A Survey on Teaching–Learning-Based Optimization Algorithm: Short Journey from 2011 to 2017

Nayak, Janmenjoy; Naik, Bighnaraj; Chandrasekhar, G.; Behera, Himansu Sekhar

doi:10.1007/978-981-10-8055-5_66

Cited by 9 publications

(2 citation statements)

References 95 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As with other swarm-intelligence algorithms, TLBO is a population-based stochastic-heuristic optimization algorithm. It has achieved great popularity due to some of its unique features, such as its concept and the fact that it does not require specific parameters, it is fast and easy to implement, and it has been widely applied to solve numerous problems in various engineering areas [3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

THD Minimization in a Seven-Level Multilevel Inverter Using the TLBO Algorithm

Díaz

León-Aldaco

Aguayo

et al. 2023

Eng

View full text Add to dashboard Cite

This paper presents the minimization of total harmonic distortion in a seven-level cascaded H-bridge multilevel inverter with resistive load using the teaching–learning-based optimization algorithm. The minimization of Total Harmonic Distortion (THD)is a challenging optimization problem due to the fact that nonlinear equations are involved. Recently, bio-inspired algorithms have become very popular approaches to solving various optimization problems in different areas of engineering. For this reason, the results obtained with the Teaching–Learning-Based Optimization (TLBO)algorithm were compared with three other popular bio-inspired algorithms, the genetic algorithm, differential evolution, and particle swarm optimization. The comparative analysis, conducted by sweeping the modulation index, made it possible to obtain graphs and data on the behavior of the four analyzed algorithms. Finally, it was concluded that the TLBO algorithm is very effective and is able to solve the THD-minimization problem. Its main advantage over the other algorithms is the fact that it does not require control parameters for its correct operation in the solution of the problem.

show abstract

Section: Introductionmentioning

confidence: 99%

THD Minimization in a Seven-Level Multilevel Inverter Using the TLBO Algorithm

Díaz

León-Aldaco

Aguayo

et al. 2023

Eng

View full text Add to dashboard Cite

show abstract

“…These parameters are the population size and generation number. Since the initial development of TLBO, researchers have proposed numerous variants of the algorithm to achieve high convergence speed and solution quality [33,34].The TLBO algorithm has been widely accepted by optimization researchers. Zou et al recently conducted a comprehensive investigation into the study of the TLBO algorithm [35] .…”

Section: Introductionmentioning

confidence: 99%

Allocation of FACTS Devices Using a Probabilistic Multi-Objective Approach Incorporating Various Sources of Uncertainty and Dynamic Line Rating

et al. 2020

View full text Add to dashboard Cite

Flexible AC transmission system (FACTS) controller play an essential role in increasing the penetration level of renewable energy resources owing to their ability in continuously controlling the active and reactive power flow in the network. This paper presents a probabilistic multi-objective optimization approach to obtain the optimal sizes and locations of static var compensators (SVCs) and thyristor-controlled series capacitors (TCSCs) in a power transmission network with high penetration level of wind generation. The objective of the problem is to maximize the system loadability while minimizing the network power losses and the installation cost of the FACTS controllers. In this study, the uncertainties associated with wind power generation and the correlated load demand are considered. The uncertainties are handled in this work using the points estimation method. Moreover, the dynamic line ratings (DLRs) of the transmission lines are considered in this work. In this case, the maximum transmission capacity of transmission lines is estimated dynamically according to the weather conditions. Considering the DLRs or transmission lines is expected to avoid unrealistic congestion in the network, and hence, improve its loadability. The optimization problem is solved using the multi-objective teaching-learning based optimization (MO-TLBO) algorithm to find the best locations and ratings for the FACTS controllers. Additionally, a technique based on the fuzzy decisionmaking approach is employed to extract one of the Pareto optimal solutions as the best compromise. The proposed approach is applied on the modified IEEE 30-bus system. The numerical results demonstrate the effectiveness of the proposed approach and show that the maximum loadability limit of the study system increases when considering the DLR. This limit can be enhanced to 123.0% without FACTS controller and 137.0 % ,130 % and 132.0% by SVC, TCSC and (SVC-TCSC) respectively.

show abstract

Efficient content retrieval in fog zone using Nano‐Caches

Singh

Kumar

Sharma

2019

Concurrency and Computation

View full text Add to dashboard Cite

Summary It is always desired to improve the response time from cloud servers, which deliver contents without buffering. As the penetration of mobile/fog devices is increasing, the limits of cellular ranges come under question. This question arises in spite of the fact that the current Internet Service Providers and data operators are adding cellular towers frequently to reduce delay and enhance performance. This performance can be improved by increasing Nano‐Cache(s) at the edges of the network for forwarding interrelated contents to remote corner of the earth. In this research work, Nano‐Caches are integrated for delivering contents efficiently, using search‐based optimization techniques, which are energy and response aware in nature. An algorithm, namely, Modified Teaching Learning‐Based Optimization(MTLBO), is devised and implemented in fog zone to find efficient route for forwarding contents using Nano‐Caches and subsequently to improve content retrieval time. Mathematical distribution model of traffic is used for simulation process. MTLBO is compared with existing algorithms, namely, Teaching Learning‐Based Optimization (TLBO) Algorithm and Simulated Annealing (SA) Algorithm. The design of experiments (DOE) was carried out to observe number of iterations, learning rate, and by changing the network size. Java library was used for observing values of memory and execution time. The results show that Modified Teaching Learning‐Based Optimization (MTLBO) approach is better than Teaching Learning‐Based Optimization (TLBO) approach as it has less overheads in terms of memory (considering number of fog caches) and network size for delivering contents at remote areas. In comparison to the Simulated Annealing (SA) algorithm, MTLBO performs better in terms of execution time, overhead in terms of memory, and scalability as function of network size.

show abstract

A Survey on Teaching–Learning-Based Optimization Algorithm: Short Journey from 2011 to 2017

Cited by 9 publications

References 95 publications

THD Minimization in a Seven-Level Multilevel Inverter Using the TLBO Algorithm

THD Minimization in a Seven-Level Multilevel Inverter Using the TLBO Algorithm

Allocation of FACTS Devices Using a Probabilistic Multi-Objective Approach Incorporating Various Sources of Uncertainty and Dynamic Line Rating

Efficient content retrieval in fog zone using Nano‐Caches

Contact Info

Product

Resources

About