Cleaning Uncertain Database with Aggregate Constraints Based on the Modified Simulated Annealing Algorithm

2015 ◽  
Vol 713-715 ◽  
pp. 1661-1664
Author(s):  
Ji Cheng Shan ◽  
Bin Liu ◽  
Qing Bao Liu
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Uncertain Data ◽  
Data Sets ◽  
Function Model ◽  
Object Function ◽  
Dirty Data ◽  
Efficiency And Effectiveness ◽  
Uncertain Database ◽  
Annealing Algorithm

In this paper, we investigate the problem how to clean uncertain data with aggregate constraints in order to reduce the uncertainty and clean the dirty data in uncertain data sets. We find the shortages by analyzing the existing model and methods for cleaning uncertain data with aggregate constraints. We modified the existing Object Function model in literature and designed an appropriate algorithm for our problem by studying the Modified Simulated Annealing algorithm. Our experiments verify the efficiency and effectiveness of our algorithm.

Diversification-based learning simulated annealing algorithm for hub location problems

2019 ◽  
Vol 26 (6) ◽  
pp. 1995-2016
Author(s):  
Himanshu Rathore ◽  
Shirsendu Nandi ◽  
Peeyush Pandey ◽  
Surya Prakash Singh
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Convergence Rates ◽  
Location Problems ◽  
Computational Time ◽  
Data Sets ◽  
Learning Mechanisms ◽  
Hub Location ◽  
Content Type ◽  
Annealing Algorithm

Purpose The purpose of this paper is to examine the efficacy of diversification-based learning (DBL) in expediting the performance of simulated annealing (SA) in hub location problems. Design/methodology/approach This study proposes a novel diversification-based learning simulated annealing (DBLSA) algorithm for solving p-hub median problems. It is executed on MATLAB 11.0. Experiments are conducted on CAB and AP data sets. Findings This study finds that in hub location models, DBLSA algorithm equipped with social learning operator outperforms the vanilla version of SA algorithm in terms of accuracy and convergence rates. Practical implications Hub location problems are relevant in aviation and telecommunication industry. This study proposes a novel application of a DBLSA algorithm to solve larger instances of hub location problems effectively in reasonable computational time. Originality/value To the best of the author’s knowledge, this is the first application of DBL in optimisation. By demonstrating its efficacy, this study steers research in the direction of learning mechanisms-based metaheuristic applications.

PARAMETERS OPTIMIZE METHOD BASED ON GENETIC AND SIMULATED ANNEALING ALGORITHMS

2008 ◽  
Vol 22 (01) ◽  
pp. 169-181 ◽  
Author(s):  
JIAO-MIN LIU ◽  
JING-HONG WANG
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Initial Study ◽  
Direct Selection ◽  
Data Sets ◽  
Theoretical Support ◽  
Fuzzy Extension Matrix ◽  
Annealing Algorithm ◽  
Parameter Values

This paper gives an initial study on the comparison between Genetic Algorithm (GA) and Simulated Annealing Algorithm (SAA). Firstly, a new algorithm is presented. This method combines Genetic Algorithm and Simulated Annealing Algorithm, and it can be used to optimize the three parameters α, β and γ. It involes the rules that are extracted from Fuzzy Extension Matrix (FEM). These parameters play an important part in the entire process of rule extraction based on FEM. Secondly, it provides some theoretical support to the direct selection of the parameter values through experiments. Lastly, five data sets from the UCI Machine Learning centers are employed in the study. Experimental results and discussions are given.

A Metaheuristic for the Pickup and Delivery Problem with Time Windows

2003 ◽  
Vol 12 (02) ◽  
pp. 173-186 ◽  
Author(s):  
Haibing Li ◽  
Andrew Lim
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Time Windows ◽  
Pickup And Delivery ◽  
Data Sets ◽  
Search Procedure ◽  
Delivery Problem ◽  
Pickup And Delivery Problem ◽  
Annealing Algorithm ◽  
Problem Instances

In this paper, we propose a metaheuristic to solve the pickup and delivery problem with time windows. Our approach is a tabu-embedded simulated annealing algorithm which restarts a search procedure from the current best solution after several non-improving search iterations. The computational experiments on the six newly-generated different data sets marked our algorithm as the first approach to solve large multiple-vehicle PDPTW problem instances with various distribution properties.

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