Optimizing time, cost, and carbon in construction: grasshopper algorithm empowered with tournament selection and opposition-based learning

This section is dedicated to introducing the proposed model, known as the eMOGOA, which amalgamates the MOGOA with the TS and OBL mechanisms. To facilitate a comprehensive understanding of this model, the indices, parameters, and decision-making variables are first defined. These elements are fundamental to the decision-making process within the eMOGOA framework and are detailed in “List of symbols” section.

Grasshopper optimization algorithm (GOA)

In the wild, grasshoppers demonstrate the capacity to locate food sources and aggregate in groups for movement and reproduction. A…