Smart Artificial Firefly Colony Algorithm-Based Support Vector Regression for Enhanced Forecasting in Civil Engineering

Computer-Aided Civil and Infrastructure Engineering

Volumn 30, Issue 9, 2015, Pages 715-732

  Chou, Jui Sheng ^a   Pham, Anh Duc ^a,b  

^a NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^b THE UNIVERSITY OF DANANG   (Viet Nam)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLUMINESCENCE; CHAOTIC SYSTEMS; DATA MINING; GLOBAL OPTIMIZATION; HIGH PERFORMANCE CONCRETE; HYBRID SYSTEMS;

ANALYTICAL RESULTS; BUILDING COOLING LOAD; COLONY ALGORITHMS; CROSS VALIDATION ALGORITHMS; FIREFLY ALGORITHMS; LEAST SQUARES SUPPORT VECTOR REGRESSION; META HEURISTIC ALGORITHM; RESILIENT MODULUS;

SUPPORT VECTOR REGRESSION;

ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; CIVIL ENGINEERING; CONCRETE; DATA MINING; ERROR ANALYSIS; FORECASTING METHOD; HYPOTHESIS TESTING;

EID: 84937973686     PISSN: 10939687     EISSN: 14678667     Source Type: Journal    
DOI: 10.1111/mice.12121     Document Type: Article

References (80)

