In this paper, a novel trust-region-based surrogate-assisted optimization method, called CBOILA (Constrained Black-box Optimization by Intrinsically Linear Approximation), has been proposed to reduce the number of black-box function evaluations and enhance the efficient performance for solving complex optimization problems. This developed optimization approach utilizes an assembly of intrinsically linear approximations to seek the optimum with incorporation of three strategies: (1) extended-box selection strategy (EBS), (2) global intelligence selection strategy (GIS) and (3) balanced trust-region strategy. EBS aims at reducing the number of function evaluations in current iteration by selecting points close to the given trust region boundary. Whilst, GIS is designed to improve the exploration performance by adaptively choosing points outside the trust region. The balanced trust-region strategy works with four indicators, which will be triggered by the quality of the approximation, the movement direction of the search, the location of the sub-optimum, and the condition of the termination, respectively. By modifying the move limit of each dimension accordingly, CBOILA is capable of attaining a balanced search between exploitation and exploration for the optimal solutions. To demonstrate the potentials of the proposed optimization method, four widely used benchmark problems have been examined and the results have also been compared with solutions by other metamodel-based algorithms in published works. Results show that the proposed method can efficiently and robustly solve constrained black-box optimization problems within an acceptable computational time.

中文翻译：

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通过内在线性近似 (CBIOLA) 进行约束黑盒优化的有效策略

在本文中，提出了一种新的基于信任区域的代理辅助优化方法，称为 CBIOLA（通过内在线性逼近的约束黑盒优化），以减少黑盒函数评估的数量并提高效率解决复杂的优化问题。这种开发的优化方法利用一组内在线性近似值来寻求最优并结合三种策略：(1) 扩展框选择策略 (EBS)，(2) 全局智能选择策略 (GIS) 和 (3) 平衡信任-区域战略。EBS 旨在通过选择靠近给定信任区域边界的点来减少当前迭代中函数评估的数量。同时，GIS 旨在通过自适应地选择信任区域外的点来提高探索性能。平衡信任域策略使用四个指标，分别由逼近质量、搜索移动方向、次优位置和终止条件触发。通过相应地修改每个维度的移动限制，CBIOLA 能够在开发和探索之间实现最佳解决方案的平衡搜索。为了证明所提出的优化方法的潜力，已经检查了四个广泛使用的基准问题，并且还将结果与已发表作品中其他基于元模型的算法的解决方案进行了比较。