Application of the parallel adaptive genetic simulated annealing algorithm for the synthesis of heat exchanger networks

ABSTRACT A robust hybrid genetic algorithm that can be used to solve process synthesis problems with mixed integer nonlinear programming (MINLP) models is developed. The proposed hybrid approach constructs an efficient genetic simulated annealing algorithm for global search, whereas the iterative hi...

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Veröffentlicht in:Asia-Pacific journal of chemical engineering Jg. 7; H. 5; S. 660 - 669
Hauptverfasser: Zhao, Chao, Xu, Qiaoling, An, Aimin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Chichester, UK John Wiley & Sons, Ltd 01.09.2012
Schlagworte:
genetic simulated annealing
heat exchanger network synthesis
hybrid genetic algorithm
iterative hill climbing algorithm
mixed integer nonlinear programming (MINLP)
ISSN:1932-2135, 1932-2143
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Zusammenfassung:ABSTRACT A robust hybrid genetic algorithm that can be used to solve process synthesis problems with mixed integer nonlinear programming (MINLP) models is developed. The proposed hybrid approach constructs an efficient genetic simulated annealing algorithm for global search, whereas the iterative hill climbing method as a local search technique is incorporated into genetic simulated annealing loop to speed up the convergence of the algorithm. To efficiently locate quality solutions to complex nonconvex MINLP problems, a self‐adaptive scheme is developed in the parallel adaptive genetic simulated annealing to maintain a better tradeoff between the global and local search, which can improve the convergence speed and computational efficiency compared with some other hybrid genetic algorithm methods. For the evaluations, we use some well‐known standard benchmark functions. The computational results demonstrate the effectiveness and robustness of the present approach. Further, the proposed algorithm is tailored to find optimum solution to heat exchanger network synthesis problem. The results presented prove that the proposed method is a very efficient global optimization tool for large dimensional, nonconvex MINLP problems. © 2012 Curtin University of Technology and John Wiley & Sons, Ltd.
Bibliographie:ArticleID:APJ1622
istex:D5819858E805D857757BF5494541BFA557A7627E
ark:/67375/WNG-DFXNGTMR-7
ISSN:1932-2135
1932-2143
DOI:10.1002/apj.1622