Adaptive ranking based constraint handling for explicitly constrained black-box optimization
Adaptive ranking based constraint handling for explicitly constrained black-box optimization
复制标题
基于自适应排序的约束处理，用于显式约束的黑盒优化
DOI：
10.1145/3321707.3321717
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Akimoto Youhei
中科院分区：
文献类型：
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作者：
Sakamoto Naoki;Akimoto Youhei
针对协方差矩阵自适应进化策略（CMA-ES）提出了一种新的显式约束处理技术。建议的约束处理显示两个不变性属性。一是目标函数和约束函数对任意元递增变换的不变性。二是对搜索空间任意仿射变换的不变性。该方法通过考虑目标函数值排序和约束违例排序的自适应加权和，将约束优化问题转化为无约束优化问题，目标函数值排序和约束违例排序由每个候选解到约束边界投影的马氏距离度量。仿真结果表明，采用约束处理的CMA-ES具有仿射不变性，性能与无约束处理的CMA-ES相似。
A novel explicit constraint handling technique for the covariance matrix adaptation evolution strategy (CMA-ES) is proposed. The proposed constraint handling exhibits two invariance properties. One is the invariance to arbitrary element-wise increasing transformation of the objective and constraint functions. The other is the invariance to arbitrary affine transformation of the search space. The proposed technique virtually transforms a constrained optimization problem into an unconstrained optimization problem by considering an adaptive weighted sum of the ranking of the objective function values and the ranking of the constraint violations that are measured by the Mahalanobis distance between each candidate solution to its projection onto the boundary of the constraints. Simulation results are presented and show that the CMA-ES with the proposed constraint handling exhibits the affine invariance and performs similarly to the CMA-ES on unconstrained counterparts.