scholarly journals Micro Seismic Source Location Based on Teaching Learning-Based Optimization Technique

2020 ◽  
Vol 6 (3) ◽  
Keyword(s):  
Optimization Technique ◽  
Seismic Source ◽  
Source Location ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Seismic Source Location

A Comparative Study of Improved Teaching Learning Based Optimization Technique on Economic Load Dispatch Problem with Generator Constraints

2016 ◽  
Vol 5 (4) ◽  
pp. 1-25 ◽  
Author(s):  
Sumit Banerjee ◽  
Chandan Chanda ◽  
Deblina Maity
Keyword(s):  
Optimization Technique ◽  
Inequality Constraints ◽  
Test System ◽  
Economic Load Dispatch ◽  
Thermal Plant ◽  
Equality And Inequality Constraints ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Operational Constraints ◽  
Rate Limit

This article presents a novel improved teaching learning based optimization (I-TLBO) technique to solve economic load dispatch (ELD) problem of the thermal plant without considering transmission losses. The proposed methodology can take care of ELD problems considering practical nonlinearities such as ramp rate limit, prohibited operating zone and valve point loading. The objective of economic load dispatch is to determine the optimal power generation of the units to meet the load demand, such that the overall cost of generation is minimized, while satisfying different operational constraints. I-TLBO is a recently developed evolutionary algorithm based on two basic concepts of education namely teaching phase and learning phase. The effectiveness of the proposed algorithm has been verified on test system with equality and inequality constraints. Compared with the other existing techniques demonstrates the superiority of the proposed algorithm.

Micro-seismic source location determined by a modified objective function

2019 ◽  
Vol 36 (4) ◽  
pp. 1849-1856
Author(s):  
L. Z. Wu ◽  
S. H. Li ◽  
R. Q. Huang ◽  
S. Y. Wang
Keyword(s):  
Objective Function ◽  
Seismic Source ◽  
Source Location ◽  
Seismic Source Location

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.

Minimizing constraint forces and moments of manipulators using teaching–learning-based optimization and octahedron point mass model

2017 ◽  
Vol 232 (19) ◽  
pp. 3500-3511 ◽  
Author(s):  
Devi Singh Kumani ◽  
Himanshu Chaudhary
Keyword(s):  
Optimization Technique ◽  
Computational Effort ◽  
Point Mass ◽  
Constraint Forces ◽  
Mass Model ◽  
Industrial Manipulator ◽  
Model Configuration ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
First Time

The octahedron seven point mass model of equimomental point masses and optimization technique “teaching–learning-based optimization” is presented to minimize constraint forces and moments at joints of an industrial manipulator. The octahedron point mass model configuration presented offers positive values for equimomental point masses to facilitate link shape formation. Equations are derived to compute point masses and their locations for the rigid links of an industrial manipulator. The flow chart of the teaching–learning-based optimization applicable in solving the manipulators problem is presented and used. The constraint moments at heavily loaded joints are reduced significantly. Moreover, the maximum values of driving torques are also reduced at joints. It is observed that teaching–learning-based optimization gives better results with less computational effort vis-à-vis genetic algorithm for the manipulator optimization problem formulated. The teaching–learning-based optimization algorithm introduced first time for manipulator balancing as optimization solver.

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