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ISPRS Int. J. Geo-Inf. 2018, 7(2), 63; doi:10.3390/ijgi7020063

A Knowledge-Informed and Pareto-Based Artificial Bee Colony Optimization Algorithm for Multi-Objective Land-Use Allocation

1
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100864, China
2
Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA
3
Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Nanjing 210097, China
4
Institute of Electronics, Chinese Academy of Sciences, Beijing 100864, China
*
Author to whom correspondence should be addressed.
Received: 18 December 2017 / Revised: 4 February 2018 / Accepted: 6 February 2018 / Published: 12 February 2018
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Abstract

Land-use allocation is of great significance in urban development. This type of allocation is usually considered to be a complex multi-objective spatial optimization problem, whose optimized result is a set of Pareto-optimal solutions (Pareto front) reflecting different tradeoffs in several objectives. However, obtaining a Pareto front is a challenging task, and the Pareto front obtained by state-of-the-art algorithms is still not sufficient. To achieve better Pareto solutions, taking the grid-representative land-use allocation problem with two objectives as an example, an artificial bee colony optimization algorithm for multi-objective land-use allocation (ABC-MOLA) is proposed. In this algorithm, the traditional ABC’s search direction guiding scheme and solution maintaining process are modified. In addition, a knowledge-informed neighborhood search strategy, which utilizes the auxiliary knowledge of natural geography and spatial structures to facilitate the neighborhood spatial search around each solution, is developed to further improve the Pareto front’s quality. A series of comparison experiments (a simulated experiment with small data volume and a real-world data experiment for a large area) shows that all the Pareto fronts obtained by ABC-MOLA totally dominate the Pareto fronts by other algorithms, which demonstrates ABC-MOLA’s effectiveness in achieving Pareto fronts of high quality. View Full-Text
Keywords: land-use allocation; multi-objective; spatial optimization; Pareto; artificial bee colony algorithm land-use allocation; multi-objective; spatial optimization; Pareto; artificial bee colony algorithm
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Yang, L.; Zhu, A.; Shao, J.; Chi, T. A Knowledge-Informed and Pareto-Based Artificial Bee Colony Optimization Algorithm for Multi-Objective Land-Use Allocation. ISPRS Int. J. Geo-Inf. 2018, 7, 63.

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