Abstract
Micro differential evolution (mDE) refers to algorithms that evolve with a small population to search for good solutions. Although mDEs are very useful for resource-constrained optimization tasks, the research on mDEs is still limited. In this paper, we propose a new mDE, i.e., vectorized bimodal distribution based mDE (called VB-mDE). The main idea is to employ a vectorized bimodal distri- bution parameter adjustment mechanism in mDE for performance enhancement. Specifically, in the VB-mDE, two important control parameters, i.e., scale factor F and crossover rate CR, are adjusted by bimodal Cauchy distribution. At the same time, to increase the population diversity, the scale factor F is vectorized. The pro- posed VB-mDE is evaluated on the CEC2014 benchmark functions and compared with the state-of-the-art mDEs and normal DEs. The results show that the proposed VB-mDE has advantages in terms of solution accuracy and convergence speed.
Original language | English |
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Pages (from-to) | 245-261 |
Number of pages | 17 |
Journal | Multiagent and Grid Systems |
Volume | 16 |
Issue number | 3 |
DOIs | |
Publication status | Published - 30 Oct 2020 |
Keywords
- micro differential evolution
- bimodal distribution
- vectorized bimodal Cauchy distribution
- parameter adjustment mechanism
- small population