:: Volume 20, Issue 69 (5-2020) ::
جغرافیایی 2020, 20(69): 39-56 Back to browse issues page
A Comparison of artificial bee colony algorithm (ABC) and Multi Objective Land Allocation (MOLA) for Land use optimal allocation (semnan watershed)
Mojtaba Ghandali1, Kamran Shayesteh *1, Mohammad sadi Mesgari2
1- malayer university
2- khaje nasir university
Abstract:   (1993 Views)
Introduction: As a serviceable tool for land-use management, the optimal allocation of land-use plays an important role in the full exploitation of land-use potential and maintenance of land–ecosystem balance. Land-use allocation is a spatial optimization problem that primarily involves the assignment of specific land activities to suitable land units through a spatial search to optimize land location and type.
 Evaluation and multi-objective allocation of Land use is a resource allocation decision that evaluates the Suitability of each land unit for different land use alternatives. The purpose of this method is to achieve optimal allocation of land use and maximize the suitability between them. Compaction and continuity are also important goals in land use planning for sustainability. During the optimization process, it is difficult to extract logical solutions without considering such a goal.
Numerous methods have been employed to optimize land allocation. In the early stages, various mathematical methods, such as linear programming and integer programming, were commonly used. Recently, meta-heuristic algorithms have received more attention from scholars, and these methods, which include artificial neural networks (ANNs), genetic algorithms (GA), simulated annealing (SA) and ant algorithms, can effectively solve complex spatial optimization problems. Scholars have also applied other metaheuristic algorithms, including ant colony algorithms, artificial immune systems and particle swarm optimization (PSO) algorithms, for land-use allocation.Artificial bee colony (ABC) algorithm, which simulates the foraging behavior of honey bees, was conceived by Karaboga (2005). The ABC algorithm has been found to be effective and capable of producing good results at a low computational cost in both continuous and discrete domains.
Materials & Methods: In this study, using artificial bee colony (ABC) in MATLAB software, considering the continuity and compaction criterion was defined in land use allocation. This algorithm, consists of a information-based pseudorandom initialization method for initial solutions and pseudorandom search strategy consist of cross over and mutation for neighborhood searches.land-use allocation typically involves K types of land use in a study area that can be abstracted into two-dimensional raster data with R rows and C columns. Each cell (i,j) in the raster data can be assigned only one land-use type according to certain constraints, which typically involve multiple decision criteria. Where the weights of criteria can be obtained using the analytic hierarchy process (AHP) method. In order to evaluate the efficiency of this algorithm was compared with MOLA approach in the Idrisi software in terms of suitability, and landscape metrics assigned to each user.
Discussion of Results & Conclusions:  The new approach consists of a information-based pseudorandom initialization method for initial solutions and pseudorandom search strategy for neighborhood searches; together, these methods substantially improve the search efficiency and quality when handling spatial data in large areas.The results showed that ABC in land use allocation, while presenting appropriate results for the amount of suitability for each land use, provided more suitable results in terms of Compaction and continuity of land use patches, and as an appropriate approach to allocate Optimal land use can be applied.
Keywords: multi objective land allocation – landscape - artificial bee colony – Compactness
Full-Text [PDF 1093 kb]   (588 Downloads)    
Type of Study: Research | Subject: Special
Received: 2018/04/8 | Accepted: 2018/09/16 | Published: 2020/05/30

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Volume 20, Issue 69 (5-2020) Back to browse issues page