  In the evolutionary multi-objective optimization (EMO) community, it is
usually assumed that the final population is presented to the decision maker as
the result of the execution of an EMO algorithm. Recently, an unbounded
external archive was used to evaluate the performance of EMO algorithms in some
studies where a pre-specified number of solutions are selected from all the
examined non-dominated solutions. In this framework, which is referred to as
the solution selection framework, the final population does not have to be a
good solution set. Thus, the solution selection framework offers higher
flexibility to the design of EMO algorithms than the final population
framework. In this paper, we examine the design of MOEA/D under these two
frameworks. First, we show that the performance of MOEA/D is improved by
linearly changing the reference point specification during its execution
through computational experiments with various combinations of initial and
final specifications. Robust and high performance of the solution selection
framework is observed. Then, we examine the use of a genetic algorithm-based
offline hyper-heuristic method to find the best configuration of MOEA/D in each
framework. Finally, we further discuss solution selection after the execution
of an EMO algorithm in the solution selection framework.
