Optimization of cylindrical wall domes via metaheuristic algorithms

2021 ◽  
Vol 7 (4) ◽  
pp. 180
Author(s):  
Aylin Ece Kayabekir
Keyword(s):  
Structural Engineering ◽  
Performance Comparison ◽  
Metaheuristic Algorithms ◽  
Optimization Process ◽  
Cylindrical Wall ◽  
Optimum Result ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Two Hybrid ◽  
Usage Area

Optimization is a widely used phenomenon in various problems and fields. Because time and resources are very limited in today's world, it can be said that the usage area of the optimization process will be expanded and spread in all areas of life. Although different methods are used in the realization of the optimization process, the performance of metaheuristic algorithms in solving problems has led to an increase in research on these methods. As in other fields, the application examples of these algorithms are diversifying and increasing in the field of structural engineering. In this study, the performance comparison of five different algorithms for the optimum design of an axisymmetric cylindrical wall with a dome is investigated. These algorithms are Jaya (JA), Flower pollination (FPA), teaching-learning-based optimization (TLBO) algorithms and two hybrid versions of these algorithms. ACI 318 regulation was used in reinforced concrete design with a flexibility method-based approach in the analyses. In the analyzes with five different situations of the wall height, some statistical values , and data of analysis numbers were obtained by running the algorithms a large number of times. According to the analysis results, Jaya algorithm is slightly better in terms of the speed of reaching the optimum result, but also all algorithms are quite effective and reliable in solving the problem.

An MPPT method using hybrid radial movement optimization with teaching-learning based optimization under fluctuating atmospheric conditions

2021 ◽  
pp. 1-10
Author(s):  
Imran Pervez ◽  
Adil Sarwar ◽  
Afroz Alam ◽  
Mohammad ◽  
Ripon K. Chakrabortty ◽  
...  
Keyword(s):  
Maximum Power ◽  
Metaheuristic Algorithms ◽  
Solar Pv ◽  
Maximum Power Point ◽  
Local Optima ◽  
Pv Array ◽  
Radial Movement ◽  
Teaching Learning Based Optimization ◽  
Power Point ◽  
Teaching Learning

Due to its clean and abundant availability, solar energy is popular as a source from which to generate electricity. Solar photovoltaic (PV) technology converts sunlight incident on the solar PV panel or array directly into non-linear DC electricity. However, the non-linear nature of the solar panels’ power needs to be tracked for its efficient utilization. The problem of non-linearity becomes more prominent when the solar PV array is shaded, even leading to high power losses and concentrated heating in some areas (hotspot condition) of the PV array. Bypass diodes used to eliminate the shading effect cause multiple peaks of power on the power versus voltage (P-V) curve and make the tracking problem quite complex. Conventional algorithms to track the optimal power point cannot search the complete P-V curve and often become trapped in local optima. More recently, metaheuristic algorithms have been employed for maximum power point tracking. Being stochastic, these algorithms explore the complete search area, thereby eliminating any chance of becoming trapped stuck in local optima. This paper proposes a hybridized version of two metaheuristic algorithms, Radial Movement Optimization and teaching-learning based optimization (RMOTLBO). The algorithm has been discussed in detail and applied to multiple shading patterns in a solar PV generation system. It successfully tracks the maximum power point (MPP) in a lesser amount of time and lesser fluctuations.

Optimum Design of Reinforced Concrete Retaining Walls

2020 ◽  
pp. 161-182
Keyword(s):  
Reinforced Concrete ◽  
Optimum Design ◽  
Optimization Algorithm ◽  
Computer Code ◽  
Retaining Walls ◽  
Optimization Process ◽  
Stability Conditions ◽  
Teaching Learning Based Optimization ◽  
The Stability ◽  
Teaching Learning

In this chapter, the optimization of reinforced concrete (RC) retaining walls is presented. RC retaining walls are one of the structural types that are constructed on land and used for retaining soil backfill. Because of this reason, both structural and geotechnical limits are in progress in the optimization process. Additionally, the stability conditions against pressure of soils are the key constraints in the optimum design of RC retaining walls. The presented methodology in this chapter considers both static and dynamic soil pressures resulting from earthquakes. A computer code employing teaching-learning-based optimization algorithm is also given.

scholarly journals Applications of Metaheuristic Algorithms in Solar Air Heater Optimization: A Review of Recent Trends and Future Prospects

2021 ◽  
Vol 2021 ◽  
pp. 1-36
Author(s):  
Jean De Dieu Niyonteze ◽  
Fumin Zou ◽  
Godwin Norense Osarumwense Asemota ◽  
Walter Nsengiyumva ◽  
Noel Hagumimana ◽  
...  
Keyword(s):  
Solar Energy ◽  
Fossil Fuels ◽  
Energy Systems ◽  
Industrial Applications ◽  
Metaheuristic Algorithms ◽  
Solar Air Heater ◽  
Bee Colony ◽  
Metaheuristic Methods ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

