Master-Slave TLBO Algorithm for Constrained Global Optimization Problems

EAI Endorsed Transactions on Scalable Information Systems

Amol Adamuthe¹  Sandeep Mane²  Rajshree Omane³ 

[1] Dept. of CS&Dept. of CSE, Rajarambapu Institute of Technology (affiliated to Shivaji University Kolhapur), Rajaramnagar, Dist. Sangli, MH, India;IT, Rajarambapu Institute of Technology (affiliated to Shivaji University Kolhapur), Rajaramnagar, Dist. Sangli, MH, India;

关键词: master-slave tlbo algorithm;    parallel evolutionary algorithms;    gpgpu;    constrained benchmark functions;    optimization problems;   

DOI  :  10.4108/eai.26-5-2020.166292

来源: DOAJ

【 摘 要 】

INTRODUCTION: Theteaching-learningbasedoptimization(TLBO)algorithmisarecentlydevelopedalgorithm.Theproposed work presents a design of a master-slave TLBO algorithm. OBJECTIVES: Thisresearchaimstodesignamaster-slaveTLBOalgorithmtoimproveitsperformanceandsystemutilization for CEC2006 single-objective benchmark functions. METHODS: TheproposedapproachimplementedusingOpenMPandCUDAC,ahybridprogrammingapproachtoenhancetheutilizationofthesystem’scomputationalresources.Thedeviceutilizationandperformanceoftheproposedapproach 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.

【 授权许可】

Unknown