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Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/jspui/handle/10054/11325

Title: Solution of constrained optimization problems by multi-objective genetic algorithm
Authors: SUMMANWAR, VS
JAYARAMAN, VK
KULKARNI, BD
KUSUMAKAR, HS
GUPTA, K
RAJESH, J
Keywords: disjunctive programming-models
algebraic process systems
dynamic optimization
distillation-columns
minlp problems
optimal-design
batch
search
bioprocesses
framework
Issue Date: 2002
Publisher: PERGAMON-ELSEVIER SCIENCE LTD
Citation: COMPUTERS & CHEMICAL ENGINEERING, 26(10), 1481-1492
Abstract: 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 . .
URI: 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
ISSN: 0098-1354
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