TY - JOUR
T1 - The directed search method for unconstrained parameter dependent multi-objective optimization problems
AU - Sosa Hernández, Víctor Adrián
AU - Lara, Adriana
AU - Trautmann, Heike
AU - Rudolph, Günter
AU - Schütze, Oliver
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2017.
PY - 2017
Y1 - 2017
N2 - In this chapter we present the adaptions of the recently proposed Directed Search method to the context of unconstrained parameter dependent multi-objective optimization problems (PMOPs). The new method, called λ-DS, is capable of performing a movement both toward and along the solution set of a given differentiable PMOP.We first discuss the basic variants of the method that use gradient information and describe subsequently modifications that allow for a gradient free realization. Finally, we show that λ-DS can be used to understand the behavior of stochastic local search within PMOPs to a certain extent which might be interesting for the development of future local search engines, or evolutionary strategies, for the treatment of such problems. We underline all our statements with several numerical results indicating the strength of the novel approach.
AB - In this chapter we present the adaptions of the recently proposed Directed Search method to the context of unconstrained parameter dependent multi-objective optimization problems (PMOPs). The new method, called λ-DS, is capable of performing a movement both toward and along the solution set of a given differentiable PMOP.We first discuss the basic variants of the method that use gradient information and describe subsequently modifications that allow for a gradient free realization. Finally, we show that λ-DS can be used to understand the behavior of stochastic local search within PMOPs to a certain extent which might be interesting for the development of future local search engines, or evolutionary strategies, for the treatment of such problems. We underline all our statements with several numerical results indicating the strength of the novel approach.
KW - Continuation method
KW - Descent method
KW - Evolutionary algorithms
KW - Local search
KW - Parameter dependent multi-objective optimization
KW - Stochastic local search
UR - http://www.scopus.com/inward/record.url?scp=84989860109&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-44003-3_12
DO - 10.1007/978-3-319-44003-3_12
M3 - Artículo
AN - SCOPUS:84989860109
SN - 1860-949X
VL - 663
SP - 281
EP - 330
JO - Studies in Computational Intelligence
JF - Studies in Computational Intelligence
ER -