predict the residual, and the predicted values of the above two are added up to obtain the predicted results.

Among the above models, the GM-LSTM hybrid model is proposed in this thesis. We take the prediction of the arrival number of tourists from countries along the route of One Belt and One Road to Xi 'an as the target and use the above models for analysis and calculation. Then the results of comparison of the working models show that the GM-LSTM hybrid model integrates the advantages of the grey model (GM) and the neural network (NN), and can make self-adaptive prediction based on the observation of small samples. The first-order grey model is applied to describe the overall trend of tourism demand, and the nonlinear residual fluctuation of tourism demand is described by the LSTM network with rolling mechanism. Based on the case analysis of the annual arrival number of international tourists in Xi 'an from 1980 to 2018, the hybrid model of GM-LSTM is evaluated in effectiveness and compared with the standard time series model in result. The study result shows that the proposed hybrid model of GM-LSTM is more accurate and more effective for tourism prediction. Therefore, the GM-LSTM model is a more effective model for time series prediction with a small amount of data and high degree of nonlinearity. The model can not only predict tourism problems, but also has a good reference value in the prediction of other similar events.

論文の公開元へ

この論文で使われている画像

関連論文

関連論文をもっと見る

参考文献

[1] The New Trend, New Exploration and New Formats of Cultural and Tourism Integration April 22, 2019 15:52 Source: Economic Daily - China Economic Net

[2] Zhang qian: Pioneer of the Silk Road. CCTV. 2017-05-23.

[3] Bai changhong. Industry Talent Training under the Background of Cultural and Tourism Integration–Practical Demand and Theoretical Topic. People’s Forum · Academic Frontier, June, 2019

[4] Yang jinsong, “One Belt And One Road” Cultural and Tourism Integration Has Broad Prospects, China Tourism News, 2018-4-13

[5] Zhang qian, Pioneer of the Silk Road. CCTV. 2017-05-23

[6] The Concept of China-central Asia New Silk Road Economic Development Belt. Central Asia Research Network. 2013-09-09.

[7] Li qiyuan, Empirical Research on the Impact of Foreign Exchange Income from Tourism on Economic Growth, Research on Financial and Economic Issues, No. 09, 2014.

[8] Zhou caifeng and Ren wenbo, Probe into the Hidden Worries Behind the Rise of Tourism in Countries along the “One Belt And One Road”, [J]. Modern marketing, 2019(6): 8.

[9] Shaanxi tourism blue book, 2014, shaanxi tourism press: 22.

[10] Internal Information: 42.

[11] Victor Maier Schoenberg. Era of Big Data [M]. Hangzhou: Zhejiang People’s Publishing house,2013: 67.

[12] Application Channels. The large data of shallow data structure and management [EB/OL]. [2012-09-10]. http://www.50cnnet.com/html/2012/dashuju 0910/33580.html

[13] Guo xin. Tourism big data and mining analysis research [J]. Computer knowledge and technology,2013,9(14):3215-3216.

[14] Lu yuan. Analysis of intelligent tourism mode from the perspective of big data [J]. Holiday tourism,2018(12):98-99.

[15] Zhang xiaohua, Guo xuan, Li Juan, Ma hao. Intelligent tourism information analysis system based on big data platform [J]. Fujian computer,2015,31(08):93-94.

[16] Tian qiuyang. Application analysis of big data in tourism management [J]. Holiday tourism,2018(12):100-105.

[17] Xiao jie. Application analysis of big data in tourism management [J]. Holiday tourism,2018(12):104-105.

[18] Zhang lijun, Zhao xia. Tourism management service system based on big data analysis [J]. Information communication,2014(11):232-233.

[19] Liu xiaoyan, Zhang min. Tourism management information system based on artificial intelli- gence [J]. Automation and instrumentation,2016(08):147-148.

[20] Meng zhihui, Liu guanglu, Hao chengyu. Analysis on the development path of tourist attrac- tions based on the analysis of visitor flow data [J]. World of labor security,2017(36):63.

[21] Sun yan-ping, Zhang lin, Lu ren-yi. Neural network method for tourism source prediction [J]. Human geography,2002(06):50-52.

[22] Yu mingtao, Ye xiaotong. Improved BP neural network based on particle swarm optimization [J]. Microcomputers and applications,2015,34(21):51-54.

[23] Jing le, Development of China’s tourism service trade under the background of “One Belt And One Road” [J]. Reform and strategy,2017,33(07):172-172

[24] Cheng qian, Development of tourism service trade in China under the background of “One Belt And One Road” [J]. Modern economic information,2017(10):140.

