Correlation of Activity Coefficients in Aqueous Solutions of Ammonium Salts Using Local Composition Models and Stochastic Optimization Methods

2008 ◽  
Vol 3 (1) ◽  
Author(s):  
José Enrique Jaime-Leal ◽  
Adrián Bonilla-Petriciolet
Keyword(s):  
Simulated Annealing ◽  
Stochastic Optimization ◽  
Activity Coefficients ◽  
Optimization Problem ◽  
Optimization Methods ◽  
Data Fitting ◽  
Pattern Search ◽  
Ammonium Salts ◽  
Aqueous Electrolytes ◽  
Local Composition

In this study, several local composition models and stochastic optimization methods have been used and compared in data fitting of activity coefficients in aqueous electrolytes. We have utilized the electrolyte-NRTL model of Chen et al. and the modified Wilson models proposed by Xu and Macedo, and Zhao et al. to fit the activity coefficients of several quaternary ammonium salts in water at 25°C. These electrolytes have interesting properties for their application as ionic liquids. However, the modeling of their thermodynamic behavior using local composition models is a global optimization problem. In this study, several stochastic optimization methods have been used to solve this optimization problem and their numerical performances have been compared. Specifically, we have tested the classical Simulated Annealing and the hybrid methods: Direct Search Simulated Annealing, Simplex Coding Genetic Algorithm, Simulated Annealing Heuristic Pattern Search, and Directed Tabu Search. Our results show that Simulated Annealing is a suitable tool for data fitting of the activity coefficients of aqueous electrolytes. Finally, the tested models can satisfactorily correlate the mean activity coefficients of electrolytes treated in this study, and are suitable for process design.

scholarly journals Optimizing Virtual Private Network Design Using a New Heuristic Optimization Method

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Hongbing Lian ◽  
András Faragó
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Design Optimization ◽  
Optimization Problem ◽  
Optimization Methods ◽  
Optimization Method ◽  
Virtual Private Network ◽  
Successful Model ◽  
Search Speed ◽  
Private Network

In virtual private network (VPN) design, the goal is to implement a logical overlay network on top of a given physical network. We model the traffic loss caused by blocking not only on isolated links, but also at the network level. A successful model that captures the considered network level phenomenon is the well-known reduced load approximation. We consider here the optimization problem of maximizing the carried traffic in the VPN. This is a hard optimization problem. To deal with it, we introduce a heuristic local search technique called landscape smoothing search (LSS). This study first describes the LSS heuristic. Then we introduce an improved version called fast landscape smoothing search (FLSS) method to overcome the slow search speed when the objective function calculation is very time consuming. We apply FLSS to VPN design optimization and compare with well-known optimization methods such as simulated annealing (SA) and genetic algorithm (GA). The FLSS achieves better results for this VPN design optimization problem than simulated annealing and genetic algorithm.

A simulated annealing approach for curve fitting in automated manufacturing systems

2007 ◽  
Vol 18 (2) ◽  
pp. 202-216 ◽  
Author(s):  
Hsien‐Yu Tseng ◽  
Chang‐Ching Lin
Keyword(s):  
Simulated Annealing ◽  
Curve Fitting ◽  
Manufacturing Systems ◽  
Optimization Problem ◽  
Simulated Annealing Algorithm ◽  
Optimization Method ◽  
Pattern Search ◽  
Automated Manufacturing ◽  
Automated Manufacturing Systems ◽  
Content Type

PurposeThis research aims to develop an effective and efficient algorithm for solving the curve fitting problem arising in automated manufacturing systems.Design/methodology/approachThis paper takes curve fitting as an optimization problem of a set of data points. Expressing the data as a function will be very effective to the data analysis and application. This paper will develop the stochastic optimization method to apply to curve fitting. The proposed method is a combination optimization method based on pattern search (PS) and simulated annealing algorithm (SA).FindingsThe proposed method is used to solve a nonlinear optimization problem and then to implement it to solve three circular arc‐fitting problems of curve fitting. Based on the analysis performed in the experimental study, the proposed algorithm has been found to be suitable for curve fitting.Practical implicationsCurve fitting is one of the basic form errors encountered in circular features. The proposed algorithm is tested and implemented by using nonlinear problem and circular data to determine the circular parameters.Originality/valueThe developed machine vision‐based approach can be an online tool for measurement of circular components in automated manufacturing systems.

