In many technical fields, single-objective optimization procedures in continuous domains involve expensive numerical simulations. In this context, an improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide fast convergence speed, high solution accuracy and robust performance over a wide range of problems. It implements enhancements of the ABC structure and hybridizations with interpolation strategies. The latter are inspired by the quadratic trust region approach for local investigation and by an efficient global optimizer for separable problems. Each modification and their combined effects are studied with appropriate metrics on a numerical benchmark, which is also used for comparing AsBeC with some effective ABC variants and other derivative-free algorithms. In addition, the presented algorithm is validated on two recent benchmarks adopted for competitions in international conferences. Results show remarkable competitiveness and robustness for AsBeC.

中文翻译：

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一种基于混合群算法的单目标优化问题，涉及高成本分析

在许多技术领域中，连续域中的单目标优化过程涉及昂贵的数值模拟。在这种情况下，提出了一种改进的人工蜂群（ABC）算法，称为人工超级蜂增强菌落（AsBeC）。AsBeC旨在提供快速收敛速度，高解决方案精度和强大的性能，可解决各种问题。它实现了ABC结构的增强，并通过插值策略实现了杂交。后者的灵感来自用于局部调查的二次信任区域方法，以及针对可分离问题的高效全局优化器。在数值基准上使用适当的指标研究每种修改及其组合效果，它也用于将AsBeC与一些有效的ABC变体和其他无导数算法进行比较。此外，本文提出的算法已在国际会议的竞赛中采用的两个最新基准进行了验证。结果表明，AsBeC具有显着的竞争力和稳健性。