Dynamic Island Model based on Spectral Clustering in Genetic Algorithm

TL;DR

This paper introduces a dynamic island model based on spectral clustering for genetic algorithms, which adaptively maintains diverse sub-populations, controls the number of islands, and improves optimization performance.

Contribution

It proposes a novel dynamic island model that addresses diversity loss and island number determination issues in genetic algorithms.

Findings

Outperforms state-of-the-art island models in benchmark problems

Maintains higher diversity throughout the evolution process

Reduces the need for predefined island numbers

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 problem, travelling salesmen problem and quadratic multiple knapsack problem.