Multi-objective genetic algorithm optimization of artificial neural network for estimating suspended sediment yield in Mahanadi River basin, India

Genetic algorithm (GA) is improved with fast non-dominated sort approach and crowded comparison operator. A new algorithm called parallel multi-objective genetic algorithm (PMGA) is developed with the support of Massage Passing Interface (MPI). Then, PMGA is combined with Artificial Neural Network (ANN) to improve the optimization efficiency. Training samples of the ANN are evaluated based on the two-dimensional Navier-Stokes equation solver of cascade. To demonstrate the feasibility of the hybrid algorithm, an optimization of a controllable diffusion cascade is performed. The optimization results show that the present method is efficient and trustiness.

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Monthly Suspended Sediment Load Estimation using Artificial Neural Network and Traditional Models in Krishna River Basin, India

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b7743.019320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 613-618

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Suspended Sediment ◽

River Basin ◽

Sediment Yield ◽

Sediment Load ◽

Water Discharge ◽

Tectonic Activity ◽

Precipitation Seasonality ◽

Artificial Neural

The measurement of sediment yield is essential for getting the information of the mass balance between sea and land. It is difficult to directly measure the suspended sediment because it takes more time and money. One of the most common pollutants in the aquatic environment is suspended sediments. The sediment loads in rivers are controlled by variables like canal slope, basin volume, precipitation seasonality and tectonic activity. Water discharge and water level are the major controlling factor for estimate the sediment load in the Krishna River. Artificial neural network (ANN) is used for sediment yield modeling in the Krishna River basin, India. The comparative results show that the ANN is the easiest model for the suspended sediment yield estimates and provides a satisfactory prediction for very high, medium and low values. It is also noted that the Multiple Linear Regressions (MLR) model predicted an many number of negative sediment outputs at lower values. This is entirely unreality because the suspended sediment result can not be negative in nature. The ANN is provided better results than traditional models. The proposed ANN model will be helpful where the sediment measures are not available.

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Optimization of subcooled flow boiling in a vertical pipe by using artificial neural network and multi objective genetic algorithm

Applied Thermal Engineering ◽

10.1016/j.applthermaleng.2016.09.114 ◽

2017 ◽

Vol 111 ◽

pp. 1039-1051 ◽

Cited By ~ 9

Author(s):

Hasan Alimoradi ◽

Mehrzad Shams

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Flow Boiling ◽

Vertical Pipe ◽

Subcooled Flow Boiling ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Subcooled Flow ◽

Artificial Neural

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Twin Shaft-Geared Crankweb Crankshaft System with Optimization of Crankshaft Dimensions Using Integrated Artificial Neural Network-Multi Objective Genetic Algorithm

Periodica Polytechnica Transportation Engineering ◽

10.3311/pptr.10188 ◽

2017 ◽

Vol 47 (1) ◽

pp. 68-81

Author(s):

Anierudh Vishwanathan

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Journal Bearing ◽

Combustion Engine ◽

Design Parameters ◽

Data Set ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Artificial Neural

This paper suggests a novel design of a multi cylinder internal combustion engine crankshaft which will convert the unnecessary/extra torque provided by the engine into speed of the vehicle. Transmission gear design has been incorporated with crankshaft design to enable the vehicle attain same speed and torque at lower R.P.M resulting in improved fuel economy provided the operating power remains same. This paper also depicts the reduction in the fuel consumption of the engine due to the proposed design of the crankshaft system. In order to accommodate the wear and tear of the crankshaft due to the gearing action, design parameters like crankpin diameter, journal bearing diameter, crankpin fillet radii and journal bearing fillet radii have been optimized for output parameters like stress which has been calculated using finite element analysis with ANSYS Mechanical APDL and minimum volume using integrated Artificial Neural Network-Multi objective genetic algorithm. The data set for the optimization process has been generated using Latin Hypercube Sampling technique.

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