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Simplifying multiobjective optimization: An automated design methodology for the nondominated sorted genetic algorithm‐II
Author(s) -
Reed Patrick,
Minsker Barbara S.,
Goldberg David E.
Publication year - 2003
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2002wr001483
Subject(s) - multi objective optimization , mathematical optimization , computer science , genetic algorithm , pareto optimal , pareto principle , evolutionary algorithm , key (lock) , mathematics , computer security
Many water resources problems require careful balancing of fiscal, technical, and social objectives. Informed negotiation and balancing of objectives can be greatly aided through the use of evolutionary multiobjective optimization (EMO) algorithms, which can evolve entire tradeoff (or Pareto) surfaces within a single run. The primary difficulty in using these methods lies in the large number of parameters that must be specified to ensure that these algorithms effectively quantify design tradeoffs. This technical note addresses this difficulty by introducing a multipopulation design methodology that automates parameter specification for the nondominated sorted genetic algorithm‐II (NSGA‐II). The NSGA‐II design methodology is successfully demonstrated on a multiobjective long‐term groundwater monitoring application. Using this methodology, multiobjective optimization problems can now be solved automatically with only a few simple user inputs.