scholarly journals Chaotic Multiquenching Annealing Applied to the Protein Folding Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Juan Frausto-Solis ◽  
Ernesto Liñan-García ◽  
Mishael Sánchez-Pérez ◽  
Juan Paulo Sánchez-Hernández
Keyword(s):  
Protein Folding ◽  
Simulated Annealing ◽  
Least Squares ◽  
Simulated Annealing Algorithm ◽  
Least Squares Method ◽  
Equilibrium Phase ◽  
Dynamical Equilibrium ◽  
Annealing Algorithm ◽  
Cooling Function ◽  
Very High

The Chaotic Multiquenching Annealing algorithm (CMQA) is proposed. CMQA is a new algorithm, which is applied to protein folding problem (PFP). This algorithm is divided into three phases: (i) multiquenching phase (MQP), (ii) annealing phase (AP), and (iii) dynamical equilibrium phase (DEP). MQP enforces several stages of quick quenching processes that include chaotic functions. The chaotic functions can increase the exploration potential of solutions space of PFP. AP phase implements a simulated annealing algorithm (SA) with an exponential cooling function. MQP and AP are delimited by different ranges of temperatures; MQP is applied for a range of temperatures which goes from extremely high values to very high values; AP searches for solutions in a range of temperatures from high values to extremely low values. DEP phase finds the equilibrium in a dynamic way by applying least squares method. CMQA is tested with several instances of PFP.

scholarly journals Multiphase Simulated Annealing Based on Boltzmann and Bose-Einstein Distribution Applied to Protein Folding Problem

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Juan Frausto-Solis ◽  
Ernesto Liñán-García ◽  
Juan Paulo Sánchez-Hernández ◽  
J. Javier González-Barbosa ◽  
Carlos González-Flores ◽  
...  
Keyword(s):  
Protein Folding ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Least Squares Method ◽  
Equilibrium Phase ◽  
Boltzmann Distribution ◽  
New Approach ◽  
Einstein Distribution ◽  
Problem Instances ◽  
Bose Einstein

A new hybrid Multiphase Simulated Annealing Algorithm using Boltzmann and Bose-Einstein distributions (MPSABBE) is proposed. MPSABBE was designed for solving the Protein Folding Problem (PFP) instances. This new approach has four phases: (i) Multiquenching Phase (MQP), (ii) Boltzmann Annealing Phase (BAP), (iii) Bose-Einstein Annealing Phase (BEAP), and (iv) Dynamical Equilibrium Phase (DEP). BAP and BEAP are simulated annealing searching procedures based on Boltzmann and Bose-Einstein distributions, respectively. DEP is also a simulated annealing search procedure, which is applied at the final temperature of the fourth phase, which can be seen as a second Bose-Einstein phase. MQP is a search process that ranges from extremely high to high temperatures, applying a very fast cooling process, and is not very restrictive to accept new solutions. However, BAP and BEAP range from high to low and from low to very low temperatures, respectively. They are more restrictive for accepting new solutions. DEP uses a particular heuristic to detect the stochastic equilibrium by applying a least squares method during its execution. MPSABBE parameters are tuned with an analytical method, which considers the maximal and minimal deterioration of problem instances. MPSABBE was tested with several instances of PFP, showing that the use of both distributions is better than using only the Boltzmann distribution on the classical SA.

Optimization of Financial Forecasting Based on Least Squares Support Vector Machine

2013 ◽  
Vol 760-762 ◽  
pp. 1987-1991
Author(s):  
Yun Fa Li
Keyword(s):  
Support Vector Machine ◽  
Simulated Annealing ◽  
Stock Market ◽  
Prediction Model ◽  
Least Squares ◽  
Simulated Annealing Algorithm ◽  
Least Square ◽  
Support Vector ◽  
Stock Investment ◽  
Annealing Algorithm

To master the variation regularity of finance, obtain greater benefits in stock investment. study of the support vector machine and application in prediction of stock market. The simulated annealing algorithm to optimize the least squares support vector machine prediction model, and the least square support vector machine and simulated annealing algorithm is described, given the optimal prediction model. Through the research on the simulation of the Hang Seng Index, shows that this method is simple, fast convergence, the algorithm with high accuracy. Has the actual guiding sense for investors, the stock market of the financial firm to operate.

