EAI Endorsed Transactions on Scalable Information Systems (Apr 2021)

Master-Slave TLBO Algorithm for Constrained Global Optimization Problems

  • Sandeep Mane,
  • Amol Adamuthe,
  • Rajshree Omane

DOI
https://doi.org/10.4108/eai.26-5-2020.166292
Journal volume & issue
Vol. 8, no. 30

Abstract

Read online

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