Numerous engineering applications involve ensuring the proper functioning of systems, minimizing errors, and optimizing the system and its subcomponents. Achieving desirable outcomes often requires enhancing positive ...
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Numerous engineering applications involve ensuring the proper functioning of systems, minimizing errors, and optimizing the system and its subcomponents. Achieving desirable outcomes often requires enhancing positive factors through optimization methods while mitigating negative factors. In this context, metaheuristic algorithms are favored to find solutions aligned with the intended objectives. Among such algorithms, teaching-learningbasedoptimization (TLBO) and Genetic algorithm (GA) stand out, drawing inspiration from real-life processes. This study focuses on applying the TLBO algorithm to optimize the reliability of linear k-out-of-n: F and G (lin/k/n: F and lin/k/n:G) and linear consecutive k-out-of-n: F and G (lin/con/k/n:F and lin/con/k/n:G) systems. Additionally, the system was analyzed using GA, and the results from both approaches were compared. By employing these powerful metaheuristic algorithms, we aim to attain effective and robust solutions for enhancing system reliability and performance. Also, this study can be a guide in terms of contributing to the reduction of costs by ensuring more efficient use of resources, especially in complex systems. It can also increase productivity by reducing labor by ensuring the efficient operation of machines and processes.
Purpose This paper aims to deal with the development of a newly improved version of teachinglearningbasedoptimization (TLBO) algorithm. Design/methodology/approach Random local search part was added to the classic ...
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Purpose This paper aims to deal with the development of a newly improved version of teachinglearningbasedoptimization (TLBO) algorithm. Design/methodology/approach Random local search part was added to the classic optimization process with TLBO. The new version is called TLBO algorithm with random local search (TLBO-RLS). Findings At first step and to validate the effectiveness of the new proposed version of the TLBO algorithm, it was applied to a set of two standard benchmark problems. After, it was used jointly with two-dimensional non-linear finite element method to solve the TEAM workshop problem 25, where the results were compared with those resulting from classical TLBO, bat algorithm, hybrid TLBO, Nelder-Mead simplex method and other referenced work. Originality value New TLBO-RLS proposed algorithm contains a part of random local search, which allows good exploitation of the solution space. Therefore, TLBO-RLS provides better solution quality than classic TLBO.
PurposeThis paper aims to deal with the development of a newly improved version of teachinglearningbasedoptimization (TLBO) ***/methodology/approachRandom local search part was added to the classic optimization pro...
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PurposeThis paper aims to deal with the development of a newly improved version of teachinglearningbasedoptimization (TLBO) ***/methodology/approachRandom local search part was added to the classic optimization process with TLBO. The new version is called TLBO algorithm with random local search (TLBO-RLS).FindingsAt first step and to validate the effectiveness of the new proposed version of the TLBO algorithm, it was applied to a set of two standard benchmark problems. After, it was used jointly with two-dimensional non-linear finite element method to solve the TEAM workshop problem 25, where the results were compared with those resulting from classical TLBO, bat algorithm, hybrid TLBO, Nelder–Mead simplex method and other referenced *** valueNew TLBO-RLS proposed algorithm contains a part of random local search, which allows good exploitation of the solution space. Therefore, TLBO-RLS provides better solution quality than classic TLBO.
In the present work, the minimization of total manufacturing cost of an assembly considering tolerance allocation using various optimizationalgorithms have been addressed. Complex assembly which consists of more than...
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In the present work, the minimization of total manufacturing cost of an assembly considering tolerance allocation using various optimizationalgorithms have been addressed. Complex assembly which consists of more than one critical dimension with any one dimension of the part involved in more than one dimensional chain has been considered in this work. The manufacturing cost of the various parts of an assembly with tolerance allocation has been minimized using the cost tolerance model. The benchmark solution to the problem has been derived using Lagrange Multiplier (LM) method. The results are improved using Genetic algorithm (GA) and Artificial Bee Colony (ABC) algorithm by considering the Alternate Nominal Dimension Selection (ANDS). The results have been compared with LM method and found GA outperformed ABC algorithm. Further the research has been extended by considering ANDS and Alternate Process Selection (APS) using teaching-learningbasedoptimization (TLBO) algorithm and the results have been compared with benchmark solutions. The proposed method is suitable for any type of complex assembly. An Improved Knuckle Joint (IKJ) assembly has been chosen for the illustration of proposed methodology with an implementation strategy. From the results, it was noted that TLBO yielded better results in the aspect of minimizing the total manufacturing cost of IKJ assemblies.
