The concepts of efficiency and effectiveness must be addressed in conducting research into using a Evolutionary Algorithm (EA) for optimization problems. The increased use of evolutionary approaches for real-world applications, containing multiple objectives and high dimensionality, has led to the design and generation of a number of Multiobjective Evolutionary Algorithms (MOEA). When analyzing these algorithms, the issues of effectiveness and efficiency are extremely important and typically drive the urge to parallelize these algorithms. The parallelization of MOEAs is a relatively new concept, with few researchers contributing work in this area. This parallelization process is not a simple task and involves the analysis of various parallel models and the parameters associated with these models. This paper presents a thorough analysis of the various parallel MOEA models, the issues associated with these models and recommendations for using these models in MOEAs. In particular, these parallelization concepts are applied to the Multiobjective Messy Genetic Algorithm II.
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