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Behavior of Multiobjective Evolutionary Algorithms on Many-Objective Knapsack Problems

机译:多目标背包问题上的多目标进化算法的行为

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We examine the behavior of three classes of evolutionary multiobjective optimization (EMO) algorithms on many-objective knapsack problems. They are Pareto dominance-based, scalarizing function-based, and hypervolume-based algorithms. NSGA-II, MOEA/D, SMS-EMOA, and HypE are examined using knapsack problems with 2–10 objectives. Our test problems are generated by randomly specifying coefficients (i.e., profits) in objectives. We also generate other test problems by combining two objectives to create a dependent or correlated objective. Experimental results on randomly generated many-objective knapsack problems are consistent with well-known performance deterioration of Pareto dominance-based algorithms. That is, NSGA-II is outperformed by the other algorithms. However, it is also shown that NSGA-II outperforms the other algorithms when objectives are highly correlated. MOEA/D shows totally different search behavior depending on the choice of a scalarizing function and its parameter value. Some MOEA/D variants work very well only on two-objective problems while others work well on many-objective problems with 4–10 objectives. We also obtain other interesting observations such as the performance improvement by similar parent recombination and the necessity of diversity improvement for many-objective knapsack problems.
机译:我们研究了三类进化多目标优化(EMO)算法在多目标背包问题上的行为。它们是基于Pareto优势,基于标量函数和基于超量的算法。使用2-10个目标的背包问题检查了NSGA-II,MOEA / D,SMS-EMOA和HypE。我们的测试问题是通过在目标中随机指定系数(即利润)来产生的。我们还通过组合两个目标来创建从属或相关目标,从而产生其他测试问题。随机生成的多目标背包问题的实验结果与基于Pareto优势的算法的众所周知的性能下降相一致。也就是说,NSGA-II的性能优于其他算法。但是,还显示出当目标高度相关时,NSGA-II优于其他算法。 MOEA / D根据标量函数及其参数值的选择显示完全不同的搜索行为。一些MOEA / D变体仅在两个目标的问题上效果很好,而其他一些在4-10个目标的多目标问题上效果很好。我们还获得了其他有趣的观察结果,例如通过类似的母体重组提高了性能,以及改善了多目标背包问题的多样性。

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