sis 18: e29

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: Online First},
        volume={},
        number={},
        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.