Master-Slave TLBO Algorithm for Constrained Global Optimization Problems

Sandeep Mane; Amol Adamuthe; Rajshree Omane

Research Article

Master-Slave TLBO Algorithm for Constrained Global Optimization Problems

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@ARTICLE{10.4108/eai.26-5-2020.166292,
    author={Sandeep U. Mane and Amol C. Adamuthe and Rajshree R. Omane},
    title={Master-Slave TLBO Algorithm for Constrained Global Optimization Problems},
    journal={EAI Endorsed Transactions on Scalable Information Systems},
    volume={8},
    number={30},
    publisher={EAI},
    journal_a={SIS},
    year={2020},
    month={9},
    keywords={Master-slave TLBO algorithm, Parallel Evolutionary Algorithms, GPGPU, Constrained benchmark functions, Optimization problems},
    doi={10.4108/eai.26-5-2020.166292}
}

Sandeep U. Mane
Amol C. Adamuthe
Rajshree R. Omane
Year: 2020
Master-Slave TLBO Algorithm for Constrained Global Optimization Problems
SIS
EAI
DOI: 10.4108/eai.26-5-2020.166292

Sandeep U. Mane¹^,*, Amol C. Adamuthe², Rajshree R. Omane³

1: Dept. of CSE, Rajarambapu Institute of Technology (affiliated to Shivaji University Kolhapur), Rajaramnagar, Dist. Sangli, MH, India
2: Dept. of CS&IT, Rajarambapu Institute of Technology (affiliated to Shivaji University Kolhapur), Rajaramnagar, Dist. Sangli, MH, India
3: Associate Software Engineer, Amdocs, Pune, MH, India

*Contact email: manesandip82@gmail.com

Abstract

INTRODUCTION: The teaching-learning based optimization (TLBO) algorithm is a recently developed algorithm. The proposed work presents a design of a master-slave TLBO algorithm. OBJECTIVES: This research aims to design a master-slave TLBO algorithm to improve its performance and system utilization for CEC2006 single-objective benchmark functions. METHODS: The proposed approach implemented using OpenMP and CUDA C, a hybrid programming approach to enhance the utilization of the system’s computational resources. The device utilization and performance of the proposed approach evaluated using CEC2006 benchmark functions. RESULTS: The proposed approach obtains best results in significantly reduced time for CEC2006 benchmark functions. The maximum speed-up achieved is 30.14X. The average GPGPU utilization is 90% and the average utilization of logical processors is more than 90%. CONCLUSION: The master-slave TLBO algorithm improves the utilization of computational resources significantly and obtains the best results for CEC2006 benchmark functions.

Keywords: Master-slave TLBO algorithm, Parallel Evolutionary Algorithms, GPGPU, Constrained benchmark functions, Optimization problems

Received: 2020-06-07
Accepted: 2020-09-04
Published: 2020-09-09
Publisher: EAI

: http://dx.doi.org/10.4108/eai.26-5-2020.166292

Copyright © 2020 Sandeep U. Maneet al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.