1. Adeli, H. (2001), Neural networks in civil engineering: 1989-2000, Computer-Aided Civil and Infrastructure Engineering, 16(2), 126-42.
2. Adeli, H. & Panakkat, A. (2009), A probabilistic neural network for earthquake magnitude prediction, Neural Networks, 22(7), 1018-24.
3. Adeli, H. & Wu, M. (1998), Regularization neural network for construction cost estimation, Journal of Construction Engineering and Management, 124(1), 18-24.
4. Al-Homoud, M. S. (2001), Computer-aided building energy analysis techniques, Building and Environment, 36(4), 421-33.
5. Amini, F., Hazaveh, N. K. & Rad, A. A. (2013), Wavelet PSO-based LQR algorithm for optimal structural control using active tuned mass dampers, Computer-Aided Civil and Infrastructure Engineering, 28(7), 542-57.
6. ASSHTO (2008), Mechanistic-Empirical Pavement Design Guide: A Manual of Practice, Washington DC.
7. Asuncion, A. & Newman, D. (2007), Uci Machine Learning Repository, University of California, School of Information and Computer Science, Irvine, CA.
8. Atici, U. (2010), Prediction of the strength of mineral-addition concrete using regression analysis, Magazine of Concrete Research, 62(8), 585-92.
9. Bhanja, S. & Sengupta, B. (2002), Investigations on the compressive strength of silica fume concrete using statistical methods, Cement and Concrete Research, 32(9), 1391-94.
10. Bianchini, A. & Bandini, P. (2010), Prediction of pavement performance through neuro-fuzzy reasoning, Computer-Aided Civil and Infrastructure Engineering, 25(1), 39-54.
11. Boukhatem, B., Kenai, S., Tagnit-Hamou, A. & Ghrici, M. (2011), Application of new information technology on concrete: an overview, Journal of Civil Engineering and Management, 17(2), 248-58.
12. Burdick, A. (2011), Strategy Guideline: Accurate Heating and Cooling Load Calculations, IBACOS, Inc., US Department of Energy, Washington DC.
13. Carmichael III, R. F. & Stuart, E. (1985), Predicting resilient modulus: a study to determine the mechanical properties of subgrade soils, Transportation Research Record, TRID, Washington DC.
14. Chou, J. S., Cheng, M. Y. & Wu, Y. W. (2013), Improving classification accuracy of project dispute resolution using hybrid artificial intelligence and support vector machine models, Expert Systems with Applications, 40(6), 2263-74.
15. Chou, J.-S. & Le, T.-S. (2011), Reliability-based performance simulation for optimized pavement maintenance, Reliability Engineering & System Safety, 96(10), 1402-10.
16. Chou, J.-S. & Pham, A.-D. (2013), Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength, Construction and Building Materials, 49, 554-63.
17. Coelho, L. d. S. & Mariani, V. C. (2013), Improved firefly algorithm approach applied to chiller loading for energy conservation, Energy and Buildings, 59, 273-78.
18. Dai, H., Zhang, H., Wang, W. & Xue, G. (2012), Structural reliability assessment by local approximation of limit state functions using adaptive Markov chain simulation and support vector regression, Computer-Aided Civil and Infrastructure Engineering, 27(9), 676-86.
19. Dharia, A. & Adeli, H. (2003), Neural network model for rapid forecasting of freeway link travel time, Engineering Applications of Artificial Intelligence, 16(7-8), 607-13.
20. Dong, B., Cao, C. & Lee, S. E. (2005), Applying support vector machines to predict building energy consumption in tropical region, Energy and Buildings, 37(5), 545-53.
21. Edwards, R. E., New, J. & Parker, L. E. (2012), Predicting future hourly residential electrical consumption: a machine learning case study, Energy and Buildings, 49, 591-603.
22. Ekici, B. B. & Aksoy, U. T. (2009), Prediction of building energy consumption by using artificial neural networks, Advances in Engineering Software, 40(5), 356-62.
23. Gandomi, A. & Alavi, A. (2012), A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems, Neural Computing and Applications, 21(1), 171-87.
24. Gandomi, A. H., Yang, X. S., Talatahari, S. & Alavi, A. H. (2013), Firefly algorithm with chaos, Communications in Nonlinear Science and Numerical Simulation, 18(1), 89-98.
25. Geisel, T., Nierwetberg, J. & Zacherl, A. (1985), Accelerated diffusion in Josephson junctions and related chaotic systems, Physical Review Letters, 54(7), 616-19.
26. George, K. P. (2004), Prediction of Resilient Modulus from Soil Index Properties, Mississippi Department of Transportation, Jackson, Mississippi.
27. He, D., He, C., Jiang, L. G., Zhu, H. W. & Hu, G. R. (2001), Chaotic characteristics of a one-dimensional iterative map with infinite collapses, IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 48(7), 900-06.
28. Hong, W.-C. (2009), Hybrid evolutionary algorithms in a SVR-based electric load forecasting model, International Journal of Electrical Power & Energy Systems, 31(7-8), 409-17.
