Multiple Flat Beams Generation Using Firefly and Teaching Learning Based Optimization Techniques

2019 ◽  
pp. 433-440
Author(s):  
R. Krishna Chaitanya ◽  
P. Mallikarjuna Rao ◽  
K. V. S. N. Raju ◽  
G. S. N. Raju
Keyword(s):  
Optimization Techniques ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

TLBO-FLN: Teaching-Learning Based Optimization of Functional Link Neural Networks for Stock Closing Price Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 522-532 ◽  
Author(s):  
Sarat Chandra Nayak ◽  
Subhranginee Das ◽  
Mohammad Dilsad Ansari
Keyword(s):  
Neural Networks ◽  
Computational Cost ◽  
Optimization Techniques ◽  
Fine Tuning ◽  
Functional Link ◽  
Price Prediction ◽  
Closing Price ◽  
Teaching Learning Based Optimization ◽  
Artificial Neural ◽  
Teaching Learning

Background and Objective: Stock closing price prediction is enormously complicated. Artificial Neural Networks (ANN) are excellent approximation algorithms applied to this area. Several nature-inspired evolutionary optimization techniques are proposed and used in the literature to search the optimum parameters of ANN based forecasting models. However, most of them need fine-tuning of several control parameters as well as algorithm specific parameters to achieve optimal performance. Improper tuning of such parameters either leads toward additional computational cost or local optima. Methods: Teaching Learning Based Optimization (TLBO) is a newly proposed algorithm which does not necessitate any parameters specific to it. The intrinsic capability of Functional Link Artificial Neural Network (FLANN) to recognize the multifaceted nonlinear relationship present in the historical stock data made it popular and got wide applications in the stock market prediction. This article presents a hybrid model termed as Teaching Learning Based Optimization of Functional Neural Networks (TLBO-FLN) by combining the advantages of both TLBO and FLANN. Results and Conclusion: The model is evaluated by predicting the short, medium, and long-term closing prices of four emerging stock markets. The performance of the TLBO-FLN model is measured through Mean Absolute Percentage of Error (MAPE), Average Relative Variance (ARV), and coefficient of determination (R2); compared with that of few other state-of-the-art models similarly trained and found superior.

PMU Placement Optimization for Fault Observation Using Different Techniques

2021 ◽  
pp. 196-220
Author(s):  
Hamid Bentarzi
Keyword(s):  
Cuckoo Search ◽  
Optimal Number ◽  
Optimization Techniques ◽  
Grey Wolf Optimizer ◽  
Placement Optimization ◽  
Teaching Learning Based Optimization ◽  
Measurement Units ◽  
Pmu Placement ◽  
Teaching Learning ◽  
Full Network

This chapter presents different techniques for obtaining the optimal number of the phasor measurement units (PMUs) that may be installed in a smart power grid to achieve full network observability under fault conditions. These optimization techniques such as binary teaching learning based optimization (BTLBO) technique, particle swarm optimization, the grey wolf optimizer (GWO), the moth-flame optimization (MFO), the cuckoo search (CS), and the wind-driven optimization (WDO) have been developed for the objective function and constraints alike. The IEEE 14-bus benchmark power system has been used for testing these optimization techniques by simulation. A comparative study of the obtained results of previous works in the literature has been conducted taking into count the simplicity of the model and the accuracy of characteristics.

scholarly journals Rough set and teaching learning based optimization technique for optimal features selection

2013 ◽  
Vol 3 (1) ◽  
Author(s):  
Suresh Satapathy ◽  
Anima Naik ◽  
K. Parvathi
Keyword(s):  
Feature Selection ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Optimization Technique ◽  
Feature Selection Method ◽  
Optimization Techniques ◽  
Features Selection ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

AbstractRough set theory has been one of the most successful methods used for feature selection. However, this method is still not able to find optimal subsets. But it can be made to be optimal using different optimization techniques. This paper proposes a new feature selection method based on Rough Set theory with Teaching learning based optimization (TLBO). The proposed method is experimentally compared with other hybrid Rough Set methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) and the empirical results reveal that the proposed approach could be used for feature selection as this performs better in terms of finding optimal features and doing so in quick time.

