MSOPS-II: A general-purpose Many-Objective optimiser

Existing evolutionary methods capable of true many-objective optimisation have been limited in their application: for example either initial search directions need to be specified a-priori, or the use of hypervolume limits the search in practice to less than 10 objective dimensions. This paper descr...

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Bibliographic Details
Published in2007 IEEE Congress on Evolutionary Computation pp. 3944 - 3951
Main Author Hughes, E.J.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2007
Subjects
Approximation algorithms
Decision making
Measurement
Optimization methods
Pareto optimization
Sampling methods
Space exploration
Stochastic processes
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ISBN1424413397
9781424413393
ISSN1089-778X
DOI10.1109/CEC.2007.4424985

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Summary:Existing evolutionary methods capable of true many-objective optimisation have been limited in their application: for example either initial search directions need to be specified a-priori, or the use of hypervolume limits the search in practice to less than 10 objective dimensions. This paper describes two extensions to the multiple single objective pareto sampling (MSOPS) algorithm. The first provides automatic target vector generation, removing the requirement for initial a-priori designer intervention; and secondly redefines the fitness assignment method to simplify analysis and allow more comprehensive constraint handling. The significant enhancements allow the new MSOPS-II ranking process to be used as part of a general-purpose multi/many objective optimisation algorithm, requiring minimal initial configuration.
ISBN:1424413397
9781424413393
ISSN:1089-778X
DOI:10.1109/CEC.2007.4424985