TY - GEN
T1 - An overview of weighted and unconstrained scalarizing functions
AU - Pescador-Rojas, Miriam
AU - Gómez, Raquel Hernández
AU - Montero, Elizabeth
AU - Rojas-Morales, Nicolás
AU - Riff, María Cristina
AU - Coello Coello, Carlos A.
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Scalarizing functions play a crucial role in multi-objective evolutionary algorithms (MOEAs) based on decomposition and the R2 indicator, since they guide the population towards nearly optimal solutions, assigning a fitness value to an individual according to a predefined target direction in objective space. This paper presents a general review of weighted scalarizing functions without constraints, which have been proposed not only within evolutionary multi-objective optimization but also in the mathematical programming literature. We also investigate their scalability up to 10 objectives, using the test problems of Lamé Superspheres on the MOEA/D and MOMBI-II frameworks. For this purpose, the best suited scalarizing functions and their model parameters are determined through the evolutionary calibrator EVOCA. Our experimental results reveal that some of these scalarizing functions are quite robust and suitable for handling many-objective optimization problems.
AB - Scalarizing functions play a crucial role in multi-objective evolutionary algorithms (MOEAs) based on decomposition and the R2 indicator, since they guide the population towards nearly optimal solutions, assigning a fitness value to an individual according to a predefined target direction in objective space. This paper presents a general review of weighted scalarizing functions without constraints, which have been proposed not only within evolutionary multi-objective optimization but also in the mathematical programming literature. We also investigate their scalability up to 10 objectives, using the test problems of Lamé Superspheres on the MOEA/D and MOMBI-II frameworks. For this purpose, the best suited scalarizing functions and their model parameters are determined through the evolutionary calibrator EVOCA. Our experimental results reveal that some of these scalarizing functions are quite robust and suitable for handling many-objective optimization problems.
KW - Evolutionary algorithms
KW - Many-objective optimization
KW - Scalarizing function
KW - Tuning process
UR - http://www.scopus.com/inward/record.url?scp=85014241346&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-54157-0_34
DO - 10.1007/978-3-319-54157-0_34
M3 - Contribución a la conferencia
AN - SCOPUS:85014241346
SN - 9783319541563
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 499
EP - 513
BT - Evolutionary Multi-Criterion Optimization - 9th International Conference, EMO 2017, Proceedings
A2 - Schütze, Oliver
A2 - Rudolph, Gunter
A2 - Klamroth, Kathrin
A2 - Jin, Yaochu
A2 - Trautmann, Heike
A2 - Grimme, Christian
A2 - Wiecek, Margaret
PB - Springer Verlag
T2 - 9th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2017
Y2 - 19 March 2017 through 22 March 2017
ER -