Experimental Study of Variation Local Search Mechanism for Bee Algorithm Feature Selection

The Bees Algorithm (BA) has been applied for finding the best possible subset features of a dataset. However, the main issue of the BA for feature selection is that it requires long computational time. This is due to the nature of BA combination search approach that exploits neighborhoods with random explorative. This situation creates unwanted suboptimum solution(s) leading to the lack of accuracy and longer processing time. A set of different local neighborhood search extension and their combination approaches have been proposed, including Simple-swap, 2-Opt, 3-Opt, and 4-Opt. The performance of the proposed mechanism was compared and analyzed using benchmark dataset. The results from experimental work confirmed that the proposed approach provides better accuracy with suitable time

Holey graphene: a unique structural derivative of graphene

Holey graphene (hG), also called graphene nanomesh, is a structural derivative of graphene. hG is formed by removing a large number of atoms from the graphitic plane to produce holes distributed on and through the atomic thickness of the graphene sheets. These holes, sometimes with abundant functional groups around their edges, impart properties that are uncommon to intact graphene but advantageous toward various applications. In this review, strategies to prepare hG and the related applications that take advantage of the unique structural motif of these materials are discussed. Prospects are then given for this emerging class of graphene derivatives