On the interplay of generator and archiver within archive based multiobjective evolutionary algorithms

In this work we deal with the design of archive based multi-objective evolutionary algorithms (MOEAs) for the numerical treatment of multi-objective optimization problems (MOPs). In particular, we design two generational operators-one mutation and one crossover operator-that are tailored to a class of archiving strategies and propose a new evolutionary strategy that can be viewed as a variant of ∈-MOEA, a well-known archive based MOEA. Comparisons of the performance of the two algorithms on a class of benchmark functions indicate that this approach, namely to investigate the interplay of generator and archiver of a MOEA, is a promising step toward in the design of fast and reliable algorithms for the numerical treatment of MOPs.