An Improved Self-Adaptive Differential Evolution Algorithm for Optimization Problems
An Improved Self-Adaptive Differential Evolution Algorithm for Optimization Problems
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DOI：
10.1109/tii.2012.2198658
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发表时间：
2013-02-01
影响因子：
12.3
通讯作者：
Essam, Daryl L.
中科院分区：
文献类型：
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作者：
Elsayed, Saber M.;Sarker, Ruhul A.;Essam, Daryl L.
许多现实世界的优化问题很难解决，因为它们不具备精确算法所需的良好数学性质。进化算法被证明是适合这样的问题。在本文中，我们提出了一种改进的差分进化算法，使用不同的变异算子的混合。此外，该算法是授权的协方差自适应矩阵进化策略算法作为一个局部搜索。为了判断算法的性能，我们已经解决了著名的基准以及各种现实世界的优化问题。现实生活中的问题来自不同的来源和学科。根据得到的结果，该算法显示出上级的性能相比，其他算法，也解决了这些问题。
Many real-world optimization problems are difficult to solve as they do not possess the nice mathematical properties required by the exact algorithms. Evolutionary algorithms are proven to be appropriate for such problems. In this paper, we propose an improved differential evolution algorithm that uses a mix of different mutation operators. In addition, the algorithm is empowered by a covariance adaptation matrix evolution strategy algorithm as a local search. To judge the performance of the algorithm, we have solved well-known benchmark as well as a variety of real-world optimization problems. The real-life problems were taken from different sources and disciplines. According to the results obtained, the algorithm shows a superior performance in comparison with other algorithms that also solved these problems.