scholarly journals Design optimization of a three-stage transmission using advanced optimization techniques

2019 ◽  
Vol 10 ◽  
pp. A8 ◽  
Author(s):  
Edmund S. Maputi ◽  
Rajesh Arora
Keyword(s):  
Geometric Model ◽  
Optimization Techniques ◽  
Stress Variation ◽  
Design Variables ◽  
Teaching Learning Based Optimization ◽  
Machine Elements ◽  
Mechanical Devices ◽  
Compact Design ◽  
Teaching Learning ◽  
Intended Use

Gear transmission systems are very important machine elements and their failure can lead to losses or damage of other mechanical components that comprise a machine or device. Since gears are applied in numerous mechanical devices, there is need to design and subsequently optimize them for intended use. In the present work, two objectives, viz., volume and center distance, are minimized for a rotary tiller to achieve a compact design. Two methods were applied: (1) analytical method, (2) a concatenation of the bounded objective function method and teaching–learning-based optimization techniques, thereby improving the result by 44% for the former and 55% for the latter. Using a geometric model and previous literature, the optimal results obtained were validated with 0.01 variation. The influence of design variables on the objective functions was also evaluated using variation studies reflecting on a ranking according to objective. Bending stress variation of 12.4% was less than contact stress at 51% for a defined stress range.

Passive Suspension Optimization Using Teaching Learning Based Optimization and Genetic Algorithm Considering Variable Speed Over a Bump

2014 ◽  
Author(s):  
Bhargav Gadhvi ◽  
Vimal Savsani
Keyword(s):  
Genetic Algorithm ◽  
Ride Comfort ◽  
Optimization Techniques ◽  
Passive Suspension ◽  
The Road ◽  
Quarter Car Model ◽  
Teaching Learning Based Optimization ◽  
Quarter Car ◽  
Vehicle Suspension System ◽  
Teaching Learning

The main objectives of a vehicle suspension system are to isolate the road excitations to reach the sprung mass of the vehicle and proper road holding. This paper proposes a solution to optimize a quarter car linear passive suspension parameters while passing over a bump with variable speeds to improve the ride comfort and road holding. The Teaching-learning based optimization algorithm (TLBO) is used to solve the problem and results are compared to those obtained by Genetic algorithm (GA) technique. The quarter car model presented is simulated in time domain subjected to a Cosine speed bump considering the variable speeds of the vehicle over it. Results show sprung mass acceleration, and tire displacement are reduced by 26.03%, and 23.7% respectively by using TLBO and 22.3%, and 18.52% respectively by using GA, conforming the capabilities of the optimization techniques.

scholarly journals Strengthened Teaching–Learning-Based Optimization Algorithm for Numerical Optimization Tasks

2021 ◽  
Author(s):  
Xuefen Chen ◽  
Chunming Ye ◽  
Yang Zhang ◽  
Lingwei Zhao ◽  
Jing Guo ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Numerical Optimization ◽  
Optimization Techniques ◽  
Data Availability ◽  
Search Performance ◽  
Local Optima ◽  
Cauchy Mutation ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Solution Accuracy

Abstract The teaching–learning-based optimization algorithm (TLBO) is an efficient optimizer. However, it has several shortcomings such as premature convergence and stagnation at local optima. In this paper, the strengthened teaching–learning-based optimization algorithm (STLBO) is proposed to enhance the basic TLBO’s exploration and exploitation properties by introducing three strengthening mechanisms: the linear increasing teaching factor, the elite system composed of new teacher and class leader, and the Cauchy mutation. Subsequently, seven variants of STLBO are designed based on the combined deployment of the three improved mechanisms. Performance of the novel STLBOs is evaluated by implementing them on thirteen numerical optimization tasks. The results show that STLBO7 is at the top of the list, significantly better than the original TLBO. Moreover, the remaining six variants of STLBO also outperform TLBO. Finally, a set of comparisons are implemented between STLBO7 and other advanced optimization techniques. The numerical results and convergence curves prove that STLBO7 clearly outperforms other competitors, has stronger local optimal avoidance, faster convergence speed and higher solution accuracy. All the above manifests that STLBOs has improved the search performance of TLBO. Data Availability Statements: All data generated or analyzed during this study are included in this published article.

