Abstract
背景
The Pareto-based approaches have shown some success in designing multiobjective evolutionary algorithms (MEAs). Their methods of fitness assignment are mainly from the information of dominated and nondominated individuals. On the top of the hierarchy of MEAs, the strength Pareto evolutionary algorithm (SPEA) has been elaborately designed with this principle in mind.
基于帕累托的方法在设计多目标进化算法（MEAs）方面取得了一些成功。 他们的适应性分配方法主要来自主导和非主导个体的信息。 在MEA层次结构的顶层，强度Pareto进化算法（SPEA）在精心设计时考虑了这一原则。
方法
In this paper, we propose )
multiobjective evolutionary algorithm MEA))
, which discards the dominated individuals in each generation.
在本文中，我们提出了一种多目标进化算法，它丢弃了每一代中占主导地位的个体。
实验结果
The comparisons of the experimental results demonstrate that the MEA)
outperforms SPEA on five benchmark functions with less computational efforts.
实验的比较结果表明，在五个基准函数上优于SPEA，计算量更少。
Conclution
方法
In this paper, we developed a . Without reserving the dominated individuals, the appeared to have a very fast search speed.
在本文中，我们开发了一个。 没有保留主导的个体，似乎具有非常快的搜索速度。
实验证明
We compared its performance with SPEA using five benchmark functions. The results have shown that the performs better than SPEA in both objective functions values an search speed.
我们使用五种基准功能将其性能与SPEA进行了比较。 结果表明，在两个目标函数值和搜索速度中，的性能优于SPEA。
From the empirical studies, we can conclude that ’s parent selection scheme plays a major roleto produce quality solutions. Although the fast search speed results from the rejection of the dominated individuals, the search diversity may not be enough for some problems with discrete Pareto-optimality. So more tests on different numerical multiobjective problems will be one area of our future work. To solve discrete or combinatorial problems, the development of other suitable mutation operators is also part of our future research.
结论
从实证研究中，我们可以得出结论，的父母选择方案在产生质量解决方案方面起着重要作用。 尽管快速搜索速度是由去除主导个体引起的，但搜索多样性可能不足以解决离散Pareto最优性的一些问题。 因此，对不同数值多目标问题的更多测试将是我们未来工作的一个方面。 为了解决离散或组合问题，其他合适的变异算子的开发也是我们未来研究的一部分。
留给自己的问题
1 理解Pareto概念
2 调研SPEA算法
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