An Effective Two-Stage Feature Selection Method with Parameters Optimized by Simulated Annealing Algorithm

2018 ◽  
Author(s):  
Zhe Yang ◽  
Jia Ren
Keyword(s):  
Feature Selection ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Feature Selection Method ◽  
Selection Method ◽  
Two Stage ◽  
Annealing Algorithm

scholarly journals A Two-Stage Simulated Annealing Algorithm for the Many-to-Many Milk-Run Routing Problem with Pipeline Inventory Cost

2015 ◽  
Vol 2015 ◽  
pp. 1-22 ◽  
Author(s):  
Yu Lin ◽  
Zheyong Bian ◽  
Shujing Sun ◽  
Tianyi Xu
Keyword(s):  
Simulated Annealing ◽  
Heuristic Algorithm ◽  
Simulated Annealing Algorithm ◽  
Greedy Heuristic ◽  
Two Stage ◽  
Satisfactory Solution ◽  
Routing Problem ◽  
Annealing Algorithm ◽  
The Many ◽  
Milk Run

In recent years, logistics systems with multiple suppliers and plants in neighboring regions have been flourishing worldwide. However, high logistics costs remain a problem for such systems due to lack of information sharing and cooperation. This paper proposes an extended mathematical model that minimizes transportation and pipeline inventory costs via the many-to-many Milk-run routing mode. Because the problem is NP hard, a two-stage heuristic algorithm is developed by comprehensively considering its characteristics. More specifically, an initial satisfactory solution is generated in the first stage through a greedy heuristic algorithm to minimize the total number of vehicle service nodes and the best insertion heuristic algorithm to determine each vehicle’s route. Then, a simulated annealing algorithm (SA) with limited search scope is used to improve the initial satisfactory solution. Thirty numerical examples are employed to test the proposed algorithms. The experiment results demonstrate the effectiveness of this algorithm. Further, the superiority of the many-to-many transportation mode over other modes is demonstrated via two case studies.

scholarly journals Two-stage Unsupervised Feature Selection Method Oriented to Manufacturing Procedural Data

2019 ◽  
Vol 55 (17) ◽  
pp. 133
Author(s):  
ZHANG Jie ◽  
SHENG Xia ◽  
ZHANG Peng ◽  
QIN Wei ◽  
ZHAO Xinming
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Two Stage ◽  
Unsupervised Feature Selection

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.

MMBDE: A Two-stage Hybrid Feature Selection Method From Microarray Data

2021 ◽  
Author(s):  
Weidong Xie ◽  
Yuhuan Chi ◽  
Linjie Wang ◽  
Kun Yu ◽  
Wei Li
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Feature Selection Method ◽  
Selection Method ◽  
Two Stage

scholarly journals A New Feature Selection Method for Hyperspectral Image Classification Based on Simulated Annealing Genetic Algorithm and Choquet Fuzzy Integral

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Hongmin Gao ◽  
Lizhong Xu ◽  
Chenming Li ◽  
Aiye Shi ◽  
Fengchen Huang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Hyperspectral Image ◽  
Selection Method ◽  
Hyperspectral Images ◽  
Fuzzy Integral ◽  
High Dimensions ◽  
Simulated Annealing Genetic Algorithm

Hyperspectral remote sensing technology is a rapidly developing new integrated technology that is widely used in numerous areas. Rich spectral information from hyperspectral images can aid in the classification and recognition of the ground objects. However, the high dimensions of hyperspectral images cause redundancy in information. Hence, the high dimensions of hyperspectral data must be reduced. This paper proposes a hybrid feature selection strategy based on the simulated annealing genetic algorithm (SAGA) and the Choquet fuzzy integral (CFI). The band selection method is proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then, the selecting bands are further refined by CFI. Experimental results show that the proposed method can achieve higher classification accuracy than traditional methods.

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