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首页> 外文期刊>Soft computing: A fusion of foundations, methodologies and applications >Objective reduction for many-objective optimization problems using objective subspace extraction
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Objective reduction for many-objective optimization problems using objective subspace extraction

机译：使用客观子空间提取的多目标优化问题的客观降低

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AbstractMulti-objective evolutionary algorithms (MOEAs) have shown their effectiveness in exploring a well converged and diversified approximation set for multi-objective optimization problems (MOPs) with 2 and 3 objectives. However, most of them perform poorly when tackling MOPs with more than 3 objectives [often called many-objective optimization problems (MaOPs)]. This is mainly due to the fact that the number of non-dominated individuals increases rapidly in MaOPs, leading to the loss of selection pressure in population update. Objective reduction can be used to lower the difficulties of some MaOPs, which helps to alleviate the above problem. This paper proposes a novel objective reduction framework for MaOPs using objective subspace extraction, named OSEOR. A new conflict information measurement among different objectives is defined to sort the relative importance of each objective, and then an effective approach is designed to extract several overlapped subspaces with reduced dimensionality during the execution of MOEAs. To validate the effectiveness of the proposed approach, it is embedded into a well-known and frequently used MOEA (NSGA-II). Several test MaOPs, including four irreducible problems (i.e. DTLZ1–DTLZ4) and a reducible problem (i.e. DTLZ5), are used to assess the optimization performance. The experimental results indicate that the performance of NSGA-II can be significantly enhanced using OSEOR on both irreducible and reducible MaOPs.]]>
机译：<！[cdata [ <标题>抽象 ara id =“par1”>多目标进化算法（moeas）显示了它们探索多目标优化问题（MOPS）探索良好融合和多样化近似的有效性，具有2和3目标。然而，大多数人在使用超过3个目标的拖布时表现不佳[经常被称为多目标优化问题（MAOPS）]。这主要是由于非主导的个体数量在MAOPS中迅速增加，导致人口更新中的选择压力损失。客观减少可用于降低一些MAOPS的困难，这有助于缓解上述问题。本文提出了使用目标子空间提取的Maops的新颖目标减少框架，名为O​​seor。在不同目标之间的新冲突信息测量被定义为对每个目标的相对重要性进行分类，然后设计有效的方法以在执行MoEAS期间提取具有减小的维度的若干重叠子空间。为了验证所提出的方法的有效性，它嵌入到众所周知的和经常使用的MOEA（NSGA-II）中。几个测试Maops，包括四个不可缩短的问题（即DTLZ1-DTLZ4）和可还原问题（即DTLZ5）来评估优化性能。实验结果表明，使用ISORER在IRREAFIBLE和可再冻结的MAOPS上可以显着提高NSGA-II的性能。 ]]>

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