An Evolutionary Multi- and Many-Objective Optimization Algorithm Based on ISDE + and Region Decomposition

Zixian Lin, Hai-Lin Liu, Fangqing Gu
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引用次数: 2

Abstract

In this paper, we propose an evolutionary multi-and many-objective optimization algorithm combining I_SDE + and region decomposition. It decomposes the objective space into a number of sub-regions by a set of direction vectors and independently calculates the indicator I_SDE + by using the corresponding direction vector in each subregion. Thus, the convergence direction of each sub-region is relatively adjusted. In this way, the proposed algorithm can adapt to various of Pareto Front shapes. The inferior individuals are eliminated according to the value of I_SDE + of each individual one by one. In the experiments, we compare the proposed algorithm with four evolutionary multi-and many-objective optimization algorithms on WFG series with different number of objectives. The result shows that the proposed algorithm promotes diversity and convergence.
基于ISDE +和区域分解的多目标进化优化算法
本文提出了一种结合I_SDE +和区域分解的进化多目标优化算法。它通过一组方向向量将目标空间分解为多个子区域,并在每个子区域中使用相应的方向向量独立计算指标I_SDE +。因此,各子区域的收敛方向相对调整。这样,该算法可以适应多种Pareto Front形状。根据每个个体的I_SDE +值逐个淘汰劣势个体。在实验中,我们将该算法与四种不同目标数的WFG序列多目标和多目标进化优化算法进行了比较。结果表明,该算法具有较好的多样性和收敛性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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