The Simulated Annealing Algorithm Based on Multi-Populations Application of TSP

2013 ◽  
Vol 457-458 ◽  
pp. 1037-1041
Author(s):  
Qin Hui Gong
Keyword(s):  
Simulated Annealing ◽  
Traveling Salesman Problem ◽  
Simulated Annealing Algorithm ◽  
Traveling Salesman ◽  
Local Minima ◽  
Multiple Populations ◽  
Annealing Algorithm ◽  
Np Problem ◽  
Very High ◽  
Collateral Mechanism

Traveling salesman problem (TSP) is not only a combinatorial optimization problem but also a classical NP problem, which has has high application value. Simulated annealing algorithm is especially effective for solving TSP problems. Based on the deficiency of simulated annealing algorithm on avoiding local minima, this paper has improved the traditional simulated annealing algorithm, proposed simulated annealing algorithm of multiple populations to solve the classical TSP problem. This algorithm has introduced collateral mechanism of multiple populations and increased the initial populations so that it can include more solution set, avoid local minima, thus it has improved the optimization efficiency.This algorithm has very high use value in solving the TSP problem. Keywords: Traveling salesman problem, NP (Non-deterministic Polynomial) problem, simulated annealing algorithm, multiple populations

scholarly journals Sequential Simulated Annealing for the Vehicle Routing Problem with Time Windows

2009 ◽  
Vol 3 (2) ◽  
pp. 87-100 ◽  
Author(s):  
Marcin Woch ◽  
Piotr Łebkowski
Keyword(s):  
Simulated Annealing ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Time Windows ◽  
High Quality ◽  
Routing Problem ◽  
Annealing Algorithm ◽  
Very High

This article presents a new simulated annealing algorithm that provides very high quality solutions to the vehicle routing problem. The aim of described algorithm is to solve the vehicle routing problem with time windows. The tests were carried out with use of some well known instances of the problem defined by M. Solomon. The empirical evidence indicates that simulated annealing can be successfully applied to bi-criterion optimization problems.

scholarly journals Application of simulated annealing algorithm for 3D coordinate transformation problem solution

2020 ◽  
Vol 12 (1) ◽  
pp. 491-502 ◽  
Author(s):  
Waldemar Odziemczyk
Keyword(s):  
Simulated Annealing ◽  
Geographical Information Systems ◽  
Simulated Annealing Algorithm ◽  
Least Squares Method ◽  
Initial Approximation ◽  
Initial Step ◽  
Geographical Information ◽  
Problem Solution ◽  
Annealing Algorithm ◽  
Transformation Parameters

AbstractTransformation of spatial coordinates (3D) is a common computational task in photogrammetry, engineering geodesy, geographical information systems or computer vision. In the most frequently used variant, transformation of point coordinates requires knowledge of seven transformation parameters, of which three determine translation, another three rotation and one change in scale. As these parameters are commonly determined through iterative methods, it is essential to know their initial approximation. While determining approximate values of the parameters describing translation or scale change is relatively easy, determination of rotation requires more advanced methods. This study proposes an original, two-step procedure of estimating transformation parameters. In the initial step, a modified version of simulated annealing algorithm is used for identifying the approximate value of the rotation parameter. In the second stage, traditional least squares method is applied to obtain the most probable values of transformation parameters. The way the algorithm works was checked on two numerical examples. The computational experiments proved that proposed algorithm is efficient even in cases characterised by very disadvantageous configuration of common points.

Protein Folding Simulation Based on Improved Simulated Annealing Algorithm

2012 ◽  
Vol 424-425 ◽  
pp. 246-249
Author(s):  
Jing Fa Liu ◽  
Zi Ling Zhou ◽  
Ze Xu Gao ◽  
Guo Jian Zhang
Keyword(s):  
Protein Folding ◽  
Simulated Annealing ◽  
Ground State ◽  
Lower Energy ◽  
Simulated Annealing Algorithm ◽  
Two Dimensional ◽  
Folding Simulation ◽  
Simulation Based ◽  
Annealing Algorithm ◽  
Annealing Method

Protein folding problem is one of the most important problems in bioinformatics. By combining simulated annealing method with pull moves which is a local move set and conformation update mechanism, we put forward an improved simulated annealing (ISA) algorithm for the two-dimensional hydrophobic- polar (2D HP) protein folding problem. Numerical results show that the ISA algorithm can find the known lowest-energy ground state more rapidly and efficiently than the genetic algorithm (GA) for the several given HP sequences. For the sequence with length 20, we obtain the lower-energy conformation than GA. The performance of the algorithm show ISA is an effective method for protein folding simulation

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