Efficient Subpopulation Based Parallel TLBO Optimization Algorithms

García-Monzó, Alejandro; Migallón, Héctor; Jimeno-Morenilla, Antonio; Sánchez-Romero, José-Luis; Rico, Héctor; Rao, R. Venkata

doi:10.3390/electronics8010019

Cited by 7 publications

(6 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meanwhile, fuzzy TLBO required the most time to classify all datasets. This could be because the fuzzy TLBO's slow training procedure utilized a large amount of computer memory, making it time demanding [193]- [195]. The comparison of time expresses in Table 2.…”

Section: Resultsmentioning

confidence: 99%

Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification

Aseri

Ismail

Fakharudin

et al. 2022

IJ-AI

View full text Add to dashboard Cite

The world health organization (WHO) proclaimed the COVID-19, commonly known as the coronavirus disease 2019, was a pandemic in March 2020. When people are in close proximity to one another, the virus spreads mostly through the air. It causes some symptoms in the affected person. COVID-19 symptoms are quite variable, ranging from none to severe sickness. As a result, the fuzzy method is seen favourably as a tool for determining the severity of a person’s COVID-19 sickness. However, when applied to a large situation, manually generating a fuzzy parameter is challenging. This could be because of the identification of a large number of fuzzy parameters. A mechanism, such as an automatic procedure, is consequently required to identify the right fuzzy parameters. The metaheuristic algorithm is regarded as a viable strategy. Five meta-heuristic algorithms were analyzed and utilized in this article to classify the severity of COVID-19 sickness data. The performance of the five meta-heuristic algorithms was evaluated using the COVID-19 symptoms dataset. The COVID-19 symptom dataset was created in accordance with WHO and the Indian ministry of health and family welfare criteria. The findings provide the average classification accuracy for each approach.

show abstract

Section: Resultsmentioning

confidence: 99%

Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification

Aseri

Ismail

Fakharudin

et al. 2022

IJ-AI

View full text Add to dashboard Cite

show abstract

“…In addition, it depends on the objective function. Regarding a global convergence analysis, both TLBO and CJaya behave better but with a higher order of complexity (see [77,80]). Moreover, it is noted that when using TLBO, two new individuals are generated in each iteration; one in the teacher phase and the other one in the learner phase.…”

Section: Population Size 70 140 210mentioning

confidence: 99%

Settings-Free Hybrid Metaheuristic General Optimization Methods

et al. 2020

Self Cite

View full text Add to dashboard Cite

Several population-based metaheuristic optimization algorithms have been proposed in the last decades, none of which are able either to outperform all existing algorithms or to solve all optimization problems according to the No Free Lunch (NFL) theorem. Many of these algorithms behave effectively, under a correct setting of the control parameter(s), when solving different engineering problems. The optimization behavior of these algorithms is boosted by applying various strategies, which include the hybridization technique and the use of chaotic maps instead of the pseudo-random number generators (PRNGs). The hybrid algorithms are suitable for a large number of engineering applications in which they behave more effectively than the thoroughbred optimization algorithms. However, they increase the difficulty of correctly setting control parameters, and sometimes they are designed to solve particular problems. This paper presents three hybridizations dubbed HYBPOP, HYBSUBPOP, and HYBIND of up to seven algorithms free of control parameters. Each hybrid proposal uses a different strategy to switch the algorithm charged with generating each new individual. These algorithms are Jaya, sine cosine algorithm (SCA), Rao’s algorithms, teaching-learning-based optimization (TLBO), and chaotic Jaya. The experimental results show that the proposed algorithms perform better than the original algorithms, which implies the optimal use of these algorithms according to the problem to be solved. One more advantage of the hybrid algorithms is that no prior process of control parameter tuning is needed.

show abstract

“…García-Monzó et al developed a shared memory-based and message-passing based parallel TLBO algorithm. Authors have used thirty unconstrained benchmark functions to evaluate the performance of proposed approach [49]. Other parallel implementation of TLBO found in [2,50] to solve unconstrained benchmark functions.…”

Section: Related Workmentioning

confidence: 99%

Master-Slave TLBO Algorithm for Constrained Global Optimization Problems

Mane

Adamuthe

Omane³

2018

ICST Transactions on Scalable Information Systems

View full text Add to dashboard Cite

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.

show abstract

Efficient Subpopulation Based Parallel TLBO Optimization Algorithms

Cited by 7 publications

References 17 publications

Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification

Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification

Settings-Free Hybrid Metaheuristic General Optimization Methods

Master-Slave TLBO Algorithm for Constrained Global Optimization Problems

Contact Info

Product

Resources

About