Table of Contents
Developing hybrid intelligent location optimisers for spatial modelling in GIS
Contents
Background
Location optimisation
Cont’d
BUT !
What’s wrong with what exists already?
Objectives
Algorithm development
The Structure of a Genetic Algorithm
The structure of a Simulated Annealing Algorithm
Benchmark test
Rosing data set
PPT Slide
Algorithm building process 1 - Traditional optimisation approach -
Algorithm building process 2 - Hybrid intelligent optimisation techniques -
Optimal centres location map
Optimal solution for various p-median problems
PPT Slide
Summary of test 1 (Rosing data)
PPT Slide
Optimal solutions and computational performances
50 centres
PPT Slide
Summary of test 2 (Leeds-Bradford ED 2315 points)
Cont’d
Cont’d
Conclusions
Further research works
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Authors: Young Hoon Kim and Stan Openshaw
Email:
pgky@geog.leeds.ac.uk
stan@geog.leeds.ac.uk
Home Page:
http://www.geog.leeds.ac.uk/pgrads/y.kim/
http://www.geog.leeds.ac.uk/staff/s.openshaw/
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