A transition to solar energy systems is considered one of the most important alternatives to conventional fossil fuels. Until recently, solar air heaters (SAHs) were among the other solar energy systems that have been widely used in various households and industrial applications. However, the recent literature reveals that efficiencies of SAHs are still low. Some metaheuristic algorithms have been used to enhance the efficiencies of these SAH systems. In the paper, we do not only discuss the techniques used to enhance the performance of SAHs, but we also reviewed a majority of published papers on the applications of SAH optimization. The metaheuristic algorithms include simulated annealing (SA), particle swarm optimization (PSO), genetic algorithm (GA), artificial bee colony (ABC), teaching-learning-based optimization (TLBO), and elitist teaching-learning-based optimization (ETLBO). For this research, it should be noted that this study is mostly based on the literature published in the last ten years in good energy top journals. Therefore, this paper clearly shows that the use of all six proposed metaheuristic algorithms results in significant efficiency improvements through the selection of the optimal design set and operating parameters for SAHs. Based on the past literature and on the outcomes of this paper, ETLBO is unquestionably more competitive than ABC, GA, PSO, SA, and TLBO for the optimization of SAHs for the same considered problem. Finally, based on the covered six state-of-the-art metaheuristic techniques, some perspectives and recommendations for the future outlook of SAH optimization are proposed. This paper is the first-ever attempt to present the current developments to a large audience on the applications of metaheuristic methods in SAH optimization. Thus, researchers can use this paper for further research and for the advancement of the proposed and other recommended algorithms to generate the best performance for the various SAHs.

scholarly journals OPTIMIZATION OF MULTI-PASS FACE MILLING PARAMETERS USING METAHEURISTIC ALGORITHMS

2019 ◽  
Vol 17 (3) ◽  
pp. 365 ◽  
Author(s):  
Sunny Diyaley ◽  
Shankar Chakraborty
Keyword(s):  
Statistical Tests ◽  
Cutting Speed ◽  
Milling Process ◽  
Face Milling ◽  
Optimization Techniques ◽  
Metaheuristic Algorithms ◽  
Depth Of Cut ◽  
Total Production ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

In this paper, six metaheuristic algorithms, in the form of artificial bee colony optimization, ant colony optimization, particle swarm optimization, differential evolution, firefly algorithm and teaching-learning-based optimization techniques are applied for parametric optimization of a multi-pass face milling process. Using those algorithms, the optimal values of cutting speed, feed rate and depth of cut for both roughing and finishing operations are determined for having minimum total production time and total production cost. It is observed that the teaching-learning-based optimization algorithm outperforms the others with respect to accuracy and consistency of the derived solutions as well as computational speed. Two statistical tests, i.e. paired t-test and Wilcoxson signed rank test also confirm its superiority over the remaining algorithms. Finally, these metaheuristics are employed for multi-objective optimization of the considered multi-pass milling process while concurrently minimizing both the objectives.

TLBO-FLN: Teaching-Learning Based Optimization of Functional Link Neural Networks for Stock Closing Price Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 522-532 ◽  
Author(s):  
Sarat Chandra Nayak ◽  
Subhranginee Das ◽  
Mohammad Dilsad Ansari
Keyword(s):  
Neural Networks ◽  
Computational Cost ◽  
Optimization Techniques ◽  
Fine Tuning ◽  
Functional Link ◽  
Price Prediction ◽  
Closing Price ◽  
Teaching Learning Based Optimization ◽  
Artificial Neural ◽  
Teaching Learning

Background and Objective: Stock closing price prediction is enormously complicated. Artificial Neural Networks (ANN) are excellent approximation algorithms applied to this area. Several nature-inspired evolutionary optimization techniques are proposed and used in the literature to search the optimum parameters of ANN based forecasting models. However, most of them need fine-tuning of several control parameters as well as algorithm specific parameters to achieve optimal performance. Improper tuning of such parameters either leads toward additional computational cost or local optima. Methods: Teaching Learning Based Optimization (TLBO) is a newly proposed algorithm which does not necessitate any parameters specific to it. The intrinsic capability of Functional Link Artificial Neural Network (FLANN) to recognize the multifaceted nonlinear relationship present in the historical stock data made it popular and got wide applications in the stock market prediction. This article presents a hybrid model termed as Teaching Learning Based Optimization of Functional Neural Networks (TLBO-FLN) by combining the advantages of both TLBO and FLANN. Results and Conclusion: The model is evaluated by predicting the short, medium, and long-term closing prices of four emerging stock markets. The performance of the TLBO-FLN model is measured through Mean Absolute Percentage of Error (MAPE), Average Relative Variance (ARV), and coefficient of determination (R2); compared with that of few other state-of-the-art models similarly trained and found superior.

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