Solving ill-posed continuous, global optimization problems is challenging. No well-established methods are available to handle the objective intensity that appears when studying the inversion of non-invasive tumor tissue diagnosis or geophysical applications. The paper presents a complex metaheuristic method that identifies regions of objective function’s insensitivity (plateaus). It is composed of a multi-deme hierarchic memetic strategy coupled with random sample clustering, cluster integration, and a special kind of local evolution processes using the multiwinner selection that allows to breed the demes to cover each plateau separately. The final phase consists in a smooth local objective approximation which determines the shape of the plateaus by analyzing the objective level sets. We test the method on benchmarks with multiple non-convex plateaus and in an actual geophysical application of magnetotelluric data inversion.

中文翻译：

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在解决病态逆问题中逼近景观不敏感区域

解决不适定的连续全局优化问题具有挑战性。在研究非侵入性肿瘤组织诊断或地球物理应用反演时，尚无成熟的方法可用于处理出现的客观强度。本文提出了一种复杂的元启发式方法，该方法可识别目标函数的不敏感区域（高原）。它由多特征分层模因策略，随机样本聚类，聚类集成以及使用多赢者选择的特殊类型的局部演化过程组成，该方法允许繁殖这些特征以分别覆盖每个高原。最后阶段包括一个平滑的局部目标近似值，该近似值通过分析目标水平集来确定高原的形状。