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 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 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 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 Nonlinear Inertia Weighted Teaching-Learning-Based Optimization for Solving Global Optimization Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Zong-Sheng Wu ◽  
Wei-Ping Fu ◽  
Ru Xue
Keyword(s):  
Global Optimization ◽  
Optimization Problem ◽  
Random Number ◽  
Nonlinear Problems ◽  
Tlbo Algorithm ◽  
Teaching Learning Based Optimization ◽  
Linear And Nonlinear ◽  
Weighted Factor ◽  
Teaching Learning ◽  
Dynamic Inertia

Teaching-learning-based optimization (TLBO) algorithm is proposed in recent years that simulates the teaching-learning phenomenon of a classroom to effectively solve global optimization of multidimensional, linear, and nonlinear problems over continuous spaces. In this paper, an improved teaching-learning-based optimization algorithm is presented, which is called nonlinear inertia weighted teaching-learning-based optimization (NIWTLBO) algorithm. This algorithm introduces a nonlinear inertia weighted factor into the basic TLBO to control the memory rate of learners and uses a dynamic inertia weighted factor to replace the original random number in teacher phase and learner phase. The proposed algorithm is tested on a number of benchmark functions, and its performance comparisons are provided against the basic TLBO and some other well-known optimization algorithms. The experiment results show that the proposed algorithm has a faster convergence rate and better performance than the basic TLBO and some other algorithms as well.

An improved teaching–learning-based optimization with neighborhood search for applications of ANN

2014 ◽  
Vol 143 ◽  
pp. 231-247 ◽  
Author(s):  
Lei Wang ◽  
Feng Zou ◽  
Xinhong Hei ◽  
Dongdong Yang ◽  
Debao Chen ◽  
...  
Keyword(s):  
Neighborhood Search ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

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.

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