The new AGA was applied to optimize the first cycle pattern for the core of a pressurized water reactor (PWR) with an electrical power rating of 1000MW. The results showed that the proposed AGA effectively improved the convergence rate of GAs in the LPO. The maximum radial power peaking factor (RPPF) of the obtained loading pattern was decreased compared to the reference loading pattern. The results were published in the SCI International Journal "Annals of Nuclear Energy".
Key takeaways:
- A new adaptive genetic algorithm (AGA) has been proposed for the loading pattern optimization of Pressurized Water Reactor (PWR), aiming to improve the convergence rate and local search capability of conventional genetic algorithms (GAs).
- The AGA adjusts the crossover and mutation probabilities based on the fitness of the individual, which can effectively improve the convergence. However, the original AGA can easily lead to premature convergence.
- The proposed AGA was applied to optimize the first cycle pattern for the core of PWR with an electrical power rating of 1000MW. The results showed that the proposed AGA is effective in improving the convergence rate of GAs in the loading pattern optimization (LPO).
- The research was published in the SCI International Journal "Annals of Nuclear Energy" under the title "PWR core loading pattern optimization with adaptive genetic algorithm".