基于弱关联的自适应高维多目标进化算法研究
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桂林理工大学

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TP18

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国家自然科学基金 广西自然科学基金


WAEA: A Weak Association-Based Adaptive Evolutionary Algorithm for Many-objective Optimization
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Guilin University of Technology

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    摘要:

    本文对现有的分解方法进行了改进,提出了一种基于弱关联的自适应高维多目标进化算法(WAEA)。首先,提出了一种基于夹角子空间的关联策略,使得一个解能与多个参考向量相关联;其次,提出弱关联概念,并基于此概念设计了双模态标量函数,使算法能够更好的地处理复杂PF问题。此外,算法通过检测参考向量子空间内解的数量,自适应调整惩罚参数大小,使其能有效处理各类多目标问题。最后,将WAEA算法与8种代表性的高维多目标算法进行比较,实验结果表明WAEA算法在处理复杂Pareto前沿的高维多目标问题时能更好地平衡Pareto最优解的收敛性与多样性。

    Abstract:

    This paper proposes a Weak Association-Based Adaptive Evolutionary Algorithm (WAEA) on Many-objective Optimization by improving the previous decomposition approaches. First, an association strategy has been presented based on the angle subspace, which can make a solution associated with multiple reference vectors. Then, the idea of weak association has been employed to design a bimodal scalar function which improves the capability of dealing with the complex PF problem. Moreover, through the detection of the number of solutions in the reference vector subspace, the proposed algorithm is capable of doing self-adaption to adjust the size of penalty parameters to efficiently deal with multi-type issue on many-objective optimization. Finally, the proposed WAEA algorithm has been compared with eight representative many-objective based algorithms, respectively. The results show that the WAEA has the capability of gaining better balance of the Pareto optimum in convergence and diversity while dealing with high-dimensional many-objective problems.

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历史
  • 收稿日期:2019-12-09
  • 最后修改日期:2021-03-09
  • 录用日期:2020-04-03
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