[25] Wang zhan-long. Influence of “One Belt And One Road” on tourism [J]. Tourism overview (second half),2018(12):46.

[26] Han zhiyong. The impact of “One Belt And One Road” strategy on asean tourism [J]. Rural economy and science and technology,2016,27(06):73

[27] Song hongjuan, jiang jade-shi. Value judgment of “One Belt And One Road” tourism market [J]. Development research,2017(04):149-155.

[28] Aslihan Dursun A M C. Using data mining techniques for profiling profitable hotel customers: An application of RFM analysis [J]. Tourism Management Perspectives, 2016 (18) : 153 - 160.

[29] Jehn - Yih Wong H C P C. Identifying Valuable Travelers and Their Next Foreign Destination by the Application of Data Mining Techniques [J]. Asia Pacific Journal of Tourism Research, 2006, 11 (4) : 355 - 373.

[30] Yang ting. Development path of xi’an cultural tourism industry under the background of “One Belt And One Road” [N]. Xi’an daily,2019-06-25(007).

[31] Su hongxia, Zhang jie. Dynamic evolution characteristics of shaanxi international tourist mar- ket under the background of One Belt And One Road – based on statistical data from 2007 to 2016 [J]. Henan science,2019,37(04):684-688.

[32] Commentator of this newspaper. Build “One Belt And One Road” into the road of civilization [N]. China culture daily,2019-04-26(001).

[33] Wen ke, nearly 150 million outbound tourists visited China in 2018,China consumer news - China consumer network 2019-02-18.

[34] Mar´ıa Henar, Salas-Olmedo, Borja, Moya-G´omez, Juan Carlos,Garc´ıa-Palomares, Javier Guti´errez. Tourists’ digital footprint in cities: Comparing Big Data sources[J]. Tourism Man- agement, 2018,66.

[35] Sheng-Hshiung Tsaur, Yi-Chang Chiu, Chung-Huei Huang. Determinants of guest loyalty to international tourist hotels—a neural network approach[J]. Tourism Management, 2002, 23(4).

[36] Stephen F. Witt, Lindsay W. Turner. Trends and Forecasts for Inbound Tourism to China[J]. Journal of Travel & Tourism Marketing, 2003, 13(1-2).

[37] Alfonso Palmer,Juan Jos´e Montan˜o,Albert Ses´e. Designing an artificial neural network for forecasting tourism time series[J]. Tourism Management, 2005, 27(5)

[38] Chi Kin Chan, Stephen F. Witt,Y.C.E. Lee,H. Song. Tourism forecast combination using the CUSUM technique[J]. Tourism Management, 2009, 31(6).

[39] Jamal Shahrabi,Esmaeil Hadavandi,Shahrokh Asadi. Developing a hybrid intelligent model for forecasting problems: Case study of tourism demand time series[J]. Knowledge-Based Systems, 2013, 43.

[40] Oscar Claveria, Salvador Torra. Forecasting tourism demand to Catalonia: Neural networks vs. time series models[J]. Economic Modelling, 2014, 36.

[41] Yen-Hsien Lee, Ya-Ling Huang. Accurately Forecasting Model for the Stochastic Volatility Data in Tourism Demand[J]. Modern Economy, 2011, 02(05).

[42] Haiyan Song,Stephen F. Witt. Forecasting international tourist flows to Macau[J]. Tourism Management, 2006, 27(2).

[43] Wei CHEN, Jian SUN, Nonmembers, Shangce GAO, Member, Jiu-Jun CHENG, Jiahai WANG and Yuki TODO‘Using a Single Dendritic Neuron to Forecast Tourist Arrivals to Japan’, IEICE TRANS. INF. & SYST., VOL.E100–D, NO.1 JANUARY, pp.190–202, 2017.

[44] Athanasopoulos, G., Hyndman, R. J., Song, H. Y., & Wu, D. C. (2011). The tourism forecast- ing competition. International Journal of Forecasting, 27(3), 822–844.

[45] Jungmittag, A. (2016). Combination of forecasts across estimation windows: An application to air travel demand. Journal of Forecasting, 35(4), 373–380.

[46] Liang, Y. H. (2014). Forecasting models for Taiwanese tourism demand after allowance for Mainland China tourists visiting Taiwan. Computers & Industrial Engineering, 74(1), 111–119.

[47] Emily Ma, Yulin Liu, Jinghua Li, Su Chen. Anticipating Chinese tourists arrivals in Australia: A time series analysis. Tourism Management Perspectives 17 (2016) 50–58.

