COMPARATIVE ANALYSIS OF THRESHOLD ACCEPTANCE ALGORITHM, SIMULATED ANNEALING ALGORITHM AND GENETIC ALGORITHM FOR FUNCTION OPTIMIZATION

Dr. Tejas P Patalia; Dr. G.R. Kulkarni

COMPARATIVE ANALYSIS OF THRESHOLD ACCEPTANCE ALGORITHM, SIMULATED ANNEALING ALGORITHM AND GENETIC ALGORITHM FOR FUNCTION OPTIMIZATION

Dr. Tejas P Patalia * §

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dr._g.r.kulkarni

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§ VVP Engineering College, Rajkot & Singhania Universi...

COMPARATIVE ANALYSIS OF THRESHOLD ACCEPTANCE ALGORITHM, SIMULATED ANNEALING ALGORITHM AND GENETIC ALGORITHM FOR FUNCTION OPTIMIZATION

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Abstract

The goal of this study of threshold acceptance algorithm (TA), simulated annealing algorithm (SA) and genetic algorithm (GA) is to determine strength of Genetic Algorithm over other algorithm. It gives a clear idea of how genetic algorithm works. It gives the idea of various sub methods used in genetic algorithm to improve the results and outcome. Basically genetic algorithm and all traditional heuristic methods are used for optimization. Optimization problems are class NP complete problems. Genetic algorithm can be viewed as an optimization technique which exploits random search within a defined search space to solve a problem by some intelligence ideas of nature. In this work we have done Comparative analysis of Threshold Acceptance Algorithm, Simulated Annealing Algorithm and Genetic Algorithm by considering different test functions and its constraints to minimize the test functions.

References

12 Cites in Article

Reference Format

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Dr. Patalia, Dr. Kulkarni. 2012. "COMPARATIVE ANALYSIS OF THRESHOLD ACCEPTANCE ALGORITHM, SIMULATED ANNEALING ALGORITHM AND GENETIC ALGORITHM FOR FUNCTION OPTIMIZATION". Global Journal of Research in Engineering, Global Journal of Research in Engineering - I: Numerical Methods GJRE-I Volume 12 (GJRE Volume 12 Issue I1).

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GJRE Volume 12 Issue I1

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Journal Specifications

Crossref Journal DOI 10.17406/gjre

Print ISSN 0975-5861

e-ISSN 2249-4596

Keywords

Chromosomes function optimization Genetic Algorithm Heuristic methods Mutation simulated annealing algorithm threshold acceptance algorithm

Classification

GJRE-I Classification FOR Code: 010399

Submission ReceivedDecember 16, 2011
Peer Review Double Blind
Handling Editor
Accepted January 3, 2012
Published January 15, 2012

Version of record

v1.2

Issue date

March 14, 2012

Language

English

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Article Matrices

Total Score: 112

Country: India

Subject: Global Journal of Research in Engineering

Authors: Dr. Tejas P Patalia, Dr. G.R. Kulkarni (PhD/Dr. count: 2)

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Publish Date: 2012 01, Sun

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Total Views: 2K

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