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

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.