Global and Local Surrogate-Assisted Differential Evolution for Expensive Constrained Optimization Problems With Inequality Constraints
Global and Local Surrogate-Assisted Differential Evolution for Expensive Constrained Optimization Problems With Inequality Constraints
复制标题
具有不等式约束的昂贵约束优化问题的全局和局部代理辅助差分进化
DOI：
10.1109/tcyb.2018.2809430
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发表时间：
2019-05
影响因子：
11.8
通讯作者：
Sun Guangyong
中科院分区：
文献类型：
--
作者：
Wang Yong;Yin Da Qing;Yang Shengxiang;Sun Guangyong
对于昂贵约束优化问题（ECOPs），目标函数和约束的计算是非常耗时的。提出了一种新的全局和局部代理辅助差分进化算法求解带不等式约束的ECOPs问题。所提出的方法包括两个主要阶段：1）全局代理辅助阶段和2）局部代理辅助阶段。在全局代理辅助阶段，DE作为搜索引擎产生多个试验向量。然后，使用广义回归神经网络对这些试验向量进行评估。为了从这些试验向量中选择最佳候选，结合两个规则。第一个是可行性规则，它首先将种群引导到可行区域，然后引导到最优解。此外，第二条规则更强调具有最高预测不确定性的解决方案，从而消除了替代项的不准确性。在局部代理人辅助阶段，利用内点法结合径向基函数对种群中的个体进行精化。在进化过程中，全局代理辅助阶段具有快速定位有希望区域的能力，局部代理辅助阶段能够加快收敛速度。因此，通过结合这两个重要的元素，可以显着减少适应度评估的数量。所提出的方法已经过测试，从三个测试套件和两个现实世界的情况下，众多的基准测试功能。实验结果表明，该方法的性能优于其他国家的最先进的方法。
For expensive constrained optimization problems (ECOPs), the computation of objective function and constraints is very time-consuming. This paper proposes a novel global and local surrogate-assisted differential evolution (DE) for solving ECOPs with inequality constraints. The proposed method consists of two main phases: 1) global surrogate-assisted phase and 2) local surrogate-assisted phase. In the global surrogate-assisted phase, DE serves as the search engine to produce multiple trial vectors. Afterward, the generalized regression neural network is used to evaluate these trial vectors. In order to select the best candidate from these trial vectors, two rules are combined. The first is the feasibility rule, which at first guides the population toward the feasible region, and then toward the optimal solution. In addition, the second rule puts more emphasis on the solution with the highest predicted uncertainty, and thus alleviates the inaccuracy of the surrogates. In the local surrogate-assisted phase, the interior point method coupled with radial basis function is utilized to refine each individual in the population. During the evolution, the global surrogate-assisted phase has the capability to promptly locate the promising region and the local surrogate-assisted phase is able to speed up the convergence. Therefore, by combining these two important elements, the number of fitness evaluations can be reduced remarkably. The proposed method has been tested on numerous benchmark test functions from three test suites and two real-world cases. The experimental results demonstrate that the performance of the proposed method is better than that of other state-of-the-art methods.
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影响因子：
14.3
作者：
R. Regis;C. Shoemaker
通讯作者：
R. Regis;C. Shoemaker
影响因子：
5.7
作者：
Grimaccia, Francesco;Mussetta, Marco;Zich, Riccardo E.
通讯作者：
Zich, Riccardo E.
DOI：
10.1098/rspa.2006.1679
发表时间：
2006-07
期刊：
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
影响因子：
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作者：
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通讯作者：
Alexander I. J. Forrester;N. Bressloff;A. Keane
DOI：
10.1137/120902434
发表时间：
2013-05
期刊：
SIAM Rev.
影响因子：
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作者：
Stefan M. Wild;C. Shoemaker
通讯作者：
Stefan M. Wild;C. Shoemaker
影响因子：
14.3
作者：
Liu, Bo;Zhang, Qingfu;Gielen, Georges G. E.
通讯作者：
Gielen, Georges G. E.