Abstract
It is known that almost every multi-objective algorithm is to stay on Pareto dominance, and the single optimization local search cannot be easily integrated with these MOEA. Only a few multi-objective (MO) algorithms are built using a decomposition strategy. MOEA/D algorithm decomposes problem based on multi-objective into various sub-problems and optimizes these entire sub-problem concurrently. One and all sub-problems are optimized by using information from its neighboring sub-problems. Also, a good comparison between these two approaches was missing in the literature on multi-objective targets. The comparisons carried out between these two different strategies are needed for identifying their strengths and weaknesses. The development of efficiency and effectiveness of these multi-objective evolutionary algorithms is the basis of these comparisons. This paper compares three MOEAs and also covers NSGA version II (Pareto domination), NSGA version III (Pareto domination), and the MOEA/D (decomposition-based). We had taken the multi-objective Rosenbrock function as a testing MO problem.
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