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A Novel Hybrid Direct Search Method for Constrained Non-Smooth Black-Box Problems

机译:约束非光滑黑盒问题的一种新型混合直接搜索方法

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Process engineering applications often lead to non-smooth constrained optimization problems in which the objective function and/or the constraints have non-differentiabilities and step discontinuities. Since the objective function is often the outcome of a complex simulation or the outcome of a lower level optimization problem, it may also be noisy and not defined in some point. In this work we propose and test a new hybrid direct search method for constrained non-smooth discontinuous problems which combines the positive features of Particle Swarm, Generating Set Search, and Complex, that we refer to as PGS-COM. Computational results show that PGS-COM outperforms the main available methods and exhibits considerable robustness to non-smoothness, unrelaxable constraints, evaluation failures and numerical noise.
机译:过程工程应用通常会导致非平滑约束优化问题,其中目标函数和/或约束具有不可微性和步骤不连续性。由于目标函数通常是复杂模拟的结果或较低级别优化问题的结果,因此目标函数也可能是嘈杂的,并且在某些时候未定义。在这项工作中,我们提出并测试了一种新的混合直接搜索方法,用于解决受约束的非平滑不连续问题,该方法结合了粒子群,生成集搜索和复杂的积极特征,我们将其称为PGS-COM。计算结果表明,PGS-COM的性能优于主要可用方法,并且对非光滑性,不可宽松的约束,评估失败和数值噪声表现出相当强的鲁棒性。

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