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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Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems
TL;DR: A comprehensive comparative study has been carried out to show the performance of the MBA over other recognized optimizers in terms of computational effort (measured as the number of function evaluations) and function value (accuracy).
Journal ArticleDOI
A survey on new generation metaheuristic algorithms
TL;DR: In this survey, fourteen new and outstanding metaheuristics that have been introduced for the last twenty years other than the classical ones such as genetic, particle swarm, and tabu search are distinguished.
Journal ArticleDOI
An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems
R. Venkata Rao,Vivek Patel +1 more
TL;DR: The basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self motivated learning.
Journal ArticleDOI
An improved TLBO with elite strategy for parameters identification of PEM fuel cell and solar cell models
Qun Niu,Hongyun Zhang,Kang Li +2 more
TL;DR: An improved and simplified teaching-learning based optimization algorithm (STLBO) is proposed to identify and optimize parameters for PEM fuel cell as well as solar cell models by introducing an elite strategy to improve the quality of population and a local search is employed to further enhance the performance of the global best solution.
Journal ArticleDOI
Parameters identification of photovoltaic models using self-adaptive teaching-learning-based optimization
TL;DR: A self-adaptive teaching-learning-based optimization (SATLBO) that improves the searching ability of different learning phases, an elite learning strategy and a diversity learning method are introduced into the teacher phase and learner phase.
References
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Second law based optimisation of crossflow plate-fin heat exchanger design using genetic algorithm
TL;DR: A genetic algorithm based optimisation technique has been developed for crossflow plate-fin heat exchangers using offset-strip fins that aims at minimising the number of entropy generation units for a specified heat duty under given space restrictions.
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Use of genetic algorithms for the optimal design of shell-and-tube heat exchangers
TL;DR: In this article, an approach based on genetic algorithms for the optimal design of shell-and-tube heat exchangers is presented. But the approach uses the Bell-Delaware method for the description of the shell-side flow with no simplifications.
Journal ArticleDOI
Design optimization of shell-and-tube heat exchangers
TL;DR: In this paper, the authors present a study about the design optimization of shell-and-tube heat exchangers, which consists of the minimization of the thermal surface area for a certain service, involving discrete decision variables.
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Minimizing shell-and-tube heat exchanger cost with genetic algorithms and considering maintenance
TL;DR: In this article, the authors present a procedure for minimizing the cost of a shell-and-tube heat exchanger based on GA, where the global cost includes the operating cost (pumping power) and the initial cost expressed in terms of annuities.
Journal ArticleDOI
Exergetic optimization of shell and tube heat exchangers using a genetic based algorithm
TL;DR: In this article, a genetic-based algorithm was developed, programmed, and applied to estimate the optimum values of discrete and continuous variables of the MINLP (mixed integer nonlinear programming) test problems.
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