Evolutionary Algorithms, Homomorphous Mappings, and Constrained Parameter Optimization
Evolutionary Algorithms, Homomorphous Mappings, and Constrained Parameter Optimization
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DOI：
10.1162/evco.1999.7.1.19
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发表时间：
1999-03-01
影响因子：
6.8
通讯作者：
Michalewicz, Zbigniew
中科院分区：
文献类型：
--
作者：
Koziel, Slawomir;Michalewicz, Zbigniew
在过去的五年中，已经提出了几种使用进化算法（EAS）来处理非线性约束的方法，以解决数值优化问题。最近的调查论文将这些方法分为四类：可行性，惩罚功能，寻找可行性和其他混合动力。在本文中，我们研究了一种解决约束数值优化问题的新方法，该问题包含了N量量立方体之间的同形映射可行的搜索空间。这种方法构成了基于第五个解码器的约束处理技术类别的示例。我们证明了这种新方法在几种测试案例上的力量，并讨论了其进一步的潜力。
During the last five years, several methods have been proposed for handling nonlinear constraints using evolutionary algorithms (EAs) for numerical optimization problems. Recent survey papers classify these methods into four categories: preservation of feasibility, penalty functions, searching for feasibility, and other hybrids.In this paper we investigate a new approach for solving constrained numerical optimization problems which incorporates a homomorphous mapping between n-dimensional cube and a feasible search space. This approach constitutes an example of the fifth decoder-based category of constraint handling techniques. We demonstrate the power of this new approach on several test cases and discuss its further potential.