To enhance the global search ability of population based incremental learning (PBIL) methods, it is proposed that multiple probability vectors are to be included on available PBIL algorithms. The strategy for updating those probability vectors and the negative learning and mutation operators are thus re-defined correspondingly. Moreover, to strike the best tradeoff between exploration and exploitation searches, an adaptive updating strategy for the learning rate is designed. Numerical examples are reported to demonstrate the pros and cons of the newly implemented algorithm.
- Genetic algorithm (GA)
- Global optimization
- Inverse problem
- Population based incremental learning (PBIL) method
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Physics and Astronomy (miscellaneous)