Metaheuristic approaches for optimizing agricultural land areas

Abstract

Metaheuristic approaches are the most selected technique to find optimization solution intelligently in many areas of timetabling and scheduling, space allocation, decision making and others. These approaches have promised a better solution in single objective optimization problem. However, there is no revealed discussion on the issue that has more than one problem. In agricultural land use planning, we found that there are two related problems need to be solved intelligently before obtaining the main objective of optimal solution for the land. The problems are i) to allocate the resources into agricultural land optimally, then ii) to arrange the plant in the planting area in order to find an optimal layout. The solution of the both problems will utilize the land and consequently maximize number of plant to be planted in an area respectively. This paper is preliminary investigation towards optimizing agricultural land, in order that we focus on the understanding of the issues in agricultural land and solution methods by referring to the similarity of the previous researches. We also promote the solutions idea and show the complexity of the problems, and finally find that the metaheuristic approaches are a necessity.

Agricultural Model for Allocation of Crops Using Pollination Intelligence Method

“…As said earlier, the modern method of optimization has not been fully explored in solving land allocation problems. Badarudin et al [23] gave a valid conclusion that metaheuristic approaches are good methods for solving problems relating to agriculture and land allocation. Based on this conclusion, we seek further study on the work of the abovementioned authors.…”

Metaheuristics for agricultural land use optimization. A review

“…Inspired by the classification made in (Groot et al 2007), we distinguished studies dealing with economic crop planning, water resources management issues, landscape ecology issues including forest management, and nature restoration (Groot et al 2007;Badarudin et al 2009). Many classifications of the studies dealing with land use allocation are possible following the used method, e.g., simulated annealing, taboo search, evolutionary algorithms, the type of decisions to be The main steps of a particle swarm optimization algorithm implemented, i.e., land use allocation to crops, configuration of the landscape, or combination of allocation and configuration, and the spatiotemporal decision levels, i.e., operational, tactical, strategic.…”