Bitumen, aggregate, and air void (VA) are the three primary ingredients of asphalt concrete. VA changes over time as a function of four factors: traffic loads and repetitions, environmental regimes, compaction, and as...
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Bitumen, aggregate, and air void (VA) are the three primary ingredients of asphalt concrete. VA changes over time as a function of four factors: traffic loads and repetitions, environmental regimes, compaction, and asphalt mix composition. Due to the high as-constructed VA content of the material, it is expected that VA will reduce over time, causing rutting during initial traffic periods. Eventually, the material will undergo shear flow when it reaches its densest state with optimum aggregate interlock or refusal VA content. Therefore, to ensure the quality of construction, VA in asphalt mixture need to be modeled throughout the service life. This study aims to implement a hybrid evolutionary polynomial regression (EPR) combined with a teaching-learningbasedoptimization (TLBO) algorithm and multi-gene genetic programming (MGGP) to predict the VA percentage of asphalt mixture during the service life. For this purpose, 324 data records of VA were collected from the literature. The variables selected as inputs were original as-constructed VA, VA orig \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${VA}_{orig}$$\end{document} (%);mean annual air temperature, MAAT \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MAAT$$\end{document} (degrees F);original viscosity at 77 degrees F, eta o r i g , 77 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\eta }_{orig,77}$$\end{document} (Mega-Poises);and time \documentclass[12pt]{minimal} \use
The problems of fuel reloading optimization is a complex multi-objective discrete optimization problem with multiple local optimums. In recent years, great process has been made in solving these problems by using meta...
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The problems of fuel reloading optimization is a complex multi-objective discrete optimization problem with multiple local optimums. In recent years, great process has been made in solving these problems by using meta heuristic optimizationalgorithms. teaching-learning based optimization algorithm (TLBO) is one kind of novel meta-heuristic optimizationalgorithms. However, it is seldom used to solve the problems of fuel reloading optimization, because its original purpose is to solve continuous optimization problems. In this paper, a hybrid teaching-learning genetic algorithm (HTLGA) is developed, which could be directly applied to solve the problems of fuel reloading optimization. This hybrid algorithm takes TLBO as main part, combines three operators of genetic algorithms (GA) which are coding, crossover and mutation. The optimization solutions which are represented as students in TLBO are further divided into top students, ordinary students and poor students in HTLGA. The calculation phases "Teacher phase" and "Learner phase" in TLBO are improved into "Teacher phase", "Discussion phase" and "Self-study phase" in HTLGA. For testing the optimization ability of HTLGA, it is applied to solve the problems of fuel reloading optimization for the 1/6 core of thorium-based block-type HTGRs. The results showed that the developed HTLGA has more powerful optimization ability than TLBO and GA.
In full illumination conditions, there is a distinct maximum power point (MPP) in the power-voltage curve of a photovoltaic system, which can be found using a conventional algorithm, such as the perturb and observe (P...
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ISBN:
(纸本)9781665403665
In full illumination conditions, there is a distinct maximum power point (MPP) in the power-voltage curve of a photovoltaic system, which can be found using a conventional algorithm, such as the perturb and observe (P&O) method. However, the irradiation reduces due to the presence of shading conditions on the PV panels. Hence, multiple local MPPs (LMPP) may arise while there is only one global MPP (GMPP). An evolutionary algorithm is required for accurate tracking of the true GMPP. In this paper, an MPP Tracking (MPPT) controller design using teachinglearningbasedoptimization (TLBO) for photovoltaic systems under partial shading conditions is presented. Also, the MPPT controller design based on the Grey Wolf optimization (GWO), Particle Swarm optimization (PSO), and P&O algorithms are presented for the competence investigation of the TLBO algorithm. The proposed system is simulated considering the shading effect, then the feasibility of the presented controller is investigated.
