scholarly journals Using Parallel Genetic Algorithms for Estimating Model Parameters in Complex Reactive Transport Problems

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 640 ◽  
Author(s):  
Jagadish Torlapati ◽  
T. Prabhakar Clement
Keyword(s):  
Genetic Algorithm ◽  
Reactive Transport ◽  
Numerical Solutions ◽  
Optimization Method ◽  
Water Quality Modeling ◽  
Model Parameters ◽  
Initial Population ◽  
Desktop Computer ◽  
Linear Speedup ◽  
Transport Problems

In this study, we present the details of an optimization method for parameter estimation of one-dimensional groundwater reactive transport problems using a parallel genetic algorithm (PGA). The performance of the PGA was tested with two problems that had published analytical solutions and two problems with published numerical solutions. The optimization model was provided with the published experimental results and reasonable bounds for the unknown kinetic reaction parameters as inputs. Benchmarking results indicate that the PGA estimated parameters that are close to the published parameters and it also predicted the observed trends well for all four problems. Also, OpenMP FORTRAN parallel constructs were used to demonstrate the speedup of the code on an Intel quad-core desktop computer. The parallel code showed a linear speedup with an increasing number of processors. Furthermore, the performance of the underlying optimization algorithm was tested to evaluate its sensitivity to the various genetic algorithm (GA) parameters, including initial population size, number of generations, and parameter bounds. The PGA used in this study is generic and can be easily scaled to higher-order water quality modeling problems involving real-world applications.

scholarly journals Nonlinear Predictive Control for a Boiler–Turbine Unit Based on a Local Model Network and Immune Genetic Algorithm

2019 ◽  
Vol 11 (18) ◽  
pp. 5102
Author(s):  
Hongxia Zhu ◽  
Gang Zhao ◽  
Li Sun ◽  
Kwang Y. Lee
Keyword(s):  
Genetic Algorithm ◽  
Predictive Control ◽  
Turbine Unit ◽  
Optimization Method ◽  
System Stability ◽  
Local Model ◽  
Initial Population ◽  
Immune Genetic Algorithm ◽  
Model Network ◽  
Local Model Network

This paper proposes a nonlinear model predictive control (NMPC) strategy based on a local model network (LMN) and a heuristic optimization method to solve the control problem for a nonlinear boiler–turbine unit. First, the LMN model of the boiler–turbine unit is identified by using a data-driven modeling method and converted into a time-varying global predictor. Then, the nonlinear constrained optimization problem for the predictive control is solved online by a specially designed immune genetic algorithm (IGA), which calculates the optimal control law at each sampling instant. By introducing an adaptive terminal cost in the objective function and utilizing local fictitious controllers to improve the initial population of IGA, the proposed NMPC can guarantee the system stability while the computational complexity is reduced since a shorter prediction horizon can be adopted. The effectiveness of the proposed NMPC is validated by simulations on a 500 MW coal-fired boiler–turbine unit.

Optimal Sensor Placement Based on MAC and SPGA Algorithms

2012 ◽  
Vol 594-597 ◽  
pp. 1118-1122 ◽  
Author(s):  
Yong Ming Fu ◽  
Ling Yu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Manipulation ◽  
Optimization Method ◽  
Frame Structure ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Modal Assurance Criterion ◽  
Sensor Optimization ◽  
Computation Efficiency ◽  
The Individual

In order to solve the problem on sensor optimization placement in the structural health monitoring (SHM) field, a new sensor optimization method is proposed based on the modal assurance criterion (MAC) and the single parenthood genetic algorithm (SPGA). First, the required sensor numbers are obtained by using the step accumulating method. The SPGA is used to place sensors, in which the binary coding is adopted to realize the genetic manipulation through gene exchange, gene shift and gene inversion. Then, the method is further simplified and improved for higher computation efficiency. Where, neither the individual diversity of initial population nor the immature convergence problem is required. Finally, a numerical example of 61 truss frame structure is used to assess the robustness of the proposed method. The illustrated results show that the new method is better than the improved genetic algorithm and the step accumulating method in the search capacity, computational efficiency and reliability.