scholarly journals A simulated annealing-based algorithm for selecting balanced samples

2021 ◽  
Author(s):  
Roberto Benedetti ◽  
Maria Michela Dickson ◽  
Giuseppe Espa ◽  
Francesco Pantalone ◽  
Federica Piersimoni
Keyword(s):  
Simulated Annealing ◽  
Optimization Problem ◽  
Sample Selection ◽  
Auxiliary Information ◽  
Real Data ◽  
Simulation Experiments ◽  
Balanced Sampling ◽  
Inclusion Probabilities ◽  
Random Method ◽  
Annealing Algorithms

AbstractBalanced sampling is a random method for sample selection, the use of which is preferable when auxiliary information is available for all units of a population. However, implementing balanced sampling can be a challenging task, and this is due in part to the computational efforts required and the necessity to respect balancing constraints and inclusion probabilities. In the present paper, a new algorithm for selecting balanced samples is proposed. This method is inspired by simulated annealing algorithms, as a balanced sample selection can be interpreted as an optimization problem. A set of simulation experiments and an example using real data shows the efficiency and the accuracy of the proposed algorithm.

scholarly journals Adaptive Gene Level Mutation

Algorithms ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 16
Author(s):  
Jalal Al-afandi ◽  
Horváth András
Keyword(s):  
Genetic Algorithms ◽  
Stochastic Optimization ◽  
Specific Problem ◽  
Traditional Approach ◽  
Fitness Function ◽  
Optimization Methods ◽  
Analytic Solutions ◽  
Problem Representation ◽  
Travelling Salesman ◽  
Gene Level

Genetic Algorithms are stochastic optimization methods where solution candidates, complying to a specific problem representation, are evaluated according to a predefined fitness function. These approaches can provide solutions in various tasks even, where analytic solutions can not be or are too complex to be computed. In this paper we will show, how certain set of problems are partially solvable allowing us to grade segments of a solution individually, which results local and individual tuning of mutation parameters for genes. We will demonstrate the efficiency of our method on the N-Queens and travelling salesman problems where we can demonstrate that our approach always results faster convergence and in most cases a lower error than the traditional approach.

Optimization Design of Trusses Based on Covariance Matrix Adaptation Evolution Strategy Algorithm

2012 ◽  
Vol 215-216 ◽  
pp. 133-137
Author(s):  
Guo Shao Su ◽  
Yan Zhang ◽  
Zhen Xing Wu ◽  
Liu Bin Yan
Keyword(s):  
Stochastic Optimization ◽  
Covariance Matrix ◽  
Optimization Problems ◽  
Fitness Function ◽  
Optimization Methods ◽  
Evolution Strategy ◽  
Covariance Matrix Adaptation ◽  
Study Results ◽  
Highly Nonlinear ◽  
Adaptation Evolution

Covariance matrix adaptation evolution strategy algorithm (CMA-ES) is a newly evolution algorithm. It has become a powerful tool for solving highly nonlinear multi-peak optimization problems. In many real-world optimization problems, the location of multiple optima is often required in a search space. In order to evaluate the solution, thousands of fitness function evaluations are involved that is a time consuming or expensive processes. Therefore, conventional stochastic optimization methods meet a special challenge for a very large number of problem function evaluations. Aiming to overcome the shortcoming of stochastic optimization methods in the high calculation cost, a truss optimal method based on CMA-ES algorithm is proposed and applied to solve the section and shape optimization problems of trusses. The study results show that the method is feasible and has the advantages of high accuracy, high efficiency and easy implementation.

Trajectory Optimization Applied to Space Rendezvous

2013 ◽  
Vol 756-759 ◽  
pp. 3466-3470
Author(s):  
Xu Min Song ◽  
Qi Lin
Keyword(s):  
Simulated Annealing ◽  
Optimization Model ◽  
Trajectory Optimization ◽  
Optimization Problem ◽  
Optimization Method ◽  
Nonlinear Constraints ◽  
Design Variables ◽  
Adaptive Simulated Annealing ◽  
Simulation Results

The trajcetory plan problem of spece reandezvous mission was studied in this paper using nolinear optimization method. The optimization model was built based on the Hills equations. And by analysis property of the design variables, a transform was put forward , which eliminated the equation and nonlinear constraints as well as decreaseing the problem dimensions. The optimization problem was solved using Adaptive Simulated Annealing (ASA) method, and the rendezvous trajectory was designed.The method was validated by simulation results.

