Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm
R. Venkata Rao,Vivek Patel +1 more
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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Multi-objective shape optimization of a heat exchanger using parallel genetic algorithms
TL;DR: A genetic algorithm is used to find the geometry most favorable to simultaneously maximize heat exchange while obtaining a minimum pressure loss, and a nearly optimal speed-up is obtained for the present configuration.
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Design optimization of shell and tube heat exchangers using global sensitivity analysis and harmony search algorithm
TL;DR: In this paper, the authors explored the use of global sensitivity analysis (GSA) and harmony search algorithm (HSA) for design optimization of shell and tube heat exchangers (STHXs) from the economic viewpoint.
Journal ArticleDOI
Multi-objective optimization of shell and tube heat exchangers
TL;DR: In this paper, a shell and tube heat exchanger was first thermally modeled using e-NTU method while Bell-Delaware procedure was applied to estimate its shell side heat transfer coefficient and pressure drop.
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Thermodynamic optimization of cross flow plate-fin heat exchanger using a particle swarm optimization algorithm
R.V. Rao,Vivek Patel +1 more
TL;DR: In this article, the authors explored the use of particle swarm optimization (PSO) algorithm for thermodynamic optimization of a cross flow plate-fin heat exchanger, and the results of optimization using PSO are validated by comparing with those obtained by using GA.
Journal ArticleDOI
Optimization design of shell-and-tube heat exchanger by entropy generation minimization and genetic algorithm
TL;DR: In this paper, a new shell-and-tube heat exchanger optimization design approach is developed, wherein the dimensionless entropy generation rate obtained by scaling the entropy generation on the ratio of the heat transfer rate to the inlet temperature of cold fluid is employed as the objective function.
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