Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm
R. Venkata Rao,Vivek Patel +1 more
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TLDR
In this paper, a modified version of the TLBO algorithm is introduced and applied for the multi-objective optimization of heat exchangers, where the objective function is to maximize the heat exchanger effectiveness and minimize the total cost of the exchanger.About:
This article is published in Applied Mathematical Modelling.The article was published on 2013-02-01 and is currently open access. It has received 305 citations till now. The article focuses on the topics: Plate fin heat exchanger & Shell and tube heat exchanger.read more
Citations
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A Posteriori Multiobjective Self-Adaptive Multipopulation Jaya Algorithm for Optimization of Thermal Devices and Cycles
TL;DR: A posteriori is proposed, and it is applied for the multiobjective optimization of the selected thermal devices and cycles to obtain the sets of nondominated alternative solutions.
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OpenMP Teaching-Learning Based Optimization Algorithm over Multi-Core System
TL;DR: In this paper, an implementation of teaching-learning-based optimization (TLBO) on a multi-core system using OpenMP API's with C/C++ is proposed, which maximizes the CPU (Central Processing Unit) utilization.
Journal ArticleDOI
Multi-objective optimization on structural parameters of torsional flow heat exchanger
TL;DR: In this paper, a multi-objective structure optimization on the baffle was carried out by using the method of response surface optimization to achieve a better heat transfer performance in a torsional flow heat exchanger.
Journal ArticleDOI
Inertia-weight local-search-based TLBO algorithm for energy management in isolated micro-grids with renewable resources
TL;DR: In this article , an Inertia-Weight Local-Search based Teaching-Learning-Based Optimization (IWLS-TLBO), is proposed as an exploration-exploitation balanced metaheuristic algorithm to solve the energy management system (EMS) problem.
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Comparison of modified teaching---learning-based optimization and extreme learning machine for classification of multiple power signal disturbances
TL;DR: This paper presents a modified TLBO (teaching–learning-based optimization) approach for the local linear radial basis function neural network (LLRBFNN) model to classify multiple power signal disturbances and reveals that the latter is much faster in implementation, thus making it suitable for processing large quantum of power signal disturbance data.
References
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Journal ArticleDOI
Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems
TL;DR: The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort and results show that TLBO is more effective and efficient than the other optimization methods.
Journal ArticleDOI
Compact heat exchangers
TL;DR: The third edition of the second edition as discussed by the authors was published in 1964 and contains basic test data for eleven new surface configurations, including some of the very compact ceramic matrices, in both the English and the Systeme International (SI) system of units.
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Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems
TL;DR: An efficient optimization method called 'Teaching-Learning-Based Optimization (TLBO)' is proposed in this paper for large scale non-linear optimization problems for finding the global solutions.
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An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems
R. Rao,Vivek Patel +1 more
TL;DR: Elitism concept is introduced in the TLBO algorithm and its effect on the performance of the algorithm is investigated and the effects of common controlling parameters such as the population size and the number of generations on the results are investigated.
Journal ArticleDOI
Heat exchanger design based on economic optimisation
TL;DR: In this paper, a procedure for optimal design of shell and tube heat exchangers is proposed, which utilizes a genetic algorithm to minimize the total cost of the equipment including capital investment and the sum of discounted annual energy expenditures related to pumping.
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