scholarly journals Discrete Optimum Design of Planar Steel Curved Roof and Pitched Roof Portal Frames Using Metaheuristic Algorithms

2021 ◽  
Author(s):  
Ali Kaveh ◽  
Mohammad Iman Karimi Dastjerdi ◽  
Ataollah Zaerreza ◽  
Milad Hosseini
Keyword(s):  
Optimal Design ◽  
Optimum Design ◽  
Population Based ◽  
Colliding Bodies Optimization ◽  
Industrial Buildings ◽  
Enhanced Colliding Bodies Optimization ◽  
Teaching Learning Based Optimization ◽  
Fire Stations ◽  
Teaching Learning ◽  
Discrete Optimum

Portal frames are single-story frame buildings including columns and rafters, and their rafters can be either curved or pitched. These are used widely in the construction of industrial buildings, warehouses, gyms, fire stations, agricultural buildings, hangars, etc. The construction cost of these frames considerably depends on their weight. In the present research, the discrete optimum design of two types of portal frames including planar steel Curved Roof Frame (CRF) and Pitched Roof Frame (PRF) with tapered I-section members are presented. The optimal design aims to minimize the weight of these frame structures while satisfying some design constraints based on the requirements of ANSI/AISC 360-16 and ASCE 7-10. Four population-based metaheuristic optimization algorithms are applied to the optimal design of these frames. These algorithms consist of Teaching-Learning-Based Optimization (TLBO), Enhanced Colliding Bodies Optimization (ECBO), Shuffled Shepherd Optimization Algorithm (SSOA), and Water Strider Algorithm (WSA). Two main objectives are followed in this paper. The first one deals with comparing the optimized weight of the CRF and PRF structures with the same dimensions for height and span in two different span lengths (16.0 m and 32.0 m), and the second one is related to comparing the performance of the considered metaheuristics in the optimum design of these portal frames. The obtained results reveal that CRF is more economical than PRF in the fair comparison. Moreover, comparing the results acquired by SSOA with those of other considered metaheuristics reveals that SSOA has better performance for the optimal design of these portal frames.

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

2020 ◽  
Author(s):  
Saeed Hosseinaei ◽  
Mohammad Reza Ghasemi ◽  
Sadegh Etedali
Keyword(s):  
Optimal Design ◽  
Structural Control ◽  
Passive Control ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Control Device ◽  
Feedback Gain ◽  
Active Structural Control ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

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.

scholarly journals Teaching-Learning-Based Optimization with Learning Enthusiasm Mechanism and Its Application in Chemical Engineering

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Xu Chen ◽  
Bin Xu ◽  
Kunjie Yu ◽  
Wenli Du
Keyword(s):  
Real World ◽  
Teaching And Learning ◽  
Chemical Engineering ◽  
Search Algorithm ◽  
Population Based ◽  
Metaheuristic Search ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Real World Problems

Teaching-learning-based optimization (TLBO) is a population-based metaheuristic search algorithm inspired by the teaching and learning process in a classroom. It has been successfully applied to many scientific and engineering applications in the past few years. In the basic TLBO and most of its variants, all the learners have the same probability of getting knowledge from others. However, in the real world, learners are different, and each learner’s learning enthusiasm is not the same, resulting in different probabilities of acquiring knowledge. Motivated by this phenomenon, this study introduces a learning enthusiasm mechanism into the basic TLBO and proposes a learning enthusiasm based TLBO (LebTLBO). In the LebTLBO, learners with good grades have high learning enthusiasm, and they have large probabilities of acquiring knowledge from others; by contrast, learners with bad grades have low learning enthusiasm, and they have relative small probabilities of acquiring knowledge from others. In addition, a poor student tutoring phase is introduced to improve the quality of the poor learners. The proposed method is evaluated on the CEC2014 benchmark functions, and the computational results demonstrate that it offers promising results compared with other efficient TLBO and non-TLBO algorithms. Finally, LebTLBO is applied to solve three optimal control problems in chemical engineering, and the competitive results show its potential for real-world problems.

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.

Optimal Location of TCSC Using Opposition Teaching Learning Based Optimization

2015 ◽  
Vol 4 (1) ◽  
pp. 85-101 ◽  
Author(s):  
Pranabesh Mukhopadhyay ◽  
Susanta Dutta ◽  
Provas Kumar Roy
Keyword(s):  
Power System ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Population Based ◽  
Flow Solution ◽  
Opposition Based Learning ◽  
Optimal Power ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Thyristor Controlled Series Compensator

This paper focuses on the optimal power flow solution and the enhancement of the performance of a power system network. The paper presents a secured optimal power flow solution by integrating Thyristor controlled series compensator (TCSC) with the optimization model developed under overload condition. The Teaching Learning Based Optimization (TLBO) has been implemented here. Recently, the opposition-based learning (OBL) technique has been applied in various conventional population based techniques to improve the convergence performance and get better simulation results. In this paper, opposition-based learning (OBL) has been integrated with teaching learning based optimization (TLBO) to form the opposition teaching learning based optimization (OTLBO). Flexible AC Transmission System (FACTS) devices such as Thyristor controlled series compensator (TCSC) can be very effective for power system security. Numerical results on test systems IEEE 30-Bus with valve point effect is presented and compared with results of other competitive global approaches. The results show that the proposed approach can converge to the optimum solution and obtains the solution with high accuracy.

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