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Solution of constrained optimization problems by multi-objective genetic algorithm

DSpace at IIT Bombay

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Title Solution of constrained optimization problems by multi-objective genetic algorithm
 
Creator SUMMANWAR, VS
JAYARAMAN, VK
KULKARNI, BD
KUSUMAKAR, HS
GUPTA, K
RAJESH, J
 
Subject disjunctive programming-models
algebraic process systems
dynamic optimization
distillation-columns
minlp problems
optimal-design
batch
search
bioprocesses
framework
constrained optimization problems
multi-objective
genetic algorithm
 
Description This paper introduces a method for constrained optimization using a modified multi-objective algorithm. The algorithm treats the constraints as objective functions and handles them using the concept of Pareto dominance. The population members are ranked by two different ways: first ranking is based on objective function. value and the second ranking is based on Pareto dominance of the population members. The maintenance of elite lists for both rankings facilitates preservation of potentially superior solutions. A range of problems including non-linear programming and mixed integer non-linear programming has been solved to test the efficacy of the proposed algorithm. The algorithm effectively handles constraints encountered in both small-scale and large-scale optimization problems. The performance of the algorithm compares favourably with existing evolutionary and heuristic approaches. (C) 2002 . .
 
Publisher PERGAMON-ELSEVIER SCIENCE LTD
 
Date 2011-08-26T13:34:27Z
2011-12-26T12:57:28Z
2011-12-27T05:41:35Z
2011-08-26T13:34:27Z
2011-12-26T12:57:28Z
2011-12-27T05:41:35Z
2002
 
Type Article
 
Identifier COMPUTERS & CHEMICAL ENGINEERING, 26(10), 1481-1492
0098-1354
http://dx.doi.org/10.1016/S0098-1354(02)00125-4
http://dspace.library.iitb.ac.in/xmlui/handle/10054/11325
http://hdl.handle.net/10054/11325
 
Language en