[48] Shaowen Li, Tao Chen, Lin Wang, Chenghan Ming. Effective tourist volume forecasting sup- ported by PCA and improved BPNN using Baidu index. Tourism Management 68 (2018) 116-126.

[49] Zhan R, Wan J. Iterated unscented Kalman filter for passive target tracking. IEEE Trans Aero Elec Sys 2007;43:1155-1163.

[50] Witt, S. F. and Witt, C. A., Forecasting tourism demand: A review of empirical research, International Journal of Forecasting, Vol. 11, 1995, pp. 447–475.

[51] Ma, H., and Zhang, Z., Grey prediction with Markov-Chain for Crude oil production and consumption in China, Advances in Intelligent and Soft Computing, Vol. 56, 2009, pp. 551–561.

[52] Vietnam National Administration of Tourism, Tourism Statistics, Retrieved on Mar. 10, 2012 from http://www.vietnamtourism. gov.vn/index.php?cat=2020

[53] Ouerfelli, C., Co-integration analysis of quarterly European tourism demand in Tunisia, Tourism Management, Vol. 29(1), 2008, pp. 127–137.

[54] Song, H. and Witt, S. F., General-to-specifi c modeling to international tourism demand forecasting, Journal of Travel Research, Vol. 42(1), 2003, pp. 65–74.FORECASTING MODELS IN TOURISM DEMAND 43

[55] Wang, Z., Liu, F., Wu, J., & Wang, J. (2014). A hybrid forecasting model based on bivariate division and a backpropagation artificial neural network optimized by chaos particle swarm optimization for day-ahead electricity price. Abstract and Applied Analysis, 2014, 31

[56] Sun JL, Zhang J, Ma HL, et al. Epidemiological features of typhoid/ paratyphoid fever in provinces with high incidence rate and in the whole country, in 2012. Zhonghua Liu Xing Bing Xue Za Zhi 2013;34:1183–8.

[57] Vollaard AM, Ali S, Van Asten HA, et al. Risk factors for typhoid and paratyphoid fever in Jakarta, Indonesia. JAMA 2004;291:2607–15.

[58] Bhan MK, Bahl R, Bhatnagar S. Typhoid and paratyphoid fever. Lancet 2005;366:749–62.

[59] Sun L, Shao Q, Wang ZQ, et al. Spatial structure of rodent populations and infection patterns of hantavirus in seven villages of Shandong Province from February 2006 to January 2007. Chin Med J (Engl) 2011;124:1639–46.

[60] Hsu, L. C. and Wang, C. H. The development and testing of a modifi ed Diff usion model for predicting tourism demand, International Journal of Management, Vol. 25(3), 2008a, pp. 439–445.

[61] Hsu, L. C. and Wang, C. H. Apply multivariate forecasting model to tourism industry, Tourism: An International Interdisciplinary Journal, Vol. 56(2), 2008b, pp. 159–172.

[62] Rob J Hyndman and George Athanasopoulos. Forecasting: Principles and Practice, Monash University, Australia

[63] J. faraway and C. chatfield.: Time series forecasting with neural networks: a comparative study using the airline data.[J], applied statistics, pp. 231–250, (1998).

[64] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov.: Dropout: A simple way to prevent neural networks from overfitting [J], Mach. Learn. Res., vol. 15, pp. 1929-195, (2014)

[65] Amigo JM, Hirata Y, Aihara K. On the limits of probabilistic forecasting in nonlinear time series analysis II: differential entropy. Chaos 2017;27:083125.

[66] Huang JC. Application of grey system theory in telecare. Comput Biol Med 2011;41:302–6.

[67] Crump JA, Luby SP, Mintz ED. The global burden of typhoid fever. Bull World Health Organ 2004;82:346–53.

[68] Lagos, D. G., Exploratory forecasting methodologies for tourism demand, Ekistics Journal, Vol. 396, 1999, pp. 143–154.

[69] Li, G. D., Wang, C. H., Masuda, S., and Nagai, M. A research on short term load forecasting problem applying improved grey dynamic model, Electrical power and Energy systems, Vol. 33, 2011, pp. 809–816.

[70] Pai P F, Hong W C.: An improved neural network model in forecasting arrivals [J]. Annals of Tourism Research, 32(4): 1138-1141(2005).

[71] Box, G.M. Jenkins.: Time series analysis forecasting and control [J]. Technometrics, 19:3, 343-344

[72] Yongkang Zheng.: The research for short-term load forecasting about Phase Space Recon- struction and Support Vector Machine [D]. Chengdu: Southwest Jiaotong University, 2008.

