A Simulated Annealing Algorithm for Single Objective Trans-Dimensional Optimization Problems

2008 ◽  
Author(s):  
Hemant Kumar Singh ◽  
Amitay Isaacs ◽  
Tapabrata Ray ◽  
Warren Smith
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Annealing Algorithm ◽  
Single Objective ◽  
Dimensional Optimization

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.

scholarly journals Optimal Coordinated Strategy Analysis for the Procurement Logistics of a Steel Group

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Lianbo Deng ◽  
Zhuqiang Qiu ◽  
Pengfei Liu ◽  
Wenzhong Xiao
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Quality Requirement ◽  
Strategy Analysis ◽  
Logistics System ◽  
Evaluation Result ◽  
Objective Model ◽  
Annealing Algorithm ◽  
Procurement Strategy ◽  
Single Objective

This paper focuses on the optimization of an internal coordinated procurement logistics system in a steel group and the decision on the coordinated procurement strategy by minimizing the logistics costs. Considering the coordinated procurement strategy and the procurement logistics costs, the aim of the optimization model was to maximize the degree of quality satisfaction and to minimize the procurement logistics costs. The model was transformed into a single-objective model and solved using a simulated annealing algorithm. In the algorithm, the supplier of each subsidiary was selected according to the evaluation result for independent procurement. Finally, the effect of different parameters on the coordinated procurement strategy was analysed. The results showed that the coordinated strategy can clearly save procurement costs; that the strategy appears to be more cooperative when the quality requirement is not stricter; and that the coordinated costs have a strong effect on the coordinated procurement strategy.

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 SIMULATED ANNEALING ALGORITHM FOR FEATURE SELECTION

2015 ◽  
Vol 15 (2) ◽  
pp. 6471-6479
Author(s):  
Francisca Rosario ◽  
Dr. K. Thangadurai
Keyword(s):  
Feature Selection ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Weather Data ◽  
Computational Technique ◽  
Slow Cooling ◽  
Solution Quality ◽  
Data Set ◽  
Annealing Algorithm

In the process of physical annealing, a solid is heated until all particles randomly arrange themselves forming the liquid state. A slow cooling process is then used to crystallize the liquid. This process is known as simulated annealing. Simulated annealing is stochastic computational technique that searches for global optimum solutions in optimization problems. The main goal here is to give the algorithm more time in the search space exploration by accepting moves, which may degrade the solution quality, with some probability depending on a parameter called temperature. In this discussion the simulated annealing algorithm is implemented in pest and weather data set for feature selection and it reduces the dimension of the attributes through specified iterations.

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 A Multi-Object Simulated Annealing Approach to Identify Likely Functional Genomic Pairs

2021 ◽  
Author(s):  
Mohammad Hosseinpoor ◽  
H. Parvin
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Regulatory Elements ◽  
Metaheuristic Algorithms ◽  
Chromosome Conformation ◽  
Related Data ◽  
Health Related ◽  
Annealing Algorithm ◽  
First Time

Metaheuristic algorithms have been frequently using to tackle optimization problems, however such algorithms in the analysis of health-related data is not commonly used as developing metaheuristic algorithms that work well on health-related data is a difficult task due to complexity of the health data in particular genomics and epigenetics data. One of the important tasks in genomics is to predict genomic elements that are incorporating together to regulate a disease-related genes. Predicting such elements are important as they can be used to develop a personalized cure. In this study, we present for the first time, a multi-object simulated annealing algorithm to identify enhancer-promoter like interactions from Hi-C (chromosome conformation capture) data. These regulatory elements can potentially play vital roles as promoters and/or enhancers in appearance and exacerbation of the regulation of gene.s To evaluate the efficiency of the proposed method, we applied our proposed method and traditional methods on the Hi-C data from mice and compared together. Our results show that the interacting elements identified by our new method are more likely to be functional. The source code of the method is publicly available.

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