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Grey wolf optimization (GWO) algorithm is a new emerging algorithm that is based on the social hierarchy of grey wolves as well as their hunting and cooperation strategies. Introduced in 2014, this algorithm has been used by a large number of researchers and designers, such that the number of citations to the original paper exceeded many other algorithms. In a recent study by Niu et al., one of the main drawbacks of this algorithm for optimizing real‐world problems was introduced. In summary, they showed that GWO's performance degrades as the optimal solution of the problem diverges from 0. In this paper, by introducing a straightforward modification to the original GWO algorithm, that is, neglecting its social hierarchy, the authors were able to largely eliminate this defect and open a new perspective for future use of this algorithm. The efficiency of the proposed method was validated by applying it to benchmark and real‐world engineering problems. [ABSTRACT FROM AUTHOR]

Necessary conditions for the existence of a super‐simple, decomposable, near‐resolvable (v,4,6)‐balanced incomplete block design (BIBD) whose 2‐component subdesigns are both near‐resolvable (v,4,3)‐BIBDs are v≡1 (mod 4) and v≥17. In this paper, we show that these necessary conditions are sufficient. Using these designs, we also establish that the necessary conditions for the existence of a super‐simple near‐resolvable (v,4,3)‐RBIBD, namely v≡1 (mod 4) and v≥9, are sufficient. A few new pairwise balanced designs are also given. [ABSTRACT FROM AUTHOR]

The center of a spatial object or set of objects is a rather straightforward and well understood descriptor. Beyond providing a statistically oriented interpretation of the middle of something, the center often has much significance in terms of locating services and activities, but may also represent a place of political/historical importance, a site that explains the occurrence of events, or a position that avoids conflict. Even so, precise definition and specification of the spatial center is difficult, as many alternatives exist for such a descriptor in practice. This is because different interpretations of a spatial center are inevitable depending on context, theory, and operational nuances. As a result, a range of approaches may reflect the necessary descriptive flexibility, depending on the type of evaluation being carried out. While legacy mathematical and statistical thinking may point to particular alternatives, spatial significance and bias remain questionable for some approaches as they are not generally well understood. Further, a host of other considerations, such as direct and indirect summary along with homogeneous and heterogeneous attribute distribution, contribute additional levels of detail to choices for best characterizing a center. Definitions and approaches used to specify the center of a spatial object, or set of objects, are detailed to demonstrate the significance of spatial context, and are used to show that expanded interpretations are possible. [ABSTRACT FROM AUTHOR]