Reallocation model in land consolidation using multi‐objective particle swarm optimization dealing with landowners' rights

Abstract

In conventional reallocation, farmers' preferences are used to determine the location of their new parcels in predetermined blocks. The most common conflict that can arise during this process is that demand may be high for some blocks. The manner of resolving such disputes, which deal directly with landowners' rights, can affect the success or failure of land consolidation projects. In this study, a novel model for land reallocation is proposed which is based on the principle that the initial situation of the landowner's parcels before land consolidation will be comparable with the new situation that includes all of his/her rights. For this, a spatial similarity‐based approach was proposed, taking into account geometric, ownership, physical, and locational criteria. Then, the agricultural land reallocation model (LR‐MOPSO) was developed using the multi‐objective particle swarm algorithm. Three objectives were defined: (1) simultaneous consideration of farmers' priority and spatial similarity criteria; (2) pooling of farmers' fragmented parcels; and (3) optimal placement of parcels within the block. The LR‐MOPSO model was applied to an Iranian case study and results were compared with a conventional approach. With this model decision‐makers in land consolidation projects will be able to redistribute parcels in a more transparent way, while dealing with landowners' rights.

Optimization of Land Use Based on the Source and Sink Landscape of Ecosystem Services: A Case Study of Fengdu County in the Three Gorges Reservoir Area, China

“…The first category is traditional mathematical optimization models, such as gray linear programming models [33] and multiobjective programming (MOP) models [25,34]. The second category is heuristic algorithms, such as genetic algorithms [35], simulated annealing algorithms [36], ant colony algorithms [37], and particle swarm algorithms [38]. The third category includes other methods, such as Markov chains [39], system dynamics models [40], and neural network models [41].…”