Dynamic island model based on spectral clustering in genetic algorithm

Qinxue Meng, Jia Wu, John Ellis, Paul J. Kennedy

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

12 Citations (Scopus)


How to maintain relative high diversity is important to avoid premature convergence in population-based optimization methods. Island model is widely considered as a major approach to achieve this because of its flexibility and high efficiency. The model maintains a group of sub-populations on different islands and allows sub-populations to interact with each other via predefined migration policies. However, current island model has some drawbacks. One is that after a certain number of generations, different islands may retain quite similar, converged sub-populations thereby losing diversity and decreasing efficiency. Another drawback is that determining the number of islands to maintain is also very challenging. Meanwhile initializing many sub-populations increases the randomness of island model. To address these issues, we proposed a dynamic island model (DIM-SP) which can force each island to maintain different sub-populations, control the number of islands dynamically and starts with one sub-population. The proposed island model outperforms the other three state-of-the-art island models in three baseline optimization problems including job shop scheduler, travelling salesmen, and quadratic multiple knapsack.

Original languageEnglish
Title of host publicationIJCNN 2017
Subtitle of host publicationProceedings of the 2017 International Joint Conference on Neural Networks
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Electronic)9781509061822, 9781509061815
ISBN (Print)9781509061839
Publication statusPublished - 30 Jun 2017
Externally publishedYes
Event2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States
Duration: 14 May 201719 May 2017


Conference2017 International Joint Conference on Neural Networks, IJCNN 2017
Country/TerritoryUnited States


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