Treffer: 基于最大熵进化算法的高维多目标优化.

With the increase in data volume, dimensionality, and the number of optimization objectives, the conflicts between objectives intensify, making the solution of multi-objective optimization problems increasingly complex. This is especially true for high-dimensional heterogeneous multi-objective optimization problems, where the difficulty of solving them increases significantly. In this paper, we propose a maximum entropy-based reference vector-guided evolutionary algorithm aimed at solving many-objective optimization problems. By combining reference point strategies with evolutionary algorithm search mechanisms, the proposed method achieves complementary cooperation between the ideal and worst reference points, thereby improving the efficiency of optimization. The algorithm relies on a set of adaptively selected reference vectors and optimizes them using Bayesian maximum entropy, focusing on balancing diversity and convergence during the optimization process. Through comparative experiments on several benchmark problems, the proposed K-RVEA algorithm demonstrates significant advantages, verifying the feasibility and effectiveness of the method. [ABSTRACT FROM AUTHOR]

随着数据量、维度和优化目标数量的增加, 目标之间的冲突也随之加剧, 使得多目标优化问题的求解变得愈 加复杂。 尤其是高维异构的多目标优化问题, 解决难度显著增加。 因此, 提出了一种基于最大熵的参考向量引导进 化算法, 旨在解决高维多目标优化问题。 通过结合参考点策略与进化算法搜索机制, 本方法实现了理想参考点和最 差参考点的互补协同, 从而提高了优化效率。 该算法依赖一组自适应选择的参考向量, 并通过贝叶斯最大熵进行优 化, 重点关注优化过程中的多样性与收敛性平衡。 通过在一些基准问题上的对比实验可知, 提出的 K -RVEA 算法表 现出显著的优势, 验证了该方法的可行性与有效性。 [ABSTRACT FROM AUTHOR]