Artificial bee colony algorithm with adaptive covariance matrix for hearing loss detection

Artificial bee colony algorithm (ABC) is an efficient and popular evolutionary (EAs), which has been attracted wide attention by researchers, improved ABC with various characteristics (ABCs) have proposed. It widely acknowledged that the search operator core element in performance of ABC. However, generally designed operators ABCs are rotation-variable processes dependent mainly on natural coordinates and, as a result, those limited. In this paper, mathematical characteristic deeply analyzed, basis, adaptive covariance matrix (ACoM-ABC) proposed, (ACoM) used to establish proper making use population distribution information, can relieve dependence certain extent improve exploitation capability. To balance exploration abilities ABC, implemented eigen Then, estimate ACoM-ABC, compares six other EAs, tests CEC2014. The excellent experimental result shows ACoM-ABC outstanding algorithm. Moreover, proposed applied hearing loss detection, experiment overall accuracy 96.67%, higher than five state-of-the-art approaches about 1%. Therefore, practicability for realistic problems.