TY - CHAP
T1 - The gradient free directed search method as local search within multi-objective evolutionary algorithms
AU - Lara, Adriana
AU - Alvarado, Sergio
AU - Salomon, Shaul
AU - Avigad, Gideon
AU - Coello Coello, Carlos A.
AU - Schütze, Oliver
PY - 2013
Y1 - 2013
N2 - Recently, the Directed Search Method has been proposed as a point-wise iterative search procedure that allows to steer the search, in any direction given in objective space, of a multi-objective optimization problem. While the original version requires the objectives' gradients, we consider here a possible modification that allows to realize the method without gradient information. This makes the novel algorithm in particular interesting for hybridization with set oriented search procedures, such as multi-objective evolutionary algorithms. In this paper, we propose the DDS, a gradient free Directed Search method, and make a first attempt to demonstrate its benefit, as a local search procedure within a memetic strategy, by integrating the DDS into the well-known algorithmMOEA/D. Numerical results on some benchmark models indicate the advantage of the resulting hybrid.
AB - Recently, the Directed Search Method has been proposed as a point-wise iterative search procedure that allows to steer the search, in any direction given in objective space, of a multi-objective optimization problem. While the original version requires the objectives' gradients, we consider here a possible modification that allows to realize the method without gradient information. This makes the novel algorithm in particular interesting for hybridization with set oriented search procedures, such as multi-objective evolutionary algorithms. In this paper, we propose the DDS, a gradient free Directed Search method, and make a first attempt to demonstrate its benefit, as a local search procedure within a memetic strategy, by integrating the DDS into the well-known algorithmMOEA/D. Numerical results on some benchmark models indicate the advantage of the resulting hybrid.
UR - http://www.scopus.com/inward/record.url?scp=84872553680&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-31519-0_10
DO - 10.1007/978-3-642-31519-0_10
M3 - Capítulo
AN - SCOPUS:84872553680
SN - 9783642315183
T3 - Advances in Intelligent Systems and Computing
SP - 153
EP - 168
BT - EVOLVE A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II
PB - Springer Verlag
ER -