  It has been widely observed that there exists no universal best
Multi-objective Evolutionary Algorithm (MOEA) dominating all other MOEAs on all
possible Multi-objective Optimization Problems (MOPs). In this work, we
advocate using the Parallel Algorithm Portfolio (PAP), which runs multiple
MOEAs independently in parallel and gets the best out of them, to combine the
advantages of different MOEAs. Since the manual construction of PAPs is
non-trivial and tedious, we propose to automatically construct high-performance
PAPs for solving MOPs. Specifically, we first propose a variant of PAPs, namely
MOEAs/PAP, which can better determine the output solution set for MOPs than
conventional PAPs. Then, we present an automatic construction approach for
MOEAs/PAP with a novel performance metric for evaluating the performance of
MOEAs across multiple MOPs. Finally, we use the proposed approach to construct
a MOEAs/PAP based on a training set of MOPs and an algorithm configuration
space defined by several variants of NSGA-II. Experimental results show that
the automatically constructed MOEAs/PAP can even rival the state-of-the-art
ensemble MOEAs designed by human experts, demonstrating the huge potential of
automatic construction of PAPs in multi-objective optimization.
