Optimization in Construction Management Using Adaptive Opposition Slime Mould Algorithm

Abstract

The purpose of this research study is to solve a four-objective optimization problem in the construction industry using a hybrid model that combines the slime mould algorithm (SMA) with opposition-based learning. This hybrid model is known as the adaptive opposition slime mould algorithm (AOSMA). Two typical construction projects have introduced time, cost, quality, and safety trade-off (TCQS), which are the factors that have the greatest influence on the completion of a construction project and are represented by optimal results and obtained at Pareto, in order to better illustrate the potential of the proposed model. In order to compare AOSMA with a nondominated sorting genetic algorithm III (NSGA III), multiobjective particle swarm optimization (MOPSO), LHS-based NSGA III, and a hybrid model of MAWA (MAWA-TLBO, MAWA-GA, MAWA-AS, and MAWA-ACS-SGPU) and to assess the model's potential and viability, performance evaluation indexes are applied. To assist project managers in planning time, cost, quality, and safety for construction investment projects, this study creates a hybrid model.