Multiple View Relations Using the Teaching and Learning-Based Optimization Algorithm

In computer vision, estimating geometric relations between two different views of the same scene has great importance due to its applications in 3D reconstruction, object recognition and digitization, image registration, pose retrieval, visual tracking and more. The Random Sample Consensus (RANSAC) is the most popular heuristic technique to tackle this problem. However, RANSAC-like algorithms present a drawback regarding either the tuning of the number of samples and the threshold error or the computational burden. To relief this problem, we propose an estimator based on a metaheuristic, the Teaching–Learning-Based Optimization algorithm (TLBO) that is motivated by the teaching–learning process. We use the TLBO algorithm in the problem of computing multiple view relations given by the homography and the fundamental matrix. To improve the method, candidate models are better evaluated with a more precise objective function. To validate the efficacy of the proposed approach, several tests, and comparisons with two RANSAC-based algorithms and other metaheuristic-based estimators were executed.

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Optimal dynamic design of planar mechanisms using teaching–learning-based optimization algorithm

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406215612831 ◽

2016 ◽

Vol 230 (19) ◽

pp. 3442-3456 ◽

Cited By ~ 4

Author(s):

Kailash Chaudhary ◽

Himanshu Chaudhary

Keyword(s):

Optimization Algorithm ◽

Evolutionary Optimization ◽

Planar Mechanisms ◽

Dynamic Design ◽

Computational Performance ◽

Tlbo Algorithm ◽

Teaching Learning Based Optimization ◽

Optimal Dynamic ◽

Teaching Learning ◽

Shaking Moment

A two-stage optimization method for optimal dynamic design of planar mechanisms is presented in this paper. For dynamic balancing, minimization of the shaking force and the shaking moment is achieved by finding optimum mass distribution of mechanism links using the equimomental system of point-masses in the first stage of the optimization. In the second stage, their shapes are synthesized systematically by closed parametric curve, i.e. cubic B-spline curve corresponding to the optimum inertial parameters found in the first stage. The multi-objective optimization problem to minimize both the shaking force and the shaking moment is solved using evolutionary optimization algorithm – “Teaching-learning-based optimization (TLBO) algorithm”. The computational performance of TLBO algorithm is compared with another evolutionary optimization algorithm, i.e. genetic algorithm.

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Real Power Loss Reduction by Enhanced Trailblazer Optimization Algorithm

Herald of the Bauman Moscow State Technical University Series Natural Sciences ◽

10.18698/1812-3368-2021-3-77-93 ◽

2021 ◽

pp. 77-93

Author(s):

K. Lenin

Keyword(s):

Optimization Algorithm ◽

Power Loss ◽

Local Optimum ◽

Loss Reduction ◽

Exploration And Exploitation ◽

Real Power ◽

Tlbo Algorithm ◽

Power Loss Reduction ◽

Teaching Learning Based Optimization ◽

Teaching Learning

In this paper Teaching learning based Trailblazer optimization algorithm (TLBOTO) is used for solving the power loss lessening problem. Trailblazer optimization algorithm (TOA) is alienated into dual phases for exploration: trailblazer phase and adherent phase. Both phases epitomize the exploration and exploitation phase of TOA correspondingly. Nevertheless, in order to avoid the solution falling in local optimum in this paper Teaching-learning-based optimization (TLBO) is integrated with TOA approach. Learning segment of the TLBO algorithm is added to the adherent phase. Proposed Teaching learning based Trailblazer optimization algorithm (TLBOTO) augment exploration capability of the algorithm and upsurge the convergence speed. Algorithm's exploration competences enhanced by linking the teaching phase and learning. Exploration segment of the trailblazer algorithm identifies the zone with the pre-eminent solution. Subsequently inducing the teaching process, the trailblazer performs as a teacher to teach additional entities and engender a new-fangled entity. The new-fangled unit is equated with the trailblazer, and with reference to the greedy selection norm, the optimal one is designated as the trailblazer to endure exploration. The location of trailblazer is modernized. Legitimacy of the Teaching learning based Trailblazer optimization algorithm (TLBOTO) is substantiated in IEEE 30 bus system (with and devoid of L-index). Actual power loss lessening is reached. Proportion of actual power loss lessening is augmented

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Teaching-Learning-Based Optimization Algorithm for Dealing with Real-Parameter Optimization Problems

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.380-384.1342 ◽

2013 ◽

Vol 380-384 ◽

pp. 1342-1345 ◽

Cited By ~ 1

Author(s):

Kai Lin Wang ◽

Hui Bin Wang ◽

Li Xia Yu ◽

Xue Yu Ma ◽

Yun Sheng Xue

Keyword(s):

Parameter Optimization ◽

Optimization Algorithm ◽

Optimization Problems ◽

Real Parameter ◽

Experimental Comparison ◽

Practical Applications ◽

Evaluation Functions ◽

Tlbo Algorithm ◽

Teaching Learning Based Optimization ◽

Teaching Learning

A latest optimization algorithm, named Teaching-Learning-Based Optimization (simply TLBO) was proposed by R. V. Rao et al, at 2011. Afterwards, some improvements and practical applications have been conducted toward TLBO algorithm. However, as far as our knowledge, there are no such works which categorize the current works concerning TLBO from the algebraic and analytic points of view. Hence, in this paper we firstly introduce the concepts and algorithms of TLBO, then survey the running mechanism of TLBO for dealing with the real-parameter optimization problems, and finally group its real-world applications with a categorizing framework based on the clustering, multi-objective optimization, parameter optimization, and structure optimization. The main advantage of this work is to help the users employ TLBO without knowing details of this algorithm. Meanwhile, we also give an experimental comparison for demonstrating the effectiveness of TLBO on 5 benchmark evaluation functions and conclude this work by identifying trends and challenges of TLBO research and development.

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Improved Teaching-Learning-Based Optimization Algorithm and its Application in PID Parameter Optimization

International Journal of Cognitive Informatics and Natural Intelligence ◽

10.4018/ijcini.2019040101 ◽

2019 ◽

Vol 13 (2) ◽

pp. 1-17

Author(s):

Fahui Gu ◽

Wenxiang Wang ◽

Luyan Lai

Keyword(s):

Parameter Optimization ◽

Optimization Algorithm ◽

Adaptive Learning ◽

Teaching And Learning ◽

Optimization Problems ◽

Learning Algorithm ◽

Global Search ◽

Opposition Based Learning ◽

Teaching Learning Based Optimization ◽

Teaching Learning

The teaching-learning-based optimization (TLBO) algorithm has been applied to many optimization problems, but its theoretical basis is relatively weak, its control parameters are difficult to choose, and it converges slowly in the late period and makes it too early to mature. To overcome these shortcomings, this article proposes a dual-population co-evolution teaching and learning optimization algorithm (DPCETLBO) in which adaptive learning factors and a multi-parent non-convex hybrid elite strategy are introduced for a population with high fitness values to improve the convergence speed of the algorithm, while an opposition-based learning algorithm with polarization is introduced for a population with lower fitness values to improve the global search ability of the algorithm. In a proportion integration differentiation (PID) parameter optimization experiment, the simulation results indicate that the convergence of the DPCETLBO algorithm is fast and precise, and its global search ability is superior to those of the TLBO, ETLBO and PSO algorithms.

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