Optimal Design of Open Channel Sections Using PSO Algorithm
Authors
Abstract:
This paper applies an evolutionary algorithm, the particle swarm optimization (PSO), to design the optimum open channel section. Depth, channel side slope and bottom width are considered as the variables for rectangular, triangular and trapezoidal channels, respectively. The objective function is minimizing the construction cost of the channel section. MATLAB software is used for programming and doing the optimization process. Manning’s uniform flow formula has been used as a constraint for the optimization model. The cost function is included the cost of earthwork, the increment in the cost of earthwork with the depth below the ground surface and the cost of lining. Simple functions of unit cost terms have been used to express the optimum values of section variables. The optimum section variables are obtained for the case of minimum area or minimum wetted perimeter problems. The results of this study showed that the PSO is a robust algorithm to compute the optimum section variables in open channel design.
similar resources
Optimal Rule Set Generation Using Pso Algorithm
Classification and Prediction is an important research area of data mining. Construction of classifier model for any decision system is an important job for many data mining applications. The objective of developing such a classifier is to classify unlabeled dataset into classes. Here we have applied a discrete Particle Swarm Optimization (PSO) algorithm for selecting optimal classification rul...
full textAerodynamic Optimal Design of Wind Turbine Blades using Genetic Algorithm
Wind power has been widely considered and utilized in recent years as one of the most promising renewable energy sources. In the current research study, aerodynamic analysis of the upwind three-bladed horizontal axis turbine is carried out using blade element momentum theory (BEM), and a genetic algorithm (GA) is applied as an optimization method. Output power generation is considered as an obj...
full textOPTIMAL DESIGN OF CANTILEVER RETAINING WALL USING DIFFERENTIAL EVOLUTION ALGORITHM
Optimal design of cantilever reinforced concrete retaining wall can lead considerable cost saving if its involvement in hill road formation and railway line formation is significant. A study of weight reduction optimization of reinforced cantilever retaining wall subjected to a sloped backfill using Differential Evolution Algorithm (DEA) is carried out in the present research. The r...
full textOPTIMAL DESIGN OF TUNNEL SUPPORT LINING USING MCBO ALGORITHM
In this paper, a systematic approach is presented for optimal design of tunnel support lining using two-dimensional finite element analysis models of soil-structure interaction developed in ABAQUS software and the Modified Colliding Bodies Optimization (MCBO) algorithm implemented in MATLAB environment. This approach is then employed to study the influence of variable geometrical and geo-mechan...
full textOPTIMAL DESIGN OF GRAVITY DAM USING DIFFERENTIAL EVOLUTION ALGORITHM
The shape optimization of gravity dam is posed as an optimization problem with goals of minimum value of concrete, stresses and maximum safety against overturning and sliding need to be achieved. Optimally designed structure generally saves large investments especially for a large structure. The size of hydraulic structures is usually huge and thus requires a huge investment. If the optimizatio...
full textAerodynamic optimal design of wind turbine blades using genetic algorithm
Wind power has been widely considered and utilized in recent years as one of the most promising renewable energy sources. In the current research study, aerodynamic analysis of the upwind three-bladed horizontal axis turbine is carried out using blade element momentum theory (BEM), and a genetic algorithm (GA) is applied as an optimization method. Output power generation is considered as an...
full textMy Resources
Journal title
volume 11 issue 2
pages 0- 0
publication date 2019-12-01
By following a journal you will be notified via email when a new issue of this journal is published.
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023