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.

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.

Artificial Intelligence Application in Modern Management

2010 ◽  
Vol 37-38 ◽  
pp. 203-206
Author(s):  
Rong Jiang
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Local Optimum ◽  
Efficient Operation ◽  
Modern Management ◽  
Global Optimal ◽  
Annealing Algorithm

Modern management is a science of technology that adopts analysis, test and quantification methods to make a comprehensive arrangement of the limited resources to realize an efficient operation of a practical system. Simulated annealing algorithm has become one of the important tools for solving complex optimization problems, because of its intelligence, widely used and global search ability. Genetic algorithm may prevent effectively searching process from restraining in local optimum, thus it is more possible to obtains the global optimal solution.This paper solves unconstrained programming by simulated annealing algorithm and calculates constrained nonlinear programming by genetic algorithm in modern management. So that optimization process was simplified and the global optimal solution is ensured reliably.

scholarly journals An Enhanced Differential Evolution Based Algorithm with Simulated Annealing for Solving Multiobjective Optimization Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Bili Chen ◽  
Wenhua Zeng ◽  
Yangbin Lin ◽  
Qi Zhong
Keyword(s):  
Simulated Annealing ◽  
Multiobjective Optimization ◽  
Differential Evolution ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Acceptance Probability ◽  
Nondominated Solutions ◽  
Multiobjective Optimization Problems ◽  
Annealing Algorithm

An enhanced differential evolution based algorithm, named multi-objective differential evolution with simulated annealing algorithm (MODESA), is presented for solving multiobjective optimization problems (MOPs). The proposed algorithm utilizes the advantage of simulated annealing for guiding the algorithm to explore more regions of the search space for a better convergence to the true Pareto-optimal front. In the proposed simulated annealing approach, a new acceptance probability computation function based on domination is proposed and some potential solutions are assigned a life cycle to have a priority to be selected entering the next generation. Moreover, it incorporates an efficient diversity maintenance approach, which is used to prune the obtained nondominated solutions for a good distributed Pareto front. The feasibility of the proposed algorithm is investigated on a set of five biobjective and two triobjective optimization problems and the results are compared with three other algorithms. The experimental results illustrate the effectiveness of the proposed algorithm.

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.

ON THE PHYSICAL APPLICATION OF SIMULATED ANNEALING ALGORITHM

1999 ◽  
Vol 10 (06) ◽  
pp. 1065-1070 ◽  
Author(s):  
SHU-YOU LI ◽  
ZHI-HUI DU ◽  
MENG-YUE WU ◽  
JING ZHU ◽  
SAN-LI LI
Keyword(s):  
Simulated Annealing ◽  
Parameter Optimization ◽  
High Performance ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Source Code ◽  
Physical Application ◽  
General Program ◽  
Annealing Algorithm

A high-performance general program is presented to deal with the multi-parameter optimization problems in physics. Considering the requirements of physical application, some small but significant modifications were made on the conventional simulated annealing algorithm. A parallel realization was suggested to further improve the performance of the program. Mathematical and physical examples were taken to test the feasibility and the efficiency of the program. The source code is available from the authors free of charge.

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