Progressively Interactive Evolutionary Multi-Objective Optimization ...

Abstract
Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto.fi
Author
Ankur Sinha
Name of the doctoral dissertation
Progressively Interactive Evolutionary Multiobjective Optimization
Publisher Aalto University School of Economics
Unit Department of Business Technology
Series Aalto University publication series DOCTORAL DISSERTATIONS 17/2011
Field of research Decision Making and Optimization
Abstract
A complete optimization procedure for a multi-objective problem essentially comprises of
search and decision making. Depending upon how the search and decision making task is
integrated, algorithms can be classified into various categories. Following `a decision making
after search' approach, which is common with evolutionary multi-objective optimization
algorithms, requires to produce all the possible alternatives before a decision can be taken.
This, with the intricacies involved in producing the entire Pareto-front, is not a wise approach
for high objective problems. Rather, for such kind of problems, the most preferred point on the
front should be the target. In this study we propose and evaluate algorithms where search and
decision making tasks work in tandem and the most preferred solution is the outcome. For the
two tasks to work simultaneously, an interaction of the decision maker with the algorithm is
necessary, therefore, preference information from the decision maker is accepted periodically
by the algorithm and progress towards the most preferred point is made.
Two different progressively interactive procedures have been suggested in the dissertation
which can be integrated with any existing evolutionary multi-objective optimization
algorithm to improve its effectiveness in handling high objective problems by making it
capable to accept preference information at the intermediate steps of the algorithm. A number
of high objective un-constrained as well as constrained problems have been successfully
solved using the procedures. One of the less explored and difficult domains, i.e., bilevel multiobjective
optimization has also been targeted and a solution methodology has been proposed.
Initially, the bilevel multi-objective optimization problem has been solved by developing a
hybrid bilevel evolutionary multi-objective optimization algorithm. Thereafter, the
progressively interactive procedure has been incorporated in the algorithm leading to an
increased accuracy and savings in computational cost. The efficacy of using a progressively
interactive approach for solving difficult multi-objective problems has, therefore, further been
justified.
Keywords Evolutionary multi-objective optimization algorithms, multiple criteria decisionmaking,
interactive multi-objective optimization algorithms, bilevel optimization
ISBN (printed) 978-952-60-4052-3 ISBN (pdf) 978-952-60-4053-0
ISSN-L 1799-4934 ISSN (printed) 1799-4934 ISSN (pdf) 1799-4942
Pages 135 Location of publisher Espoo Location of printing Helsinki Year 2011