학술논문

Dynamic island model based on spectral clustering in genetic algorithm
Document Type
Conference
Source
2017 International Joint Conference on Neural Networks (IJCNN) Neural Networks (IJCNN), 2017 International Joint Conference on. :1724-1731 May, 2017
Subject
Bioengineering
Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Robotics and Control Systems
Signal Processing and Analysis
Sociology
Statistics
Optimization
Diversity reception
Computational modeling
Genetic algorithms
Evolutionary computation
Language
ISSN
2161-4407
Abstract
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.