[73] Gan R, Chen X, Yan Y, et al. Application of a hybrid method combining grey model and back propagation artificial neural networks to forecast hepatitis B in china. Computer Math Methods Med 2015;2015:328273.

[74] Chau TT, Campbell JI, Galindo CM, et al. Antimicrobial drug resistance of Salmonella en- terica serovar typhi in Asia and molecular mechanism of reduced susceptibility to the fluoro- quinolones. Antimicrob Agents Chemother 2007;51:4315–23.

[75] Graves, A., Jaitly, N., Mohamed, A.: Hybrid Speech Recognition with Deep Bidirectional LSTM[J], Automatic Speech Recognition and Understanding (ASRU), (2013).

[76] Felix A. Gers, Ju¨rgen Schmidhuber, Fred A.: Cummins Learning to forget: Continual predic- tion with LSTM Neural Computation, 12 (10), pp.

[77] Mahmod WE, Watanabe K. Modifified Grey Model and its application to groundwater flflow analysis with limited hydrogeological data: a case study of the Nubian Sandstone, Kharga Oasis, Egypt. Environ Monit Assess 2014;186:1063–81.

[78] Bao CZ, Mayila M, Ye ZH, et al. Forecasting and analyzing the disease burden of aged pop- ulation in china, based on the 2010 global burden of disease study. Int J Environ Res Public Health 2015;12:7172–84.

[79] Parry CM. The treatment of multidrug-resistant and nalidixic acid-resistant typhoid fever in Vietnam. Trans R Soc Trop Med Hyg 2004;98:413–22.

[80] Filippini, Massimo, and Lester C. Hunt. (2011) “Energy demand and energy efficiency in the OECD countries: a stochastic demand frontier approach.” Energy Journal 32 (2): 59–80.

[81] Moutinho, L.: Consumer behavior in tourism[J]. Marketing, 21(10), 1–44(1987).

[82] Box G E P, Pierce D A.: Distribution of residual autocorrelations in autoregressive-integrated moving average time series models [J]. Journal of the American statistical Association, 65(332): 1509-1526(1970).

[83] M. S. Ahmed and A. R. Cook.: Analysis of freeway traffic time-series data by using Box-Jenkins techniques[J]. Transp. Res. Rec., no. 722, pp. 1-9, (1979).

[84] C. Lim and M. McAleer.: Time series forecasts of international travel demand for Australia[J]. Tourism Management,23, 389-396, (2002).

[85] Kenji Doya.: Bifurcations in the learning of recurrent neural networks[J].Proceedings of IEEE International Symposium on Circuits and Systems 1992, vol. 6, pp. 2777–2780(1992).

[86] J. Faraway and C. Chatfield.: Time series forecasting with neural networks: a comparative study using the airline data[J], Applied statistics, pp. 231–250, (1998).

[87] Herbert Jaeger.: Tutorial on training recurrent neural networks, covering BPTT, RTRL, EKF and the “echo state network” approach[J], German National Research Center for Information Technology, Technical Report GMD Report 159, (2002).

[88] Yoshua Bengio, Patrice Simard, Paolo Frasconi.: Learning long-term dependencies with gra- dient descent is difficult[J], IEEE Transactions on Neural Networks, 5 (2), pp. 157-166(1994). 2451-2471(2000).

[89] Tan, Y. F., McCahon, C., and Miller, J., Modelling tourist fl ows to Indonesia and Malaysia, Journal of Travel and Tourism Marketing, Vol. 12(1-2), 2002, pp. 63–84.

[90] Tsaur, R. C and Kuo, T. C, The adaptive fuzzy time series model with an application to Taiwan’s tourism demand, Expert systems with Applications, Vol. 38, 2011, pp. 9164–9171.

[91] Wang, S. J., Wang, W. L, Huang, C. T., and Chen, S. C., Improving inventory eff ective- ness in RFID-enabled global supply chain with Grey forecasting model, Journal of Strategic Information Systems, Vol. 20, 2011, pp. 307–322.

[92] S. Hochreiter and J. Schmidhuber: Long short-term memory[J], Neural Compute., vol. 9, pp. 1735-17 0, (1997).

[93] Askari, M., and Fetanat, A., Long-term load forecasting in power system: Grey system prediction-based models, Journal of Applied Sciences, Vol. 11, 2011, pp. 3034–3038.

[94] Chang, Y. W. and Liao, M. Y., A seasonal ARIMA model of tourism forecasting: The case of Taiwan, Asia Pacifi c journal of Tourism research, Vol. 15(2), 2010, pp. 215–221.