29. Hong, W.-C., Dong, Y., Chen, L.-Y. & Wei, S.-Y. (2011), SVR with hybrid chaotic genetic algorithms for tourism demand forecasting, Applied Soft Computing, 11(2), 1881-90.
30. Hou, Z. & Lian, Z. (2009), An application of support vector machines in cooling load prediction, in Proceedings of the International Workshop on Intelligent Systems and Applications, ISA 2009, Wuhan, China, 1-4.
31. Hsiao, F.-Y., Wang, S.-H., Wang, W.-C., Wen, C.-P. & Yu, W.-D. (2012), Neuro-fuzzy cost estimation model enhanced by fast messy genetic algorithms for semiconductor hookup construction, Computer-Aided Civil and Infrastructure Engineering, 27(10), 764-81.
32. Hsu, C.-W. & Lin, C.-J. (2002), A simple decomposition method for support vector machines, Machine Learning, 46(1-3), 291-314.
33. Huang, Y. H. (1993), Pavement Analysis and Design, Prentice Hall, Englewood Cliffs, NJ.
34. Jiang, X. & Adeli, H. (2005), Dynamic wavelet neural network model for traffic flow forecasting, Journal of Transportation Engineering, 131(10), 771-79.
35. Jiang, X. & Adeli, H. (2008), Neuro-genetic algorithm for non-linear active control of structures, International Journal for Numerical Methods in Engineering, 75(7), 770-86.
36. Jie, S., Hui, L. & Adeli, H. (2013), Concept drift-oriented adaptive and dynamic support vector machine ensemble with time window in corporate financial risk prediction, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(4), 801-13.
37. Keerthi, S. S. & Lin, C.-J. (2003), Asymptotic behaviors of support vector machines with Gaussian kernel, Neural Computation, 15(7), 1667-89.
38. Kohavi, R. (1995), A study of cross-validation and bootstrap for accuracy estimation and model selection, in Proceedings of the International Joint Conference on Artificial Intelligence, Morgan Kaufmann, San Francisco, CA, 1137-43.
39. Kris, D. B. (2011), Least Squares Support Vector Regression with Applications to Large-Scale Data: A Statistical Approach, Faculty of Engineering, KU Leuven, Katholieke Universiteit Leuven, Leuven, Belgium.
40. Li, D., Xu, L., Goodman, E. D., Xu, Y. & Wu, Y. (2013), Integrating a statistical background-foreground extraction algorithm and SVM classifier for pedestrian detection and tracking, Integrated Computer-Aided Engineering, 20(3), 201-16.
41. Li, Q., Meng, Q., Cai, J., Yoshino, H. & Mochida, A. (2009), Applying support vector machine to predict hourly cooling load in the building, Applied Energy, 86(10), 2249-56.
42. Li, Y., Deng, S. & Xiao, D. (2011), A novel hash algorithm construction based on chaotic neural network, Neural Computing and Applications, 20(1), 133-41.
43. Liao, S.-H., Chu, P.-H. & Hsiao, P.-Y. (2012), Data mining techniques and applications - a decade review from 2000 to 2011, Expert Systems with Applications, 39(12), 11303-11.
44. Lin, D.-Y. & Ku, Y.-H. (2014), Using genetic algorithms to optimize stopping patterns for passenger rail transportation, Computer-Aided Civil and Infrastructure Engineering, 29(4), 264-78.
45. Montalvo, I., Izquierdo, J., Pérez-García, R. & Herrera, M. (2014), Water distribution system computer-aided design by agent swarm optimization, Computer-Aided Civil and Infrastructure Engineering, 29(6), 433-48.
46. Mosa, A. M., Rahmat, R. A. O. K., Ismail, A. & Taha, M. R. (2013), Expert system to control construction problems in flexible pavements, Computer-Aided Civil and Infrastructure Engineering, 28(4), 307-23.
47. Mousavi, S. M., Aminian, P., Gandomi, A. H., Alavi, A. H. & Bolandi, H. (2012), A new predictive model for compressive strength of HPC using gene expression programming, Advances in Engineering Software, 45(1), 105-14.
48. Nazzal, M. D. & Tatari, O. (2013), Evaluating the use of neural networks and genetic algorithms for prediction of subgrade resilient modulus, International Journal of Pavement Engineering, 14(4), 364-73.
49. Olamaei, J., Moradi, M. & Kaboodi, T. (2013), A new adaptive modified firefly algorithm to solve optimal capacitor placement problem, in Proceedings of the 2013 18th Conference on Electrical Power Distribution Networks (EPDC), Kermanshah, Iran, 1-6.
50. Pacheco, R., Ordóñez, J. & Martínez, G. (2012), Energy efficient design of building: a review, Renewable and Sustainable Energy Reviews, 16(6), 3559-73.
51. Pal, S. K., Rai, C. S. & Singh, A. P. (2012), Comparative study of firefly algorithm and particle swarm optimization for noisy non-linear optimization problems, International Journal of Intelligent Systems and Applications, 4, 50-57.
52. Panakkat, A. & Adeli, H. (2009), Recurrent neural network for approximate earthquake time and location prediction using multiple seismicity indicators, Computer-Aided Civil and Infrastructure Engineering, 24(4), 280-92.
53. Park, H. I., Kweon, G. C. & Lee, S. R. (2009), Prediction of resilient modulus of granular subgrade soils and subbase materials using artificial neural network, Road Materials and Pavement Design, 10(3), 647-65.
