A progressive barrier derivative-free trust-region algorithm for constrained optimization
A progressive barrier derivative-free trust-region algorithm for constrained optimization
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DOI：
10.1007/s10589-018-0020-4
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发表时间：
2016-06
影响因子：
2.2
通讯作者：
Charles Audet;A. Conn;Sébastien Le Digabel;Mathilde Peyrega
中科院分区：
文献类型：
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作者：
Charles Audet;A. Conn;Sébastien Le Digabel;Mathilde Peyrega
我们研究无导数约束优化问题，并提出了一个信赖域方法，建立线性或二次模型周围的最佳可行的和周围的最佳不可行的解决方案，到目前为止。这些模型在一个信任区域内进行优化，渐进式障碍方法通过逐步将不可行的解决方案推向可行域来处理约束。对40个光滑约束问题的计算结果表明，该方法与COBYLA具有竞争力;对两个机械工程非光滑多学科优化问题的计算结果表明，该方法与NOMAD软件具有竞争力。
We study derivative-free constrained optimization problems and propose a trust-region method that builds linear or quadratic models around the best feasible and around the best infeasible solutions found so far. These models are optimized within a trust region, and the progressive barrier methodology handles the constraints by progressively pushing the infeasible solutions toward the feasible domain. Computational experiments on 40 smooth constrained problems indicate that the proposed method is competitive with COBYLA, and experiments on two nonsmooth multidisciplinary optimization problems from mechanical engineering show that it can be competitive with the NOMAD software.