Many‐objective land use planning using a hypercube‐based NSGA‐III algorithm

Abstract Due to the many objectives and constraints involved in urban land use planning (ULUP), this is considered as a many‐objective and complex optimization problem that needs a variety of geographical analyses. In this article, the main target is improving NSGA‐III as an advanced many‐objective optimization algorithm for solving the ULUP problem. In this study, five objective functions (i.e., consistency, dependency, compactness, suitability, and per capita violation of land uses) are considered for their simultaneous optimization for allocation. The proposed algorithm is tested using the spatial data of region 7, district 1 of Tehran using a vector format. To evaluate the results, two more real datasets were implemented. The performance of the improved algorithm is compared concerning NSGA‐II and NSGA‐III in the main case study area and two other instances. The comparison results show that the improved algorithm increases the convergence and diversity of the generated solutions in ULUP concerning the results obtained by these two other algorithms. The results of the optimization with these methods can help decision‐makers toward sustainable development in the construction of new cities, new towns, and smart cities.