The ORYZA (v3) rice model is the most exhaustive representation of the available knowledge on rice (Oryza sativa L.) growth and development and has become an integrated rice modelling tool suitable for different scenarios according to the demands of users. A 3‐yr field experiment was conducted under potential production conditions for single‐cropped rice during 2011–2013 in Jiangsu Province in eastern China. The simulation performance of ORYZA (v3) with respect to the growth and development of different rice varieties under different establishment methods was evaluated and validated. The adaptability and stability of the model and its effect on the production of the same or similar rice varieties in different districts and years were verified using calibrated parameters. The results revealed that ORYZA (v3) performed well in simulating the dynamics of crop biomass and leaf area index for both calibration and validation datasets in 2011–2013, as is reflected by the high adjusted linear correlation coefficient (r2), low normalized root mean square error (NRMSE) value, and lack of notable differences between simulated and observed data with 99% confidence. The NRMSE value of the final yield ranged from 4.30 to 8.99% for both Indica and Japonica rice throughout the growing seasons. The performance of transplanted Indica rice varieties was better than that of most Japonica rice varieties. Overall, our evaluation suggested the suitability of ORYZA (v3) for simulating rice growth and development of different single‐cropped varieties under different establishment techniques in eastern China.
Marine Predator Algorithm (MPA) is an optimization algorithm inspired by the behavior of predator and prey to catch their own food. MPA is simple and easy to implement. To further improve the performance of MPA, this paper proposes a Multigroup Marine Predator Algorithm (MGMPA). The multigroup mechanism is to divide the initial population into several independent groups. These groups generate the top predator and the Elite matrix based on different strategies and share information after a fixed iteration. Above strategies include the maximum of the same group, the average of the same group, the maximum of different groups and the average of different groups. To verify its performance, the paper compares MGMPA with some classic algorithms such as Particle Swarm Optimization (PSO), Parallel Particle Swarm Optimization (PPSO), Slap Swarm Algorithm (SSA), and Marine Predator Algorithm (MPA). In addition, the proposed MGMPA is also applied to solve Economic Load Dispatch problem (ELD). The experimental results show that the proposed MGMPA has significant advantages under the CEC2013 suite and obtains the minimum cost of power system operation and the maximum economic benefits in the application.
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