Optimal Design of Passive and Active Control Systems in Seismic-excited Structures Using a New Modified TLBO

Vibration control devices have recently been used in structures subjected to wind and earthquake excitations. The optimal design problems of the passive control device and the feedback gain matrix of the controller for the seismic-excited structures are some attractive problems for researches to develop optimization algorithms with the advancement in terms of simplicity, accuracy, speed, and efficacy. In this paper, a new modified teaching–learning-based optimization (TLBO) algorithm, known as MTLBO, is proposed for the problems. For some benchmark optimization functions and constrained engineering problems, the validity, efficacy, and reliability of the MTLBO are firstly assessed and compared to other optimization algorithms in the literature. The undertaken statistical indicate that the MTLBO performs better and reliable than some other algorithms studied here. The performance of the MTLBO will then be explored for two passive and active structural control problems. It is concluded that the MTLBO algorithm is capable of giving better results than conventional TLBO. Hence, its utilization as a simple, fast, and powerful optimization tool to solve particular engineering optimization problems is recommended.

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Application of PSO and TLBO algorithms with neural network for optimal design of electrical machines

COMPEL The International Journal for Computation and Mathematics in Electrical and Electronic Engineering ◽

10.1108/compel-12-2016-0532 ◽

2018 ◽

Vol 37 (2) ◽

pp. 549-564 ◽

Cited By ~ 2

Author(s):

Bourahla Kheireddine ◽

Belli Zoubida ◽

Hacib Tarik ◽

Achoui Imed

Keyword(s):

Optimal Design ◽

Optimization Problems ◽

Computation Time ◽

Electrical Machines ◽

Neuron Network ◽

Element Analysis ◽

Content Type ◽

Switched Reluctance ◽

Teaching Learning Based Optimization ◽

Teaching Learning

PurposeThis study aims to focus on the application of the stochastic algorithms for optimal design of electrical machines. Among them, the authors are interested in particle swarm optimization and teaching–learning-based optimization.Design/methodology/approachThe optimization process is realized by the coupling of the above methods to finite element analysis of the electromagnetic field.FindingsTo improve the performance of these algorithms and reduce their computation time, a coupling with the artificial neuron network has been realized.Originality/valueThe proposed strategy is applied to solve two optimization problems: Team workshop problem 25 and switched reluctance motor with flux barriers.

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MLO: Multi Leader Optimizer

International Journal of Intelligent Engineering and Systems ◽

10.22266/ijies2020.1231.32 ◽

2020 ◽

Vol 13 (6) ◽

pp. 364-373

Author(s):

Mohammad Dehghani ◽

◽

Zeinab Montazeri ◽

Ali Dehghani ◽

Ricardo Ramirez-Mendoza ◽

...

Keyword(s):

Optimization Problems ◽

Optimization Algorithms ◽

Emperor Penguin ◽

Gray Wolf ◽

Test Functions ◽

Gray Wolf Optimizer ◽

Grasshopper Optimization Algorithm ◽

Teaching Learning Based Optimization ◽

Grasshopper Optimization ◽

Teaching Learning

Optimization is a topic that has always been discussed in all different fields of science. One of the most effective techniques for solving such problems is optimization algorithms. In this paper, a new optimizer called Multi-Leader optimizer (MLO) is developed in which multiple leaders guide members of the population towards the optimal answer. MLO is mathematically modelled based on the process of advancing members of the population and following the leaders. MLO performance in optimization is examined on twenty-three standard objective functions. The results of this optimization are compared with the results of the other eight existing optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Teaching-Learning-Based Optimization (TLBO), Gray Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), Emperor Penguin Optimizer (EPO), Shell Game Optimization (SGO), and Hide Objects Game Optimization (HOGO). Based on the analysis of the simulation results on unimodal test functions to evaluate exploitation ability and multimodal test functions in order to evaluate exploration ability, it has been determined that MLO has a higher ability to solve optimization problems than existing optimization algorithms.

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DM: Dehghani Method for Modifying Optimization Algorithms

Applied Sciences ◽

10.3390/app10217683 ◽

2020 ◽

Vol 10 (21) ◽

pp. 7683 ◽

Cited By ~ 1

Author(s):

Mohammad Dehghani ◽

Zeinab Montazeri ◽

Ali Dehghani ◽

Haidar Samet ◽

Carlos Sotelo ◽

...

Keyword(s):

Optimization Problems ◽

Search Algorithm ◽

Optimization Algorithms ◽

Gravitational Search Algorithm ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Objective Functions ◽

Living Things ◽

Teaching Learning Based Optimization ◽

Teaching Learning

In recent decades, many optimization algorithms have been proposed by researchers to solve optimization problems in various branches of science. Optimization algorithms are designed based on various phenomena in nature, the laws of physics, the rules of individual and group games, the behaviors of animals, plants and other living things. Implementation of optimization algorithms on some objective functions has been successful and in others has led to failure. Improving the optimization process and adding modification phases to the optimization algorithms can lead to more acceptable and appropriate solution. In this paper, a new method called Dehghani method (DM) is introduced to improve optimization algorithms. DM effects on the location of the best member of the population using information of population location. In fact, DM shows that all members of a population, even the worst one, can contribute to the development of the population. DM has been mathematically modeled and its effect has been investigated on several optimization algorithms including: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), teaching-learning-based optimization (TLBO), and grey wolf optimizer (GWO). In order to evaluate the ability of the proposed method to improve the performance of optimization algorithms, the mentioned algorithms have been implemented in both version of original and improved by DM on a set of twenty-three standard objective functions. The simulation results show that the modified optimization algorithms with DM provide more acceptable and competitive performance than the original versions in solving optimization problems.

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Modified teaching-learning based optimization for 0–1 knapsack optimization problems

2017 29th Chinese Control And Decision Conference (CCDC) ◽

10.1109/ccdc.2017.7978660 ◽

2017 ◽

Cited By ~ 1

Author(s):

Haibin Ouyang ◽

Qing Wang ◽

Xiangyong Kong

Keyword(s):

Optimization Problems ◽

Teaching Learning Based Optimization ◽

Teaching Learning

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

Computational Intelligence and Neuroscience ◽

10.1155/2016/4561507 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 5

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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