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Optimization of multistage vapour compression systems using genetic algorithms. Part 2: Application of genetic algorithm and results
Author(s) -
West A. C.,
Sherif S. A.
Publication year - 2001
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.724
Subject(s) - algorithm , coding (social sciences) , population , computer science , genetic algorithm , data compression , binary number , binary code , mathematics , statistics , arithmetic , machine learning , demography , sociology
Genetic algorithms involve the coding of a solution into a binary string in the same manner that DNA is a biological coding. A population of binary strings are randomly created, evaluated, allowed to mate and are mutated to form a new generation of strings. There is a mating preference given to those strings which rate the highest to simulate the survival‐of‐the‐fittest theory that exists in nature. This process of evaluation, mating and mutation is repeated until some termination criteria are met. A computer code was written in Visual C++ to simulate the vapour compression systems and perpetuate the genetic algorithm. The genetic algorithm functioned adequately enough to provide general trends but it did not find a universal optimum. After numerous runs, the code produced data that suggest that systems which employ intercooler/flash tanks and operate at lower evaporating temperatures have a higher multistage effectiveness. Copyright © 2001 John Wiley & Sons, Ltd.