DMCA
Scalable Multi-Objective Optimization Test Problems (2002)
| Venue: | in Congress on Evolutionary Computation (CEC’2002 |
| Citations: | 112 - 8 self |
BibTeX
@INPROCEEDINGS{Deb02scalablemulti-objective,
author = {Kalyanmoy Deb and Lothar Thiele and Marco Laumanns and Eckart Zitzler},
title = {Scalable Multi-Objective Optimization Test Problems},
booktitle = {in Congress on Evolutionary Computation (CEC’2002},
year = {2002},
pages = {825--830}
}
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Abstract
After adequately demonstrating the ability to solve different two-objective optimization problems, multi-objective evolutionary algorithms (MOEAs) must now show their efficacy in handling problems having more than two objectives. In this paper, we suggest three different approaches for systematically designing test problems for this purpose. The simplicity of construction, scalability to any number of decision variables and objectives, knowledge of exact shape and location of the resulting Paretooptimal front, and ability to control difficulties in both converging to the true Pareto-optimal front and maintaining a widely distributed set of solutions are the main features of the suggested test problems. Because of these features, they should be found useful in various research activities on MOEAs, such as testing the performance of a new MOEA, comparing different MOEAs, and having a better understanding of the working principles of MOEAs.
Keyphrases
scalable multi-objective optimization test problem various research activity multi-objective evolutionary algorithm test problem different moeas suggested test problem different approach true pareto-optimal front exact shape paretooptimal front new moea decision variable main feature different two-objective optimization problem
