Abstract
In this paper, a new hybrid algorithm is introduced based on advantage of two evolutionary algorithms, Particle Swarm Optimization (PSO) and genetic Algorithms (GA) known as PSOGA. The proposed algorithm was utilized in optimize design of ANFIS network (ANFIS-PSOGA). The ANFIS-PSOGA was employed to predict limiting velocity in sewer pipe to prevent sediment deposition. Firstly, the effective dimensionless variables were provided. Then, minimum velocity parameter was presented as densimetric Froude number (Fr) was predicted using ANFIS-PSOGA. The results of proposed hybrid method is evaluated using different statistical indices (R2 = 0.98; MAPE = 3.62; RMSE = 0.23; RRMSE = 0.05). The performance of new hybrid algorithm (PSOGA) is compared with GA, PSO and a hybrid algorithm (i.e. a combination of back-propagation and least-square (BPLS). The results show that the presented hybrid algorithm in optimize design of ANFIS (PSOGA) has better accuracy than algorithms.
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Gharabaghi, B., Bonakdari, H., Ebtehaj, I. (2019). Hybrid Evolutionary Algorithm Based on PSOGA for ANFIS Designing in Prediction of No-Deposition Bed Load Sediment Transport in Sewer Pipe. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2018. Advances in Intelligent Systems and Computing, vol 857. Springer, Cham. https://doi.org/10.1007/978-3-030-01177-2_8
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