54. Pham, D., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S. & Zaidi, M. (2005), The bees algorithm, Technical Note, Manufacturing Engineering Centre, Cardiff University, UK.
55. Rahim, A. M. (2005), Subgrade soil index properties to estimate resilient modulus for pavement design, International Journal of Pavement Engineering, 6(3), 163-69.
56. Rigatos, G. G. (2013), Adaptive fuzzy control for differentially flat MIMO nonlinear dynamical systems, Integrated Computer-Aided Engineering, 20(2), 111-26.
57. Samant, A. & Adeli, H. (2001), Enhancing neural network traffic incident-detection algorithms using wavelets, Computer-Aided Civil and Infrastructure Engineering, 16(4), 239-45.
58. Sarma, K. & Adeli, H. (2000), Fuzzy genetic algorithm for optimization of steel structures, Journal of Structural Engineering, 126(5), 596-604.
59. Senthilnath, J., Omkar, S. N. & Mani, V. (2011), Clustering using firefly algorithm: performance study, Swarm and Evolutionary Computation, 1(3), 164-71.
60. Shafahi, Y. & Bagherian, M. (2013), A customized particle swarm method to solve highway alignment optimization problem, Computer-Aided Civil and Infrastructure Engineering, 28(1), 52-67.
61. Siamak, S. G., Hamed, B. J. & Ramezanianpour, A. A. (2012), Hybrid support vector regression - particle swarm optimization for prediction of compressive strength and RCPT of concretes containing metakaolin, Construction and Building Materials, 34, 321-29.
62. Solanki, P., Zaman, M. & Ebrahimi, A. (2009), Regression and artificial neural network modeling of resilient modulus of subgrade soils for pavement design applications, in K. Gopalakrishnan, H. Ceylan and N. Attoh-Okine (eds.), Intelligent and Soft Computing in Infrastructure Systems Engineering, Springer, Berlin Heidelberg, pp. 269-304.
63. Surjanovic, S. & Bingham, D. (2013), Virtual Library of Simulation Experiments: Test Functions and Datasets, Retrieved November 12, 2013, from http://www.sfu.ca/~ssurjano.
64. Suykens, J. A. K. & Vandewalle, J. (1999), Least squares support vector machine classifiers, Neural Processing Letters, 9(3), 293-300.
65. Suykens, J. A. K., Vandewalle, J. & De-Moor, B. (2001), Optimal control by least squares support vector machines, Neural Networks, 14(1), 23-35.
66. Suykens, J. A. K., Van-Gestel, T., De-Brabanter, J., De-Moor, B. & Vandewalle, J. (2002), Least Squares Support Vector Machines, World Scientific, Singapore City, Singapore.
67. Tsanas, A. & Xifara, A. (2012), Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools, Energy and Buildings, 49, 560-67.
68. Van-Gestel, T., Suykens, J. K., Baesens, B., Viaene, S., Vanthienen, J., Dedene, G., De-Moor, B. & Vandewalle, J. (2004), Benchmarking least squares support vector machine classifiers, Machine Learning, 54(1), 5-32.
69. Vapnik, V. N. (1995), The Nature of Statistical Learning Theory, Springer-Verlag, New York.
70. Verma, A., Wei, X. & Kusiak, A. (2013), Predicting the total suspended solids in wastewater: a data-mining approach, Engineering Applications of Artificial Intelligence, 26(4), 1366-72.
71. Wang, H. & Hu, D. (2005), Comparison of SVM and LS-SVM for regression, in Proceedings of the International Conference on Neural Networks and Brain, 2005. ICNN&B '05, Beijing, China, 279-83.
72. Yang, D., Li, G. & Cheng, G. (2007), On the efficiency of chaos optimization algorithms for global optimization, Chaos, Solitons & Fractals, 34(4), 1366-75.
73. Yang, I. T. & Hsieh, Y.-H. (2013), Reliability-based design optimization with cooperation between support vector machine and particle swarm optimization, Engineering with Computers, 29(2), 151-63.
74. Yang, X.-S. (2008), Firefly Algorithm, Luniver Press, Bristol, UK.
75. Yang, X.-S. (2010), Engineering Optimization: An Introduction with Metaheuristic Applications, John Wiley & Sons, Hoboken, NJ.
76. Yang, X.-S. & Deb, S. (2009), Cuckoo search via Lévy flights, in Proceedings of the World Congress on Nature & Biologically Inspired Computing, NaBIC 2009, Coimbatore, India, 210-14.
77. Yang, X.-S., Deb, S. & Fong, S. (2011), Accelerated particle swarm optimization and support vector machine for business optimization and applications, in S. Fong (ed.), Networked Digital Technologies, Springer, Berlin Heidelberg, pp. 53-66.
78. Yeh, I. C. (1998), Modeling of strength of high-performance concrete using artificial neural networks, Cement and Concrete Research, 28(12), 1797-808.
79. Yeh, I. C. (2008), Modeling slump of concrete with fly ash and superplasticizer, Computers and Concrete, 5(6), 559-72.
80. Yeh, I. C. & Lien, L.-C. (2009), Knowledge discovery of concrete material using genetic operation trees, Expert Systems with Applications, 36(3, Part 2), 5807-12.