Optimization in Construction Management Using Adaptive Opposition Slime Mould Algorithm

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