A Covariance Matrix Self-Adaptation Evolution Strategy for Optimization Under Linear Constraints
A Covariance Matrix Self-Adaptation Evolution Strategy for Optimization Under Linear Constraints
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DOI：
10.1109/tevc.2018.2871944
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发表时间：
2018-06
影响因子：
14.3
通讯作者：
Patrick Spettel;H. Beyer;Michael Hellwig
中科院分区：
文献类型：
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作者：
Patrick Spettel;H. Beyer;Michael Hellwig
本文讨论了协方差矩阵自适应进化策略（CMSA-ES）的开发，用于解决具有线性约束的优化问题。所提出的算法被称为线性约束CMSA-ES（lcCMSA-ES）。它使用专门构建的变异算子以及投影修复来满足约束。 lcCMSA-ES 在由约束定义的线性流形上自行演化。仅在可行搜索点处评估目标函数（内点法）。这是模拟优化和有限元方法等应用领域经常需要的属性。该算法在各种不同的测试问题上进行了测试，得出了可观的结果。
This paper addresses the development of a covariance matrix self-adaptation evolution strategy (CMSA-ES) for solving optimization problems with linear constraints. The proposed algorithm is referred to as linear constraint CMSA-ES (lcCMSA-ES). It uses a specially built mutation operator together with repair by projection to satisfy the constraints. The lcCMSA-ES evolves itself on a linear manifold defined by the constraints. The objective function is only evaluated at feasible search points (interior point method). This is a property often required in application domains, such as simulation optimization and finite element methods. The algorithm is tested on a variety of different test problems revealing considerable results.