The Calibration on Nonlinear Single Return Period Storm Intensity Model Parameters by the New Blending Accelerating Genetic Algorithm

2012 ◽  
Vol 204-208 ◽  
pp. 3480-3487
Author(s):  
Bo Zhi Ren ◽  
Jing Cheng Lu ◽  
Hong Tao Zhou
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Return Period ◽  
Optimization Method ◽  
Model Parameters ◽  
Storm Intensity ◽  
Global Optimal ◽  
Accelerating Genetic Algorithm ◽  
Real Coded Genetic Algorithms ◽  
Intensity Model

Through improving the real-coded genetic algorithms and embedding the algorithm of accelerating the local search in the Powell direction and the operation of accelerating cycle, thereby it is constructed the New blend Accelerating Genetic Algorithm (hereinafter referred to as NBAGA). The applied examples of the calibration on nonlinear single return period storm intensity model parameters show that this method takes into account of the advantages of both the advantages of the improved real-coded genetic algorithms and the Powell Algorithm, therefore this method is an excellent nonlinear optimization method which can search the global optimal exact solutions quickly and in greater probability, as well as doing subtle search locally.

scholarly journals Inversion of marine controlled source electromagnetic data using a parallel non-dominated sorting genetic algorithm

2019 ◽  
Vol 220 (2) ◽  
pp. 1066-1077 ◽  
Author(s):  
Mohit Ayani ◽  
Lucy MacGregor ◽  
Subhashis Mallick
Keyword(s):  
Genetic Algorithm ◽  
Synthetic Data ◽  
Optimization Method ◽  
Model Parameters ◽  
Data Sets ◽  
Multi Objective Optimization ◽  
Controlled Source ◽  
Multi Objective ◽  
Electromagnetic Data ◽  
Deterministic Methods

SUMMARY We developed a multi-objective optimization method for inverting marine controlled source electromagnetic data using a fast-non-dominated sorting genetic algorithm. Deterministic methods for inverting electromagnetic data rely on selecting weighting parameters to balance the data misfit with the model roughness and result in a single solution which do not provide means to assess the non-uniqueness associated with the inversion. Here, we propose a robust stochastic global search method that considers the objective as a two-component vector and simultaneously minimizes both components: data misfit and model roughness. By providing an estimate of the entire set of the Pareto-optimal solutions, the method allows a better assessment of non-uniqueness than deterministic methods. Since the computational expense of the method increases as the number of objectives and model parameters increase, we parallelized our algorithm to speed up the forward modelling calculations. Applying our inversion to noisy synthetic data sets generated from horizontally stratified earth models for both isotropic and anisotropic assumptions and for different measurement configurations, we demonstrate the accuracy of our method. By comparing the results of our inversion with the regularized genetic algorithm, we also demonstrate the necessity of casting this problem as a multi-objective optimization for a better assessment of uncertainty as compared to a scalar objective optimization method.

Application of Genetic Algorithm on Travelling Salesman Person

2014 ◽  
Vol 1048 ◽  
pp. 526-530
Author(s):  
Sambourou Massinanke ◽  
Chao Zhu Zhang
Keyword(s):  
Genetic Algorithm ◽  
Travelling Salesman Problem ◽  
Optimization Method ◽  
Initial Population ◽  
Travelling Salesman ◽  
Minimum Path ◽  
Minimum Number ◽  
Permutation Problem ◽  
The City ◽  
Survival Of The Fittest

GA (Genetic algorithm) is an optimization method based on operators (mutation and crossover) utilizing a survival of the fittest idea. They are utilized favorably in various problems. (TSP) Travelling salesman problem is one of the famous studied. TSP is a permutation problem in which the aim is to determine the shortest tour between n different points (cities), otherwise, the problem aims to find a route covering all cities where that the total distance is minimal. In this study a single salesman travels to each of the cities and close the loop by returning to the city he started, the aim of this study is to determine the minimum number of generations in which salesman does the minimum path, cities are chosen at random as initial population. The new generations are then created iteratively till the proper path is attained.

