Dellino, Gabriella and Fedele, Mariagrazia and Meloni, Carlo Dynamic objectives aggregation methods in multi-objective evolutionary optimization. In: Innovative computing methods and their applications to engineering problems. Studies in Computational Intelligence (357). Springer-Verlag, pp. 85-103. ISBN 978-3-642-20957-4 (2011)Full text not available from this repository.
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.
|Item Type:||Book Section|
|Subjects:||Q Science > QA Mathematics|
|Research Area:||Economics and Institutional Change|
|Depositing User:||Users 17 not found.|
|Date Deposited:||01 Aug 2011 10:24|
|Last Modified:||04 Aug 2011 07:30|
Actions (login required)