Multi-objective optimization of combined Brayton and inverse Brayton cycles using advanced optimization algorithms

This study explores the use of teaching-learning-based optimization (TLBO) and artificial bee colony (ABC) algorithms for determining the optimum operating conditions of combined Brayton and inverse Brayton cycles. Maximization of thermal efficiency and specific work of the system are considered as...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Engineering optimization Ročník 44; číslo 8; s. 965 - 983
Hlavní autoři: Venkata Rao, R., Patel, Vivek
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 01.08.2012
Taylor & Francis Ltd
Témata:
Algorithms
artificial bee colony algorithm
Brayton cycle
Convergence
Cycle ratio
genetic algorithm
Genetic algorithms
Inverse
inverse Brayton cycle
Learning
Maximization
Multiple criteria decision making
Optimization
Optimization algorithms
particle swarm optimization
Pressure
Pressure ratio
teaching-learning-based optimization algorithm
Thermodynamics
ISSN:0305-215X, 1029-0273
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This study explores the use of teaching-learning-based optimization (TLBO) and artificial bee colony (ABC) algorithms for determining the optimum operating conditions of combined Brayton and inverse Brayton cycles. Maximization of thermal efficiency and specific work of the system are considered as the objective functions and are treated simultaneously for multi-objective optimization. Upper cycle pressure ratio and bottom cycle expansion pressure of the system are considered as design variables for the multi-objective optimization. An application example is presented to demonstrate the effectiveness and accuracy of the proposed algorithms. The results of optimization using the proposed algorithms are validated by comparing with those obtained by using the genetic algorithm (GA) and particle swarm optimization (PSO) on the same example. Improvement in the results is obtained by the proposed algorithms. The results of effect of variation of the algorithm parameters on the convergence and fitness values of the objective functions are reported.
Bibliografie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-2
content type line 23
ObjectType-Article-1
ObjectType-Feature-2
ISSN:0305-215X
1029-0273
DOI:10.1080/0305215X.2011.624183