  In this paper we systematically study the importance, i.e., the influence on
performance, of the main design elements that differentiate scalarizing
functions-based multiobjective evolutionary algorithms (MOEAs). This class of
MOEAs includes Multiobjecitve Genetic Local Search (MOGLS) and Multiobjective
Evolutionary Algorithm Based on Decomposition (MOEA/D) and proved to be very
successful in multiple computational experiments and practical applications.
The two algorithms share the same common structure and differ only in two main
aspects. Using three different multiobjective combinatorial optimization
problems, i.e., the multiobjective symmetric traveling salesperson problem, the
traveling salesperson problem with profits, and the multiobjective set covering
problem, we show that the main differentiating design element is the mechanism
for parent selection, while the selection of weight vectors, either random or
uniformly distributed, is practically negligible if the number of uniform
weight vectors is sufficiently large.
