Progressively Interactive Evolutionary Multi-Objective Optimization ...

1 Introduction
Many real-world applications of multi-objective optimization involve a
high number of objectives. Existing evolutionary multi-objective optimization
algorithms [7, 34] have been applied to problems having multiple
objectives for the task of finding a well-representative set of Paretooptimal
solutions [6, 4]. These methods have been successful in solving a
wide variety of problems with two or three objectives. However, these
methodologies tend to fail for high number of objectives (greater than
three) [8, 22]. The major hindrances in handling high number of objectives
relate to stagnation in search, increased dimensionality of Pareto-optimal
front, large computational cost, and difficulty in visualization of the objective
space. These difficulties are inherent to a multi-objective problem
having a high number of dimensions and cannot be eliminated; rather,
procedures to handle such difficulties need to be explored.
In many of the existing methodologies, preference information from
the decision maker is utilized before the beginning of the search process
or at the end of the search process to produce the optimal solution(s) in
a multi-objective problem. Some approaches interact with the decision
maker and iterate the process of elicitation and search until a satisfactory
solution is found. However, not many studies have been performed where
preference information is elicited during the search process and the information
is utilized to progressively proceed towards the most preferred
solution.
This dissertation is an effort towards development of progressively interactive
procedures to handle difficult multi-objective problems, combining
concepts from the fields of Evolutionary Multi-objective Optimization
(EMO) and Multi Criteria Decision Making (MCDM). The fields of Evolutionary
Multi-objective Optimization and Multi Criteria Decision Making
have a common goal, but researchers have shown only lukewarm interest,
until recently, in applying the principles of one field to the other. In
the dissertation, emphasis has been placed on integration of methods and
development of hybrid procedures that are helpful in the extension of the
existing algorithms to handle challenging problems with multiple objec-
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