scholarly journals Design Optimization of Mechanical Components Using an Enhanced Teaching-Learning Based Optimization Algorithm with Differential Operator

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
B. Thamaraikannan ◽  
V. Thirunavukkarasu
Keyword(s):  
Differential Operator ◽  
Optimization Problems ◽  
Population Based ◽  
Optimal Solutions ◽  
Hollow Shaft ◽  
Optimization Task ◽  
Tlbo Algorithm ◽  
Teaching Learning Based Optimization ◽  
Mechanical Components ◽  
Teaching Learning

This paper studies in detail the background and implementation of a teaching-learning based optimization (TLBO) algorithm with differential operator for optimization task of a few mechanical components, which are essential for most of the mechanical engineering applications. Like most of the other heuristic techniques, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. A differential operator is incorporated into the TLBO for effective search of better solutions. To validate the effectiveness of the proposed method, three typical optimization problems are considered in this research: firstly, to optimize the weight in a belt-pulley drive, secondly, to optimize the volume in a closed coil helical spring, and finally to optimize the weight in a hollow shaft. have been demonstrated. Simulation result on the optimization (mechanical components) problems reveals the ability of the proposed methodology to find better optimal solutions compared to other optimization algorithms.

scholarly journals Synthesis of Thinned Concentric Circular Antenna Arrays Using Modified TLBO Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Zailei Luo ◽  
Xueming He ◽  
Xuedong Chen ◽  
Xin Luo ◽  
Xiaoqing Li
Keyword(s):  
Antenna Arrays ◽  
Optimization Problems ◽  
Extended Space ◽  
Half Wavelength ◽  
Tlbo Algorithm ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Antenna Array Synthesis ◽  
Circular Antenna Arrays ◽  
Circular Antenna Array

Teaching-learning-based optimization (TLBO) algorithm is a new kind of stochastic metaheuristic algorithm which has been proven effective and powerful in many engineering optimization problems. This paper describes the application of a modified version of TLBO algorithm, MTLBO, for synthesis of thinned concentric circular antenna arrays (CCAAs). The MTLBO is adjusted for CCAA design according to the geometry arrangement of antenna elements. CCAAs with uniform interelement spacing fixed at half wavelength have been considered for thinning using MTLBO algorithm. For practical purpose, this paper demonstrated SLL reduction of thinned CCAAs in the whole regular and extended space other than the phi = 0° plane alone. The uniformly and nonuniformly excited CCAAs have been discussed, respectively, during the simulation process. The proposed MTLBO is very easy to be implemented and requires fewer algorithm specified parameters, which is suitable for concentric circular antenna array synthesis. Numerical results clearly show the superiority of MTLBO algorithm in finding optimum solutions compared to particle swarm optimization algorithm and firefly algorithm.

scholarly journals Bare-Bones Teaching-Learning-Based Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Feng Zou ◽  
Lei Wang ◽  
Xinhong Hei ◽  
Debao Chen ◽  
Qiaoyong Jiang ◽  
...  
Keyword(s):  
Learning Strategy ◽  
Optimization Problems ◽  
Neighborhood Search ◽  
Tlbo Algorithm ◽  
Teaching Learning Based Optimization ◽  
Class Room ◽  
Teaching Learning ◽  
Bare Bones ◽  
Gaussian Sampling ◽  
Better Than

Teaching-learning-based optimization (TLBO) algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI) algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO) is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms.

scholarly journals A Cyclical Non-Linear Inertia-Weighted Teaching–Learning-Based Optimization Algorithm

Algorithms ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 94 ◽  
Author(s):  
Zongsheng Wu ◽  
Ru Xue
Keyword(s):  
Global Optimization ◽  
Local Minimum ◽  
Optimization Problems ◽  
Tlbo Algorithm ◽  
Comparison Results ◽  
Non Linear ◽  
Significant Performance ◽  
Teaching Learning Based Optimization ◽  
Weighted Factor ◽  
Teaching Learning

After the teaching–learning-based optimization (TLBO) algorithm was proposed, many improved algorithms have been presented in recent years, which simulate the teaching–learning phenomenon of a classroom to effectively solve global optimization problems. In this paper, a cyclical non-linear inertia-weighted teaching–learning-based optimization (CNIWTLBO) algorithm is presented. This algorithm introduces a cyclical non-linear inertia weighted factor into the basic TLBO to control the memory rate of learners, and uses a non-linear mutation factor to control the learner’s mutation randomly during the learning process. In order to prove the significant performance of the proposed algorithm, it is tested on some classical benchmark functions and the comparison results are provided against the basic TLBO, some variants of TLBO and some other well-known optimization algorithms. The experimental results show that the proposed algorithm has better global search ability and higher search accuracy than the basic TLBO, some variants of TLBO and some other algorithms as well, and can escape from the local minimum easily, while keeping a faster convergence rate.

scholarly journals An Improved Teaching-Learning-Based Optimization with Differential Learning and Its Application

2015 ◽  
Vol 2015 ◽  
pp. 1-19 ◽  
Author(s):  
Feng Zou ◽  
Lei Wang ◽  
Debao Chen ◽  
Xinhong Hei
Keyword(s):  
Learning Strategy ◽  
Population Based ◽  
Neighborhood Search ◽  
New Mutation ◽  
Iir Filter ◽  
Differential Learning ◽  
Tlbo Algorithm ◽  
Teaching Learning Based Optimization ◽  
Digital Iir Filter ◽  
Teaching Learning