During recent years power systems are operated near the nominal capacity of system equipment with low stability margin. Operation of power systems in this condition is extremely risky and power systems lose their stab...
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During recent years power systems are operated near the nominal capacity of system equipment with low stability margin. Operation of power systems in this condition is extremely risky and power systems lose their stability with any intense failure of system equipment. In bulk power system, it is more critical and may cause a partial or overall blackout. The capability of black start to bring back system to a normal condition in the case of partial or overall shut down is very important in each power system. When a shutdown occurs, power plants with black start capability supply cranking power for non-black start power plants, pick up critical loads and energize required transmission lines. These actions should be done in minimum time interval to maximize system provided energy during the restoration process. Most important decision making during restoration process is the determination of start-up sequence of generation units. During the restoration process, units with black start capability (BS units) start at the beginning of the process to provide cranking power for non-black start units (NBS units). Hence the determination of NBS units is decision variable in the restoration problem. In this paper, this problem has been described as a bi-level optimization problem which in upper level determines the optimal start-up sequence of NBS units by using a teaching-learning based optimization algorithm and in lower level determines the optimal transmission path with minimum number of switching and maximum reliability between any two necessary buses using the searching path graph-basedalgorithm. The proposed approach has been implemented successfully on IEEE 24-bus RTS and IEEE 118-bus test systems.
The primary objective in the continuous flow gas-lift operations is to inject an optimal gas volume for a group of wells to maximize oil production. Due to the gas supply constraint in an oilfield, optimization of gas...
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The primary objective in the continuous flow gas-lift operations is to inject an optimal gas volume for a group of wells to maximize oil production. Due to the gas supply constraint in an oilfield, optimization of gas injection plays an important role in achieving this goal. In this work, for modeling of gas-lift operation, the potential application of an Artificial Neural Network (ANN) using Bayesian Regularization (BR) is investigated and the results are compared with Levenberg-Marquardt (LM) back-propagation training algorithm. For the optimization, teaching-learning-basedoptimization (TLBO) algorithm is applied to simultaneously solve the well-rate and gas-lift allocation problems under the injection capacity constraint. The efficiency of the TLBO is investigated based on (a) convergence rate and (b) the best solution, by comparing its performance with Genetic algorithm (GA). Extensive published data are used in model development and comparison. The proposed prediction and optimization model is tested in a gas-lift system for a given period of reservoir life. The prediction accuracy produced by the BRNN and the LMNN were 99.9% and 99.5% respectively. Results indicate that the two models have good predictive capability. Also, results show that the BR model appears more robust and efficient than the LM model and for the optimizationalgorithms, TBLO outperforms GA in the gas allocation mapping for continuous gas-lift system. The simulation results demonstrate the effectiveness of the proposed model on continuous flow gas-lift operations.
This paper deals with permutation flow shop scheduling problem in which an integrated cost model consisting of work-in-process inventory carrying cost and penalty cost due to batch delay is proposed. The objective is ...
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This paper deals with permutation flow shop scheduling problem in which an integrated cost model consisting of work-in-process inventory carrying cost and penalty cost due to batch delay is proposed. The objective is to obtain an optimum production schedule which minimizes the expected total cost per unit time of scheduling. To optimize the objective function, we apply two new metaheuristic optimization techniques namely TLBO (teaching-learningbasedoptimization) and Jaya and two traditional algorithms: PSO (particle swarm optimization) and SA (simulated annealing). The problem is solved for several instances ranging from 8 jobs and 5 machines to 500 jobs and 20 machines. Computational results show that for small instances, all algorithms performed equally good when compared with the exact solution (total enumeration method). However, for medium and large size problems, enumeration method was unable to give the results in a reasonable computation time period. Therefore the results of all four algorithms are compared among themselves and found that Jaya outperforms all algorithms. However, for a few large instances, SA yields better results in less computation time as against other heuristics. The overall performance of all algorithms reveals that TLBO and Jaya have considerable potential to solve discrete combinatorial problems such as permutation flow-shop scheduling problems.
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