A hybrid global optimization method: The multi-dimensional case
A hybrid global optimization method: The multi-dimensional case
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DOI：
10.1016/s0377-0427(02)00878-6
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发表时间：
2003-06-15
影响因子：
2.4
通讯作者：
Xu, PL
中科院分区：
文献类型：
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作者：
Xu, PL
我们扩展了Xu（J. Comput. 147（2002）301-314）的一维情况到多维情况。该方法由两个基本组成部分：局部优化和可行点发现。局部优化器保证在可行点附近产生局部最优解的效率和速度。可行点搜索器为新方法始终正确地产生全局最优解提供了理论保证。如果一个非线性非凸反问题有多个全局最优解，我们的算法是能够找到所有的正确。三个合成的例子，这失败了模拟退火和遗传算法，被用来证明所提出的方法。(C)2003 Elsevier Science B. V.保留所有权利。
We extend the hybrid global optimization method proposed by Xu (J. Comput. Appl. Math. 147 (2002) 301-314) for the one-dimensional case to the multi-dimensional case. The method consists of two basic components: local optimizers and feasible point finders. Local optimizers guarantee efficiency and speed of producing a local optimal solution in the neighbourhood of a feasible point. Feasible point finders provide the theoretical guarantee for the new method to always produce the global optimal solution(s) correctly. If a nonlinear nonconvex inverse problem has multiple global optimal solutions, our algorithm is capable of finding all of them correctly. Three synthetic examples, which have failed simulated annealing and genetic algorithms, are used to demonstrate the proposed method. (C) 2003 Elsevier Science B.V. All rights reserved.