TY  - CHAP
AV  - none
SN  - 978-3-642-20957-4
T3  - Studies in Computational Intelligence
SP  - 85
ID  - eprints748
N2  - Several approaches for solving multi-objective optimization problems entail a form of scalarization of the objectives. This chapter proposes a study of different dynamic objectives aggregation methods in the context of evolutionary algorithms. These methods are mainly based on both weighted sum aggregations and curvature variations. Since the incorporation of chaotic rules or behaviour in population-based optimization algorithms has been shown to possibly enhance their searching ability, this study proposes to introduce and evaluate also some chaotic rules in the dynamic weights generation process. A comparison analysis is presented on the basis of a campaign of computational experiments on a set of benchmark problems from the literature. 
Y1  - 2011///
TI  - Dynamic objectives aggregation methods in multi-objective evolutionary optimization 
A1  - Dellino, Gabriella
A1  - Fedele, Mariagrazia
A1  - Meloni, Carlo
UR  - http://dx.doi.org/10.1007/978-3-642-20958-1_6
PB  - Springer-Verlag
ED  - Nedjah, Nadia
ED  - Dos Santos Coelho, Leandro
ED  - Mariani, Viviana
ED  - De Macedo Mourelle, Luiza
T2  - Innovative computing methods and their applications to engineering problems
EP  - 103
ER  -