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The development of efficient and effective evolutionary multi-objectiveoptimization (EMO) algorithms has been an active research topic in theevolutionary computation community. Over the years, many EMO algorithms havebeen proposed. The existing EMO algorithms are mainly developed based on thefinal population framework. In the final population framework, the finalpopulation of an EMO algorithm is presented to the decision maker. Thus, it isrequired that the final population produced by an EMO algorithm is a goodsolution set. Recently, the use of solution selection framework was suggestedfor the design of EMO algorithms. This framework has an unbounded externalarchive to store all the examined solutions. A pre-specified number ofsolutions are selected from the archive as the final solutions presented to thedecision maker. When the solution selection framework is used, EMO algorithmscan be designed in a more flexible manner since the final population is notnecessarily to be a good solution set. In this paper, we examine the design ofMOEA/D under these two frameworks. We use an offline genetic algorithm-basedhyper-heuristic method to find the optimal configuration of MOEA/D in eachframework. The DTLZ and WFG test suites and their minus versions are used inour experiments. The experimental results suggest the possibility that a moreflexible, robust and high-performance MOEA/D algorithm can be obtained when thesolution selection framework is used.

Decomposition-Based Multi-Objective Evolutionary Algorithm Design under Two Algorithm Frameworks