Title :
Performance assessment of multiobjective optimizers: an analysis and review
Author :
Zitzler, Eckart ; Thiele, Lothar ; Laumanns, Marco ; Fonseca, Carlos M. ; da Fonseca, Viviane Grunert
Author_Institution :
Comput. Eng. & Networks Lab., Swiss Fed. Inst. of Technol., Switzerland
fDate :
4/1/2003 12:00:00 AM
Abstract :
An important issue in multiobjective optimization is the quantitative comparison of the performance of different algorithms. In the case of multiobjective evolutionary algorithms, the outcome is usually an approximation of the Pareto-optimal set, which is denoted as an approximation set, and therefore the question arises of how to evaluate the quality of approximation sets. Most popular are methods that assign each approximation set a vector of real numbers that reflect different aspects of the quality. Sometimes, pairs of approximation sets are also considered. In this study, we provide a rigorous analysis of the limitations underlying this type of quality assessment. To this end, a mathematical framework is developed which allows one to classify and discuss existing techniques.
Keywords :
approximation theory; genetic algorithms; Pareto-optimal set; approximation set; evolutionary algorithms; multiobjective optimization; performance assessment; quality indicator; Algorithm design and analysis; Computer networks; Design engineering; Electronic mail; Evolutionary computation; Helium; Laboratories; Performance analysis; Quality assessment; Space technology;
Journal_Title :
Evolutionary Computation, IEEE Transactions on
DOI :
10.1109/TEVC.2003.810758