The teaching-learning-based optimization (TLBO) algorithm is a population-based optimization algorithm which is based on the effect of the influence of a teacher on the output of learners in a class. A variant of teaching-learning-based optimization (TLBO) algorithm with differential learning (DLTLBO) is proposed in the paper. In this method, DLTLBO utilizes a learning strategy based on neighborhood search of teacher phase in the standard TLBO to generate a new mutation vector, while utilizing a differential learning to generate another new mutation vector. Then DLTLBO employs the crossover operation to generate new solutions so as to increase the diversity of the population. By the integration of the local search and the global search, DLTLBO achieves a tradeoff between exploration and exploitation. To demonstrate the effectiveness of our approaches, 24 benchmark functions are used for simulating and testing. Moreover, DLTLBO is used for parameter estimation of digital IIR filter and experimental results show that DLTLBO is superior or comparable to other given algorithms for the employed examples.

scholarly journals LESSENING OF ACTUAL POWER LOSS BY MODIFIED ALGORITHM

2018 ◽  
Vol 6 (8) ◽  
pp. 159-167
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Power Flow ◽  
Reactive Power ◽  
Optimization Problems ◽  
High Concentration ◽  
Reactive Power Flow ◽  
Tlbo Algorithm ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Power Flow Problem

This paper presents a Modified Teaching-Learning-Based Optimization (MTLBO) algorithm for solving reactive power flow problem. Basic Teaching-Learning-Based Optimization (TLBO) is reliable, accurate and vigorous for solving the optimization problems. Also, it has been found that TLBO algorithm slow in convergence due to its high concentration in the accuracy. This paper presents an, Modified version of TLBO algorithm, called as Modified Teaching-Learning-Based Optimization (MTLBO). A parameter called as “weight” has been included in the fundamental TLBO equations & subsequently it increases the rate of convergence. In order to evaluate the proposed algorithm, it has been tested in practical 191 test bus system. Simulation results reveal about the better performance of the proposed algorithm in reducing the real power loss & voltage profiles are within the limits.

scholarly journals Minimization of real power loss by enhanced teaching learning based optimization algorithm

2020 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Power Flow ◽  
Reactive Power ◽  
Optimization Problems ◽  
High Concentration ◽  
Real Power ◽  
Reactive Power Flow ◽  
Tlbo Algorithm ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

<p class="Abstract">This paper presents an Enhanced Teaching-Learning-Based Optimization (ETLBO) algorithm for solving reactive power flow problem. Basic Teaching-Learning-Based Optimization (TLBO) is reliable, accurate and vigorous for solving the optimization problems. Also it has been found that TLBO algorithm slow in convergence due to its high concentration in the accuracy. This paper presents an, enhanced version of TLBO algorithm, called as enhanced Teaching-Learning-Based Optimization (ETLBO). A parameter called as “weight” has been included in the fundamental TLBO equations &amp; subsequently it increases the rate of convergence. In order to evaluate the proposed algorithm, it has been tested in Standard IEEE 57,118 bus systems and compared to other standard reported algorithms. Simulation results reveal about the better performance of the proposed algorithm in reducing the real power loss &amp; voltage profiles are within the limits.</p><p> </p>

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

2013 ◽  
Vol 380-384 ◽  
pp. 1342-1345 ◽  
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.

scholarly journals Population based optimization algorithms improvement using the predictive particles

2020 ◽  
Vol 10 (3) ◽  
pp. 3261
Author(s):  
M. M. H. Elroby ◽  
S. F. Mekhamer ◽  
H. E. A. Talaat ◽  
M. A. Moustafa Hassan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithms ◽  
Pso Algorithm ◽  
Population Based ◽  
The Other ◽  
Swarm Optimization ◽  
Tlbo Algorithm ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Basic Philosophy

A new efficient improvement, called Predictive Particle Modification (PPM), is proposed in this paper. This modification makes the particle look to the near area before moving toward the best solution of the group. This modification can be applied to any population algorithm. The basic philosophy of PPM is explained in detail. To evaluate the performance of PPM, it is applied to Particle Swarm Optimization (PSO) algorithm and Teaching Learning Based Optimization (TLBO) algorithm then tested using 23 standard benchmark functions. The effectiveness of these modifications are compared with the other unmodified population optimization algorithms based on the best solution, average solution, and convergence rate.

scholarly journals An Improved Teaching-Learning-Based Optimization with the Social Character of PSO for Global Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Feng Zou ◽  
Debao Chen ◽  
Jiangtao Wang
Keyword(s):  
Optimization Problems ◽  
Global Solutions ◽  
Function Optimization ◽  
Improved Method ◽  
Social Character ◽  
The Social ◽  
Teaching Learning Based Optimization ◽  
The Mean ◽  
The Individual ◽  
Teaching Learning

An improved teaching-learning-based optimization with combining of the social character of PSO (TLBO-PSO), which is considering the teacher’s behavior influence on the students and the mean grade of the class, is proposed in the paper to find the global solutions of function optimization problems. In this method, the teacher phase of TLBO is modified; the new position of the individual is determined by the old position, the mean position, and the best position of current generation. The method overcomes disadvantage that the evolution of the original TLBO might stop when the mean position of students equals the position of the teacher. To decrease the computation cost of the algorithm, the process of removing the duplicate individual in original TLBO is not adopted in the improved algorithm. Moreover, the probability of local convergence of the improved method is decreased by the mutation operator. The effectiveness of the proposed method is tested on some benchmark functions, and the results are competitive with respect to some other methods.

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