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

被引:121
作者
Chou, Jui-Sheng [1 ]
Anh-Duc Pham [1 ,2 ]
机构
[1] Natl Taiwan Univ Sci & Technol, Dept Civil & Construct Engn, Taipei, Taiwan
[2] Univ Sci & Technol, Univ Danang, Fac Project Management, Danang, Vietnam
关键词
BUILDING ENERGY-CONSUMPTION; NEURAL-NETWORK MODEL; GENETIC ALGORITHMS; COMPRESSIVE STRENGTH; RESILIENT MODULUS; SUBGRADE SOILS; CONCRETE; MACHINE; PREDICTION; OPTIMIZATION;
D O I
10.1111/mice.12121
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Advanced data mining techniques are potential tools for solving civil engineering (CE) problems. This study proposes a novel smart artificial firefly colony algorithm-based support vector regression (SAFCA-SVR) system that integrates firefly algorithm (FA), chaotic maps, adaptive inertia weight, Levy flight, and least squares support vector regression (LS-SVR). First, adaptive approach and randomization methods are incorporated in FA to construct a novel and highly effective metaheuristic algorithm for global optimization. The enhanced FA is then used to optimize parameters in LS-SVR model. The proposed system is validated by comparing its performance with those of empirical methods and previous works via cross-validation algorithm and hypothesis test through the real-world engineering cases. Specifically, high-performance concrete, resilient modulus of subgrade soils, and building cooling load are used as case studies. The SAFCA-SVR achieved 8.8%-91.3% better error rates than those of previous works. Analytical results confirm that using the proposed hybrid system significantly improves the accuracy in solving CE problems.
引用
收藏
页码:715 / 732
页数:18
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