Implementation of meta-heuristic optimization algorithms for interview problem in land consolidation: A case study in Konya/Turkey

Abstract

The cultivation of soil for supply of nutritional products necessary for human life is called agriculture. Although agriculture is very important for human beings, it is getting more difficult day by day to cultivate soil efficiently for various reasons. One of the main causes, which significantly prevents sustainable agriculture, is land fragmentation. Land consolidation is one of the important measures taken in order to prevent further fragmentation of agricultural land and the decrease in yield obtained from agriculture. The land consolidation process consists of several time consuming steps. Interview, today conducted manually in Turkey, is the stage where preferences of landowners are taken. These preferences correspond to the blocks that enterprises want their parcels to be placed at the end of consolidation. The interview phase takes a long time as it is carried out manually by a technician. Various studies have been done to improve the land consolidation, but most of these studies focus on other stages of process. In this study, genetic algorithm, particle swarm optimization, non-dominated sorting genetic algorithm II and multi objective particle swarm optimization are applied on the interview problem. The interview problem is a discrete structure optimization problem, thus its solution with traditional methods is difficult and time consuming. Preference lists are generated automatically using optimization algorithms. These lists are compared with the actual interview lists created by the technician. The experimental results confirm the success of algorithms in solving the real world problem.