The extreme learning machine (ELM) problem can not quickly and accurately predict heat rate. Combined with swarm intelligence optimization algorithm

an ameliorated symbiotic organisms search algorithm and extreme learning machine (ASOS-ELM) comprehensive modeling method is proposed. This method uses the ameliorated symbiotic organisms search (ASOS) algorithm to optimize the parameters of the ELM hidden layer activation function to obtain the optimal ELM model. Firstly

the initial heat rate prediction model is established with ELM

and the root mean square error (RMSE) of the output heat rate is used as the fitness value of the algorithm. Then the appropriate ELM parameters are found through the ASOS algorithm to obtain an accurate heat rate prediction model. The performance of the heat rate prediction is compared with the traditional ELM model

support vector regression (SVR) model optimized by the ASOS algorithm

ELM optimized by improved particle swarm optimization (PSO) and basic symbiotic organisms search algorithm (SOS). The results show that the ASOS-ELM model has a precise forecasting ability and rapid convergence speed when dealing with complex data models

which provides a new idea for modeling the heat rate of a steam turbine.