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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >Dynamic reference vectors and biased crossover use for inverse model based evolutionary multi-objective optimization with irregular Pareto fronts
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Dynamic reference vectors and biased crossover use for inverse model based evolutionary multi-objective optimization with irregular Pareto fronts

机译：基于逆模型的动态参考向量和偏置交叉用途，与不规则帕累托前线的逆模型进化多目标优化

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摘要

The recently developed inverse model based multi-objective evolutionary algorithm (IM-MOEA) has been shown to be an effective methodology for solving multi-objective optimization problems (MOPs) with regular Pareto fronts (PFs). Due to the limitations of uniformly distributed reference vectors and inverse model sampling, however, the IM-MOEA is challenged when solving MOPs with irregular PFs. To alleviate these limitations, both an external elitist archive and a biased crossover operator are integrated into the IM-MOEA. The primary role of the former is to dynamically adjust the reference vectors, which encourages the IM-MOEA to explore the sparse regions. The latter is used to improve the search efficiency of the IM-MOEA. By incorporating both features into the IM-MOEA, an enhanced IM-MOEA variant (E-IM-MOEA) is presented in this paper. Experimental studies were conducted on eighteen MOPs with irregular PFs to compare the E-IM-MOEA with six state-of-the-art multi-objective evolutionary algorithms. The experimental results show that the proposed approach has better overall performance.
机译：最近开发的基于逆模型的多目标进化算法（IM-MOEA）已被证明是一种有效的方法，用于解决具有常规帕累托前线（PFS）的多目标优化问题（MOP）。然而，由于均匀分布的参考矢量和反向模型采样的局限性，当用不规则PFS求解拖把时，IM-MOEA受到挑战。为了减轻这些限制，将外部精英存档和偏置的交叉操作员均集成到IM-MOEA中。前者的主要作用是动态调整参考向量，鼓励IM-MOEA探索稀疏区域。后者用于提高IM-MOEA的搜索效率。通过将两个特征掺入IM-MOEA中，本文提出了增强的IM-MOEA变体（E-IM-MOEA）。在具有不规则PFS的18个MOP上进行了实验研究，以比较具有六种最先进的多目标进化算法的E-IM-MOEA。实验结果表明，该方法具有更好的整体性能。

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