Master-Slave TLBO Algorithm for Constrained Global Optimization Problems

Mane, Sandeep U. and Adamuthe, Amol C. and Omane, Rajshree R. (2020) Master-Slave TLBO Algorithm for Constrained Global Optimization Problems. EAI Endorsed Transactions on Scalable Information Systems, 8 (30). e2. ISSN 2032-9407

Available under License Creative Commons Attribution No Derivatives.

Download (2MB) | Preview


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.

Item Type: Article
Uncontrolled Keywords: Master-slave TLBO algorithm, Parallel Evolutionary Algorithms, GPGPU, Constrained benchmark functions, Optimization problems
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor IV
Date Deposited: 20 Apr 2021 07:37
Last Modified: 20 Apr 2021 07:37

Actions (login required)

View Item View Item