Global optimization by Very Fast Simulated Annealing algorithm of the 3D CRS and 3D CDS stack parameters: Application to real data of Potiguar Basin, Brazil

2017 ◽  
Author(s):  
German Garabito ◽  
Heron Schots ◽  
José Tassini
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Real Data ◽  
Potiguar Basin ◽  
Very Fast Simulated Annealing ◽  
Annealing Algorithm

Global optimization strategies for implementing 3D common-reflection-surface stack using the very fast simulated annealing algorithm: Application to real land data

Geophysics ◽  
2018 ◽  
Vol 83 (4) ◽  
pp. V253-V261 ◽  
Author(s):  
German Garabito
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Computational Cost ◽  
Velocity Model ◽  
Global Search ◽  
Optimization Strategy ◽  
Very Fast Simulated Annealing ◽  
Common Reflection Surface ◽  
Annealing Algorithm

The 3D common-reflection-surface (CRS) stack operator depends on eight kinematic wavefield attributes that must be extracted from the prestack data. These attributes are obtained by an efficient optimization strategy based on the maximization of the coherence measure of the seismic reflection events included by the CRS stacking operator. The main application of these kinematic attributes is to simulate zero-offset stacked data; however, they can also be used for regularization of the prestack data, prestack migration, and velocity model determination. The initial implementations of the 3D CRS stack used grid-search techniques to determine the attributes in several steps with the drawback that accumulated errors can deteriorate the final result. In this work, the global optimization very fast simulated annealing algorithm is used to search for the kinematic attributes by applying three optimization strategies for implementing CRS stacking: (1) simultaneous global search of five kinematic attributes of the 3D common-diffraction-surface stacking operator, (2) two-step global optimization strategy to first search for three attributes and then five attributes of the CRS stacking operator, and (3) simultaneous global search of eight kinematic attributes of the CRS operator. The proposed CRS stacking algorithms are applied to land data of the Potiguar Basin, Brazil. It is demonstrated that the one-step optimization strategy of the eight parameters produces the best results, however, with a higher computational cost.

A New Hybrid Simulated Annealing Algorithm for Large Scale Global Optimization

2015 ◽  
Vol 5 (3) ◽  
pp. 24-36
Author(s):  
Seifedine N. Kadry ◽  
Abdelkhalak El Hami
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Structural Optimization ◽  
Stochastic Approximation ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Simultaneous Perturbation Stochastic Approximation ◽  
Hybrid Simulated Annealing ◽  
Annealing Algorithm

The present paper focus on the improvement of the efficiency of structural optimization, in typical structural optimization problems there may be many locally minimum configurations. For that reason, the application of a global method, which may escape from the locally minimum points, remain essential. In this paper, a new hybrid simulated annealing algorithm for large scale global optimization problems with constraints is proposed. The authors have developed a stochastic algorithm called SAPSPSA that uses Simulated Annealing algorithm (SA). In addition, the Simultaneous Perturbation Stochastic Approximation method (SPSA) is used to refine the solution. Commonly, structural analysis problems are constrained. For the reason that SPSA method involves penalizing constraints a penalty method is used to design a new method, called Penalty SPSA (PSPSA) method. The combination of both methods (Simulated Annealing algorithm and Penalty Simultaneous Perturbation Stochastic Approximation algorithm) provides a powerful hybrid stochastic optimization method (SAPSPSA), the proposed method is applicable for any problem where the topology of the structure is not fixed. It is simple and capable of handling problems subject to any number of constraints which may not be necessarily linear. Numerical results demonstrate the applicability, accuracy and efficiency of the suggested method for structural optimization. It is found that the best results are obtained by SAPSPSA compared to the results provided by the commercial software ANSYS.

A hybrid differential evolution and simulated annealing algorithm for global optimization

2021 ◽  
pp. 1-17
Author(s):  
Xiaobing Yu ◽  
Zhenjie Liu ◽  
XueJing Wu ◽  
Xuming Wang
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Differential Evolution ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Local Optimum ◽  
Search Efficiency ◽  
Optimal State ◽  
Mutation Operation ◽  
Annealing Algorithm

Differential evolution (DE) is one of the most effective ways to solve global optimization problems. However, considering the traditional DE has lower search efficiency and easily traps into local optimum, a novel DE variant named hybrid DE and simulated annealing (SA) algorithm for global optimization (HDESA) is proposed in this paper. This algorithm introduces the concept of “ranking” into the mutation operation of DE and adds the idea of SA to the selection operation. The former is to improve the exploitation ability and increase the search efficiency, and the latter is to enhance the exploration ability and prevent the algorithm from trapping into the local optimal state. Therefore, a better balance can be achieved. The experimental results and analysis have shown its better or at least equivalent performance on the exploitation and exploration capability for a set of 24 benchmark functions. It is simple but efficient.

scholarly journals Multiple-Try Simulated Annealing Algorithm for Global Optimization

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Wei Shao ◽  
Guangbao Guo
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Real Data ◽  
Global Optimum ◽  
Stochastic Optimization Algorithm ◽  
Multiple Try Metropolis Algorithm ◽  
Quasi Newton ◽  
Cooling Schedule

Simulated annealing is a widely used algorithm for the computation of global optimization problems in computational chemistry and industrial engineering. However, global optimum values cannot always be reached by simulated annealing without a logarithmic cooling schedule. In this study, we propose a new stochastic optimization algorithm, i.e., simulated annealing based on the multiple-try Metropolis method, which combines simulated annealing and the multiple-try Metropolis algorithm. The proposed algorithm functions with a rapidly decreasing schedule, while guaranteeing global optimum values. Simulated and real data experiments including a mixture normal model and nonlinear Bayesian model indicate that the proposed algorithm can significantly outperform other approximated algorithms, including simulated annealing and the quasi-Newton method.

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