Abstract
Many realworld search and optimization problems are naturally posed
as nonlinear programming problems having multiple objectives.
Due to lack of suitable solution techniques, such problems are
artificially converted into a singleobjective problem and solved.
The difficulty arises because such problems give rise to a set
of Paretooptimal solutions, instead of a single optimum solution.
It then becomes important to find not just one Paretooptimal
solution but as many of them as possible. Classical methods are
not quite efficient in solving these problems because they require
repetitive applications to find multiple Paretooptimal solutions
and in some occasions repetitive applications do not guarantee
finding distinct Paretooptimal solutions. The population approach
of evolutionary algorithms (EAs) allows an efficient way to find
multiple Paretooptimal solutions simultaneously in a single
simulation run.
In this tutorial, we discussed the following aspects related to
EMO:
1. The basic differences in principle of EMO with classical methods.
2. A gentle introduction to evolutionary algorithms with simple
examples. A simple method of handling constraints was also
discussed.
3. The concept of domination and methods of finding nondominated
solutions in a population of solutions were discussed.
4. A brief history of the development of EMO is highlighted.
5. A number of main EMO methods (NSGAII, SPEA and PAES) were
discussed.
6. The advantage of EMO methodologies was discussed by presenting
a number of case studies. They clearly showed the advantage of
finding a number of Paretooptimal solutions simultaneously.
7. Three advantages of using an EMO methodology were stressed:
(i) For a better decision making (in terms of choosing a
compromised solution) in the presence of multiple solutions
(ii) For finding important relationships among decision variables
(useful in design optimization). Some case studies from engineering
demonstrated the importance of such studies.
(iii) For solving other optimization problems efficiently. For
example, in solving genetic programming problems, the socalled
`bloating problem of increased program size can be solved by using
a second objective of minimizing the size of the programs.
8. A number of salient research topics were highlighted. Some of
them are as follows:
(i) Development of scalable test problems
(ii) Development of computationally fast EMO methods
(iii) Performance metrics for evaluating EMO methods
(iv) Interactive EMO methodologies
(v) Robust multiobjective optimization procedures
(vi) Finding knee or other important solutions including partial
Paretooptimal set
(vii) Multiobjective scheduling and other optimization problems.
It was clear from the discussions that
evolutionary search methods offers an alternate means of solving
multiobjective optimization problems compared to classical
approaches. This is why multiobjective optimization using EAs is
getting a growing attention in the recent years.
The motivated readers may explore
current research issues and other important studies from various
texts (Coello et al, 2003; Deb, 2001), conference proceedings
(EMO01 and EMO03 Proceedings) and numerous research papers
(http://www.lania.mx/~ccoello/EMOO/).
References:

C. A. C. Coello, D. A. VanVeldhuizen, and G. Lamont.
Evolutionary Algorithms for Solving MultiObjective Problems.
Boston, MA: Kluwer Academic Publishers, 2002.
K.Deb. Multiobjective optimization using evolutionary algorithms.
Chichester, UK: Wiley, 2001.
C. Fonseca, P. Fleming, E. Zitzler, K. Deb, and L. Thiele, editors.
Proceedings of the Second Evolutionary MultiCriterion
Optimization (EMO03) Conference
(Lecture Notes in Computer Science (LNCS) 2632).
Heidelberg: Springer, 2003.
E. Zitzler, K. Deb, L. Thiele, C. A. C. Coello, and D. Corne,
editors. Proceedings of the First Evolutionary MultiCriterion
Optimization (EMO01) Conference
(Lecture Notes in Computer Science (LNCS) 1993).
Heidelberg: Springer, 2001.
BibTeX  Entry
@InProceedings{deb:DSP:2005:252,
author = {Kalyanmoy Deb},
title = {A Tutorial on Evolutionary MultiObjective Optimization (EMO)},
booktitle = {Practical Approaches to MultiObjective Optimization},
year = {2005},
editor = {J{\"u}rgen Branke and Kalyanmoy Deb and Kaisa Miettinen and Ralph E. Steuer},
number = {04461},
series = {Dagstuhl Seminar Proceedings},
ISSN = {18624405},
publisher = {Internationales Begegnungs und Forschungszentrum f{\"u}r Informatik (IBFI), Schloss Dagstuhl, Germany},
address = {Dagstuhl, Germany},
URL = {http://drops.dagstuhl.de/opus/volltexte/2005/252},
annote = {Keywords: Multiobjective optimization, multicriterion optimization, Paretooptimal solutions, Evolutionary methods, EMO}
}
Keywords: 

Multiobjective optimization, multicriterion optimization, Paretooptimal solutions, Evolutionary methods, EMO 
Seminar: 

04461  Practical Approaches to MultiObjective Optimization 
Issue Date: 

2005 
Date of publication: 

10.08.2005 