Master-Slave TLBO Algorithm for Constrained Global Optimization Problems

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.

A Master-Slave Salp Swarm Algorithm Optimizer for Hybrid Energy Storage System Control Strategy in Electric Vehicles

“…For fair comparison of the selected algorithms and the inclusion of high-dimensional multimodal functions, a high number of search agents and iterations are required [6] and for these tests which are given as (i) No:of search agents = 60 (ii) Maximum iterations = 1000 Since they are stochastic algorithms, results obtained may significantly vary on every run. Performance can only be comparatively analyzed over a significant number of runs [6,10,11], for this study.…”

“…The master controls one or more processes or devices (known as slaves) and acts as a communication hub between the slaves [9]. Introducing a master-slave approach to the optimization algorithm is endeavored towards improving the ability to maintain balance between the exploration and exploitation phases of the SSA, thus improving its ability to skip local optimum points reaching the global optimum solution faster by eliminating trap solutions [10,11]. By implementing an improved adaptive power distribution learning algorithm in a simulated environment, the theoretical minimum energy consumption from a management and control system for a HESS can be calculated [12].…”