Scalable multi-objective optimization test problems | IEEE Conference Publication | IEEE Xplore

Scalable multi-objective optimization test problems


Abstract:

After adequately demonstrating the ability to solve different two-objective optimization problems, multi-objective evolutionary algorithms (MOEAs) must show their efficac...Show More

Abstract:

After adequately demonstrating the ability to solve different two-objective optimization problems, multi-objective evolutionary algorithms (MOEAs) must 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 Pareto-optimal 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 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.
Date of Conference: 12-17 May 2002
Date Added to IEEE Xplore: 07 August 2002
Print ISBN:0-7803-7282-4
Conference Location: Honolulu, HI, USA

Contact IEEE to Subscribe

References

References is not available for this document.