scholarly journals Application of Teaching Learning Based Optimization in antenna designing

2015 ◽  
Vol 4 (1) ◽  
pp. 68 ◽  
Author(s):  
S. Dwivedi ◽  
V. Mishra ◽  
Y. Kosta
Keyword(s):  
Decision Making ◽  
Analytic Hierarchy Process ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Analytic Hierarchy ◽  
Weighted Sum Method ◽  
Order Preference ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Hierarchy Process

Numerous optimization techniques are studied and applied on antenna designs to optimize various performance parameters. Authors used many Multiple Attributes Decision Making (MADM) methods, which include, Weighted Sum Method (WSM), Weighted Product Method (WPM), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Analytic Hierarchy Process (AHP), ELECTRE, etc. Of these many MADM methods, TOPSIS and AHP are more widely used decision making methods. Both TOPSIS and AHP are logical decision making approaches and deal with the problem of choosing an alternative from a set of alternatives which are characterized in terms of some attributes. Analytic Hierarchy Process (AHP) is explained in detail and compared with WSM and WPM. Authors fi- nally used Teaching-Learning-Based Optimization (TLBO) technique; which is a novel method for constrained antenna design optimization problems.

scholarly journals Stability Enhancement of TLBO Tuned SMIB System

2020 ◽  
Vol 8 (6) ◽  
pp. 1389-1399
Keyword(s):  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Low Frequency ◽  
Optimization Techniques ◽  
System Stability ◽  
Low Frequency Oscillation ◽  
Value Analysis ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Stability Enhancement

In this paper investigation of the application of teaching learning based optimization (TLBO) technique for the design of a modified Phillips haffron model of SMIB installed with SSSC based controller is made. The design objectives are to reduce low frequency oscillation and improve power system stability. Simulation result are demonstrated with Eigen value analysis, where various types of disturbance is applied as mechanical torque input and reference voltage settling, variation in parameter & various loading condition. The results obtained are compared with some well-known optimization techniques, such as the genetic algorithm (GA), particle swarm optimization (PSO) and the gravitational search algorithm (GSA). A comparative study of results demonstrates that the results of the proposed controller were more precise and robust

Description of Various Evolutionary Optimization Techniques

2018 ◽  
pp. 1-45
Keyword(s):  
Power Systems ◽  
Behavioral Pattern ◽  
Optimization Techniques ◽  
Grey Wolf ◽  
Krill Herd Algorithm ◽  
Reaction Optimization ◽  
Krill Herd ◽  
Teaching Learning Based Optimization ◽  
Evolutionary Optimization Techniques ◽  
Teaching Learning

Despite the success of various classical optimization techniques, there remains a large class of problems where either these methods are unable to find the satisfactory results or the computational times are sufficiently large. Several heuristic methods have emerged in the recent years as complementary tools to various mathematical approaches. These methods include genetic algorithm (GA), evolutionary programming (EP), particle swarm optimization (PSO), differential evolution (DE), and so on. Researchers are constantly trying to learn from the behavioral pattern of organisms and implementing those ideas and philosophies in solving optimizing problems. In this chapter, a few efficient optimization algorithms, namely grey wolf optimization (GWO), teaching-learning-based optimization (TLBO), biogeography-based optimization (BBO), krill herd algorithm (KHA), chemical reaction optimization (CRO) algorithms, and hybrid CRO (HCRO) are discussed, and in the subsequent chapters, the performance of the aforesaid algorithms are investigated by applying them in a few areas of power systems.

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