Abstract
This chapter provides an overview of GIS-based heuristic methods for tackling spatial multiobjective decision problems. The methods are classified into two groups. First, there is a group of basic heuristic methods that tend to be designed for solving specific spatial problems. This group includes methods such as: site suitability heuristics, site location heuristics, and greedy algorithms. Second, there is a large collection of meta-heuristics. These approaches typically employ conventional meta-heuristics for solving spatial optimization problems using GIS. This group of methods include: genetic algorithms, simulated annealing, tabu search, and swarm intelligence methods. This chapter focusses on the concepts and procedures of genetic algorithms.
Keywords
- Genetic Algorithm
- Particle Swarm Optimization
- Tabu Search
- Greedy Randomize Adaptive Search Procedure
- Multiobjective Optimization Problem
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Malczewski, J., Rinner, C. (2015). Heuristic Methods. In: Multicriteria Decision Analysis in Geographic Information Science. Advances in Geographic Information Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74757-4_6
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