Group teaching optimization algorithm: A novel metaheuristic method for solving global optimization problems

Zhang, Yiying; Jin, Zhigang

doi:10.1016/j.eswa.2020.113246

“…In the past decades, metaheuristic algorithms were shown to be very successful for solving various optimization problems [63,64,65,66,67]. TLBO is a recent, nature-inspired metaheuristic, that has been widely used in tackling different optimization problems in many fields and different real-life applications [68].…”

Section: Related Workmentioning

confidence: 99%

Teaching Learning-Based Optimization With Evolutionary Binarization Schemes for Tackling Feature Selection Problems

Thaher

¹

,

Mafarja

²

,

Turabieh

³

et al. 2021

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Machine learning techniques heavily rely on available training data in a data set. Certain features in the data can interfere with the learning process, so it is required to remove irrelevant and redundant features to build a robust training model. As such, several feature selection techniques are usually applied in a pre-processing phase to obtain the most appropriate set of features and improve the overall learning process. In this paper, a new feature selection approach is proposed based on a modified Teaching-Learning-based Optimization (TLBO) combined with four new binarization methods: the Elitist, the Elitist Roulette, the Elitist Tournament, and the Rank-based method. The influence of these binarization methods is studied and compared to other state-of-the-art techniques. The experimental results such as Shapiro-Wilk normality and Wilcoxon ranksum test show that both transfer functions and binarization approaches have a significant influence on the effectiveness of the binary TLBO. The experiments show that choosing a fitting transfer function along with a suitable binarization method has a substantial impact on the exploratory and exploitative potentials of the feature selection technique.

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“…The group teaching optimization algorithm is designed to enhance the entire class knowledge by suggesting the group teaching scheme [24]. To change the group teaching appropriate for employing the optimization technique, first consider the population, fitness value, and decision variables are equivalent to the students, students knowledge, and subjects of the students, correspondingly.…”

Section: Group Teaching Optimization Algorithmmentioning

confidence: 99%

ANFIS Based Optimal Routing using Group Teaching and Adaptive Equilibrium Optimization based Trust Aware Routing Protocol in MANET

Hemalatha

¹

,

Umamaheswari²,

Jothi

³

2021

Preprint

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Recently, routing is considered the main problem in MANET due to its dynamic nature. The route discovery and the optimal route selection from the multiple routes are established for the efficient routing in MANET. The major objective of this research is to select the optimal route for packet transmission in MANET. In this paper, four stages namely trust evaluation, route discovery, optmal route selection and route maintanance are elucidated. Initially, the trust evaluation is made by using ANFIS where the primary trust values are evaluated. The next stage is the route discovery scheme, in which the routes are established by Group teaching optimization algorithm (GTA). From the route discovery scheme, multiple routes are found. The optimal route for the transmission is selected with the help of the Adaptive equilibrium optimizer (AO) algorithm. Finally, the route maintenance process is established; if any of the routes fails for the broadcast it immediately selects the alternate optimal route from the multi-zone routing table for efficient packet transmission. The proposed approach is evaluated by various performance measures like throughput, energy consumption, packet delivery ratio, end-to-end delay, packet loss rate, detection rate, and routing overhead. This result describes that the proposed approach outperforms other state-of-art approaches.

show abstract

“…The group teaching optimization algorithm is designed to enhance the entire class knowledge by suggesting the group teaching scheme (Zhang and Jin, 2020). To change the group teaching appropriate for employing the optimization technique, first consider the population, fitness value, and decision variables are equivalent to the students, students knowledge, and subjects of the students, correspondingly.…”

Section: Group Teaching Optimization Algorithmmentioning

confidence: 99%

Optimal Route Maintenance based on Adaptive Equilibrium Optimization and GTA based Route Discovery Model in MANET

Hemalatha

¹

,

Umamaheswari

²

,

Jothi

³

2021

Preprint

0

View full text Add to dashboard Cite

Recently, routing is considered the main problem in MANET due to its dynamic nature. The route discovery and the optimal route selection from the multiple routes are established for the efficient routing in MANET. The major objective of this research is to select the optimal route for packet transmission in MANET. In this paper, four stages namely trust evaluation, route discovery, optmal route selection and route maintanance are elucidated. Initially, the trust evaluation is made by using ANFIS where the primary trust values are evaluated. The next stage is the route discovery scheme, in which the routes are established by Group teaching optimization algorithm (GTA). From the route discovery scheme, multiple routes are found. The optimal route for the transmission is selected with the help of the Adaptive equilibrium optimizer (AO) algorithm. Finally, the route maintenance process is established; if any of the routes fails for the broadcast it immediately selects the alternate optimal route from the multi-zone routing table for efficient packet transmission. The proposed approach is evaluated by various performance measures like throughput, energy consumption, packet delivery ratio, end-to-end delay, packet loss rate, detection rate, and routing overhead. This result describes that the proposed approach outperforms other state-of-art approaches.

show abstract

Group teaching optimization algorithm: A novel metaheuristic method for solving global optimization problems

Cited by 182 publications

References 58 publications

Teaching Learning-Based Optimization With Evolutionary Binarization Schemes for Tackling Feature Selection Problems

Teaching Learning-Based Optimization With Evolutionary Binarization Schemes for Tackling Feature Selection Problems

ANFIS Based Optimal Routing using Group Teaching and Adaptive Equilibrium Optimization based Trust Aware Routing Protocol in MANET

Optimal Route Maintenance based on Adaptive Equilibrium Optimization and GTA based Route Discovery Model in MANET

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