Solving Mono- and Multi-Objective Problems Using Hybrid Evolutionary Algorithms and Nelder-Mead Method

2021 ◽  
Vol 12 (4) ◽  
pp. 98-116
Author(s):  
Noureddine Boukhari ◽  
Fatima Debbat ◽  
Nicolas Monmarché ◽  
Mohamed Slimane
Keyword(s):  
Convergence Rate ◽  
Optimization Problem ◽  
Optimization Problems ◽  
State Of The Art ◽  
Hybrid Approach ◽  
Optimization Methods ◽  
Global Optimum ◽  
Stochastic Methods ◽  
Local Optima ◽  
Portfolio Optimization Problem

Evolution strategies (ES) are a family of strong stochastic methods for global optimization and have proved their capability in avoiding local optima more than other optimization methods. Many researchers have investigated different versions of the original evolution strategy with good results in a variety of optimization problems. However, the convergence rate of the algorithm to the global optimum stays asymptotic. In order to accelerate the convergence rate, a hybrid approach is proposed using the nonlinear simplex method (Nelder-Mead) and an adaptive scheme to control the local search application, and the authors demonstrate that such combination yields significantly better convergence. The new proposed method has been tested on 15 complex benchmark functions and applied to the bi-objective portfolio optimization problem and compared with other state-of-the-art techniques. Experimental results show that the performance is improved by this hybridization in terms of solution eminence and strong convergence.

Design Optimization of an L-Shaped Extrusion Die

2009 ◽  
Author(s):  
Oktay Yilmaz ◽  
Hasan Gunes ◽  
Kadir Kirkkopru
Keyword(s):  
Simulated Annealing ◽  
Optimization Problem ◽  
Numerical Solutions ◽  
Latin Hypercube Sampling ◽  
Kriging Model ◽  
Narrow Channel ◽  
Geometric Parameters ◽  
Die Design ◽  
Dimensional Parameter ◽  
Large Channel

It is an important problem in the polymer extrusion of complex profiles to balance the flow at the die exit. In this paper, we employ simulated annealing-kriging meta-algorithm to optimize the geometric parameters of a die channel to obtain a uniform exit velocity distribution. Design variables for our optimization problem involve the suitable geometric parameters for the die design, which are the thickness of the large channel and the length of the narrow channel. Die balance is based on the deviation of the velocity with respect to the average velocity at the die exit. So the cost function for the optimization problem involves the minimization of this deviation. For the design of numerical experiments, we use Latin Hypercube Sampling (LHS) to construct the kriging model. Then, based on the LHS points, the numerical solutions are performed using Polyflow, a commercial software based on the finite element method and is specifically designed to simulate the flow and heat transfer of non-newtonian, viscoelastic fluids. In our simulations, a HDPE (high density polyethylene) is used as extrusion material. Having obtained numerical simulations for N = 60 LHS points in two-dimensional parameter space (t and L), the optimization of these parameters is carried out by Simulated Annealing (SA) method in conjunction with kriging model. We show that kriging model employed in SA algorithm can be used to optimize the die geometry.

scholarly journals Optimal design of a DC MHD pump by simulated annealing method

2014 ◽  
Vol 11 (2) ◽  
pp. 339-350
Author(s):  
Khadidja Bouali ◽  
Fatima Kadid ◽  
Rachid Abdessemed
Keyword(s):  
Simulated Annealing ◽  
Optimal Design ◽  
Objective Function ◽  
Design Problem ◽  
Design Methodology ◽  
Optimization Problem ◽  
Optimization Procedure ◽  
Simulated Annealing Method ◽  
Direct Interpretation ◽  
Annealing Method

In this paper a design methodology of a magnetohydrodynamic pump is proposed. The methodology is based on direct interpretation of the design problem as an optimization problem. The simulated annealing method is used for an optimal design of a DC MHD pump. The optimization procedure uses an objective function which can be the minimum of the mass. The constraints are both of geometrics and electromagnetic in type. The obtained results are reported.

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