Evolutionary computation approach for spatial workload balancing

Ahmed Abubahia*, Mohamed Bader-El-Den, Ella Haig

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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The growing demand for Geographic Information Systems (GIS) calls for high computation reliability to handle vast and complex spatial data processing tasks. A better parallel computing scheme should ensure balanced workload at different data processors to ensure optimal use of computing resources and minimise execution times, which poses more challenges with spatial data due to the nature of having spatial correlations and uneven distributions. In this paper, we propose a spatial clustering approach for workload balance, by using an evolutionary computation method that considers the nature of spatial data, to increase the computation performance for processing GIS polygon-based maps
with massive number of vertices and complex shapes. To evaluate our proposed approach, We proposed two different experimental approaches for comparing our results: (i) Non{merging based experiment, and (ii) merging based experiment. The results demonstrated the advantage of the proposed spatial clustering approach in real GIS map based partitioning scenarios. The advantages and limitations of the proposed approach are discussed and further research directions are highlighted toward a development work by the research community.
Original languageEnglish
Title of host publicationIntelligent Computing
Subtitle of host publicationProceedings of the 2021 Computing Conference
EditorsKohei Arai
Number of pages19
ISBN (Electronic)9783030801267
ISBN (Print)9783030801250
Publication statusPublished - 7 Jul 2021
EventComputing Conference 2021 - London, United Kingdom
Duration: 15 Jul 202116 Jul 2021

Publication series

NameLecture Notes in Networks and Systems
ISSN (Print)2367-3370


ConferenceComputing Conference 2021
Country/TerritoryUnited Kingdom
Internet address


  • computational optimisation
  • geographic information systems
  • spatial data
  • workload balancing
  • evolutionary computation


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