[95] Kan, M. L., Lee, Y. B. and Chen, W. C., Apply grey prediction in the number of Tourist, The fourth international conference on Genetic and Evolutionary computing, 2010, pp. 481–484.42 T. L. NGUYEN, M. H. SHU, Y. F. HUANG AND B. M. HSU

[96] Huang, Y. L. and Lee, Y. H, Accurately forecasting model for the Stochastic Volatility data in tourism demand, Modern economy, Vol. 2, 2011, pp. 823–829.

[97] Jackman, M. and Lorde, T. Modeling and forecasting tourist fl ows to Barbados using Seasonal univariate time series models, Tourism and Hospitality Research, Vol. 10, 2010, pp. 1–13.

[98] Obaro SK, Iroh Tam PY, Mintz ED. The unrecognized burden of typhoid fever. Expert Rev Vaccines 2017;16:249–60.

[99] Liu FF, Zhao SL, Chen Q, et al. Surveillance data on typhoid fever and paratyphoid fever in 2015, China. Zhonghua Liu Xing Bing Xue Za Zhi 2017;38:754–8.

[100] O. Claveria, E. Monte, and S. Torra, “Tourism demand forecasting with neural network models: different ways of treating information,” International Journal of Tourism Research, vol. 17, no. 5, pp. 492–500, 2015.

[101] S. Gao, Y Wang, Q Cao, Z Tang. Gravitational search algorithm combined with chaos for unconstrained numerical optimization, Applied Mathematics and Computation 231, 48-62, 2014

[102] T Zhou, S Gao, J Wang, C Chu, Y Todo, Z Tang. Financial time series prediction using a dendritic neuron model, Knowledge-Based Systems 105, 214-224, 2016

[103] S Gao, H Dai, G Yang, Z Tang. A novel clonal selection algorithm and its application to traveling salesman problem, IEICE transactions on fundamentals of electronics, communications and . . . , 2007

[104] S Gao, M Zhou, Y Wang, J Cheng, H Yachi, J Wang. Dendritic neuron model with effective learning algorithms for classification, approximation, and prediction, IEEE transactions on neural networks and learning systems 30 (2), 601-614, 2019.

[105] L. Wang, Y. Zeng, and T. Chen, “Back propagation neural network with adaptive differential evolution algorithm for time series forecasting,” Expert Systems with Applications, vol. 42, no. 2, pp. 855–863, 2015.

[106] K.-Y. Chen and C.-H. Wang, “Support vector regression with genetic algorithms in forecasting tourism demand,” Tourism Management, vol. 28, no. 1, pp. 215– 226, 2007.

[107] M. Khashei, S. R. Hejazi, and M. Bijari, “A new hybrid artificial neural networks and fuzzy regression model for time series forecasting,” Fuzzy Sets and Systems, vol. 159, no. 7, pp. 769– 786, 2008.

[108] O. Claveria, E. Monte, and S. Torra, “Tourism demand forecasting with neural network models: difffferent ways of treating information,” International Journal of Tourism Research, vol. 17, no. 5, pp. 492–500, 2015.

[109] L. Wang, Y. Zeng, and T. Chen, “Back propagation neural network with adaptive differential evolution algorithm for time series forecasting,” Expert Systems with Applications, vol. 42, no. 2, pp. 855–863, 2015.

[110] T Jiang, S Gao, D Wang, J Ji, Y Todo, Z Tang. A neuron model with synaptic nonlinearities in a dendritic tree for liver disorders. IEEJ Transactions on Electrical and Electronic Engineering 12 (1), 105-115, 2017

[111] Z Xu, Y Wang, S Li, Y Liu, Y Todo, S Gao. Immune algorithm combined with estimation of distribution for traveling salesman problem. IEEJ Transactions on Electrical and Electronic Engineering 11, S142-S154, 2016

[112] S. Gao, H Chai, B Chen, G Yang. Hybrid gravitational search and clonal selection algorithm for global optimization, International Conference in Swarm Intelligence, 1-10, 2013

[113] S. Gao, H Dai, J Zhang, Z Tang. An expanded lateral interactive clonal selection algorithm and its application. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, vol.E91-A, no.8, pp.2223-2231, August, 2008.

[114] S. Gao, Z Tang, H Dai, J Zhang. An improved clonal selection algorithm and its application to traveling salesman problems. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, vol.E90-A, no.12, pp.2930-2938, December 2007.

参考文献をもっと見る

全国の大学の
卒論・修論・学位論文
を
一発検索！

この論文の関連論文を見る