Evolutionary Optimization of Dynamic Multi-objective Test Functions

Mehnen, J.^{1, a}; Wagner, T.^{1, b}; Rudolph, G.²

1)

Institut für Spanende Fertigung, Universität Dortmund, Baroper Str. 301, 44227 Dortmund

2)

Universität Dortmund, Fakultät für Informatik, Lehrstuhl für Algorithm Engineering (LS 11), 44221 Dortmund

a) mehnen@isf.de; b) wagner@isf.de

Kurzfassung

Multi-objective as well as dynamic characteristics appear in many real-world problems. In order to use multi-objective evolutionary optimization algorithms (MOEA) efficiently, a systematic analysis of the algorithms’ behavior in dynamic environments by means of dynamic test functions is necessary. These functions can be classified into problems with changing Pareto sets and/or Pareto fronts with different dynamic criteria. Thus, a test suite with existing benchmark functions having dynamic behaviour and new designed dynamic test functions for yet uncovered cases is proposed. Convergence and solution distribution features of modern MOEA, namely NSGA-II, SPEA2, and MSOPS using different variation operators (Simulated Binary Crossover with Polynomial Mutation and Differential Evolution) will be analyzed. For this reason, a new path integral metric is introduced. Especially the transfer of single-objective results and the ability of the algorithms to use historically evolved population properties will be discussed.

Schlüsselwörter

dynamic environments, multi-objective optimization, evolutionary algorithms

Veröffentlichung

In: Digital Proceedings of the 3° Workshop Italiano di Vita Artificiale e delle 2a Giornata di Studio Italiana sul Calcolo Evoluzionistico, 2006, Siena, Italy, Cagnoni, S.; Vanneschi, L. (Hrsg.)