Self-adaptive ant colony optimization for construction time-cost optimization

Abstract

Purpose – This research aims to propose self-adaptive ant colony optimization (SACO) with changing parameters for solving time-cost optimization (TCO) problems to assist the relevant construction management firm with their technological tool. Design/methodology/approach – A SACO with changing parameters based on information entropy has been employed to model TCO problem, which overcomes the intrinsic weakness of premature convergence of the basic ant colony optimization by adjusting parameters according to mean information entropy of the ant system. A computer simulation with Matlab 7.0 based on a prototype example has been carried out on the basis of SACO for TCO problem. Findings – The test results show that the SACO for TCO model can generate a better cost under the same duration and achieve a better Pareto front than other models. Therefore, the SACO can be regarded as a useful approach for solving construction project TCO problems. Research limitations/implications – Further research on selection parameters should be conducted to further improve the robustness of the SACO for TCO model. Practical implications – The modelling results can help the construction management to good result of TCO problems in construction sites. Originality/value – A new approach to study the TCO model is proposed based on SACO.