Network Coding Optimization Method Research Based on Genetic Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 2059-2062
Author(s):  
Hong Yan Yan
Keyword(s):  
Genetic Algorithm ◽  
Network Coding ◽  
Optimization Method ◽  
Premature Convergence ◽  
Initial Population ◽  
Running Time ◽  
Coding Scheme ◽  
Algorithm Search

Network coding optimization method research based on genetic algorithm applies network coding technology in monophyletic multicast network. After reaching network multicast rate, find link coding scheme which makes the minimum of the total number of network coding. Moreover, it makes the analysis and improvement for general genetic algorithm’s defects in network coding link optimization such as rare individual successful decoded by randomly generated initial population strategies, reduced algorithm search ability, premature convergence of genetic algorithm and long algorithm running time.

scholarly journals Optimalisasi Distribusi Harga Tiket Pesawat berdasarkan Kepadatan Rute Menggunakan Algoritma Genetika

2019 ◽  
Vol 5 (2) ◽  
Author(s):  
Sri Hutamy Novianti ◽  
Esmeralda C. Djamal ◽  
Agus Komarudin
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Method ◽  
Aviation Industry ◽  
Initial Population ◽  
Insertion Technique ◽  
The Past ◽  
The Difference ◽  
Ticket Prices ◽  
Compatibility Function

The development of the aviation industry in Indonesia in the past decade has risen sharply. One of the impacts of the development of the aviation industry was the presence of a multilevel tariff concept. Where, the concept is the variation in ticket prices in one class with slightly different facilities such as the difference in penalty fees for making refunds and rebooking. The concept of multilevel rates is usually referred to as sub-class rates. One application of the sub-class tariffs in economic classes is divided into four types of sub-classes special promo sub-classes, promo sub-classes, then affordable sub-class and flexible sub-class. One optimization method of getting a combination that meets the requirements without having to try all possibilities is the Genetic Algorithm. The chromosomes built represent 10 subclasses on 9 routes so that they have 90 genes. The use of genetic algorithms originated from the generation of an initial population of 8 chromosomes with a length of 90 genes performed randomly, evaluation of the compatibility function was then selected using the Rank based fitness technique, crosses using Multi-Point Crossover, mutations with the Mutation Insertion technique. The system built was tested with two conditions each of eight tests with 100 generations. First, the test uses the mutation method of three subclass codes on four routes at a capacity of 150 seats, obtained the largest match value of Rp. 750,752,200 and the smallest Rp. 662,283,100. And testing with the mutation method of three subclass codes on eight routes of 150 seat capacity obtained the largest match value of Rp. 763,265,300 and the smallest Rp. 547,396,200. The results of testing the mutation method on eight routes resulted in a higher match value compared to the mutation method on four routes. The system has been implemented in software so that it can provide recommendations on the number of ticket passes distributed in the economic subclass.

scholarly journals Optimal Placement and Sizing of D-STATCOM in Radial and Meshed Distribution Networks Using a Discrete-Continuous Version of the Genetic Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1452
Author(s):  
Cristian Mateo Castiblanco-Pérez ◽  
David Esteban Toro-Rodríguez ◽  
Oscar Danilo Montoya ◽  
Diego Armando Giral-Ramírez
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Programming Model ◽  
Distribution Networks ◽  
Optimization Method ◽  
Optimal Placement ◽  
Mixed Integer ◽  
Power Balance ◽  
Continuous Version ◽  
Discrete Part

In this paper, we propose a new discrete-continuous codification of the Chu–Beasley genetic algorithm to address the optimal placement and sizing problem of the distribution static compensators (D-STATCOM) in electrical distribution grids. The discrete part of the codification determines the nodes where D-STATCOM will be installed. The continuous part of the codification regulates their sizes. The objective function considered in this study is the minimization of the annual operative costs regarding energy losses and installation investments in D-STATCOM. This objective function is subject to the classical power balance constraints and devices’ capabilities. The proposed discrete-continuous version of the genetic algorithm solves the mixed-integer non-linear programming model that the classical power balance generates. Numerical validations in the 33 test feeder with radial and meshed configurations show that the proposed approach effectively minimizes the annual operating costs of the grid. In addition, the GAMS software compares the results of the proposed optimization method, which allows demonstrating its efficiency and robustness.

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