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.

Artificial Intelligent Solver on Windscreen Wiper Mechanism of Automobile

2010 ◽  
Vol 145 ◽  
pp. 533-536
Author(s):  
Yan Dong Song
Keyword(s):  
Simulated Annealing ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Global Extremum ◽  
Local Extrema ◽  
Worm Gears ◽  
Annealing Algorithm ◽  
Four Bar Linkage ◽  
Significant Attention

The power source of windscreen wiper is motor, which is the core of the entire wiper system and is very high quality requirements. The wiper motor installed on the front windshield and worm gears are generally made of one mechanical part. The four-bar linkage of windshield wiper of automobile is driven by motor through worm gears. The method of simulated annealing is a technique that has attracted significant attention as suitable for optimization problems of large scale, especially ones where a desired global extremum is hidden among many, poorer, local extrema. The simulated annealing algorithm with penalty strategy is adopted to solve the optimal model of windscreen wiper mechanism, thus motion accuracy of wiper mechanism is greatly increased, and the searching number is greatly decreased.

Structural Shape Optimization Using an Adaptive Simulated Annealing

2010 ◽  
Vol 446 ◽  
pp. 101-110 ◽  
Author(s):  
W. El Alem ◽  
A. El Hami ◽  
Rachid Ellaia
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Structural Design ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Optimization Method ◽  
Structural Shape ◽  
Structural Shape Optimization ◽  
Adaptive Simulated Annealing ◽  
Annealing Algorithm

In structural design optimization, numerical techniques are increasingly used. 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 global optimization with constraints is proposed. We have developed a new algorithm called Adaptive Simulated Annealing algorithm (ASA); ASA is a series of modifications done to the Basic Simulated Annealing algorithm ( BSA) that gives the region containing the global solution of an objective function. In addition, the stochastic method Simultaneous Perturbation Stochastic Approximation (SPSA), for solving unconstrained optimization problems, is used to refine the solution. We also propose Penalty SPSA (PSPSA) for solving constrained optimization problems. The constraints are handled using exterior point penalty functions. 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 nonlinear constraints. Extensive tests on the ASA as a global optimization method are presented, its performance as a viable optimization method is demonstrated by applying it first to a series of benchmark functions with 2 - 30 dimensions and then it is used in structural design to demonstrate its applicability and efficiency. It is found that the best results are obtained by ASA compared to those provided by the commercial software ANSYS.

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