A harmony search algorithm for nurse rostering problems

Harmony search algorithm (HSA) is a relatively new nature-inspired algorithm. It evolves solutions in the problem search space by mimicking the musical improvisation process in seeking agreeable harmony measured by aesthetic standards. The nurse rostering problem (NRP) is a well-known NP-hard scheduling problem that aims at allocating the required workload to the available staff nurses at healthcare organizations to meet the operational requirements and a range of preferences. This work investigates research issues of the parameter settings in HSA and application of HSA to effectively solve complex NRPs. Due to the well-known fact that most NRPs algorithms are highly problem (or even instance) dependent, the performance of our proposed HSA is evaluated on two sets of very different nurse rostering problems. The first set represents a real world dataset obtained from a large hospital in Malaysia. Experimental results show that our proposed HSA produces better quality rosters for all considered instances than a genetic algorithm (implemented herein). The second is a set of well-known benchmark NRPs which are widely used by researchers in the literature. The proposed HSA obtains good results (and new lower bound for a few instances) when compared to the current state of the art of meta-heuristic algorithms in recent literature

Adaptive crossover memetic differential harmony search for optimizing document clustering

An Adaptive Crossover Memetic Differential Harmony Search (ACMDHS) method was developed for optimizing document clustering in this paper. Due to the complexity of the documents available today, the allocation of the centroid of the document clusters and finding the optimum clusters in the search space are more complex to deal with. One of the possible enhancements on the document clustering is the use of Harmony Search (HS) algorithm to optimize the search. As HS is highly dependent on its control parameters, a differential version of HS was introduced. In the modified version of HS, the Band Width parameter (BW) has been replaced by another pitch adjustment technique due to the sensitivity of the BW parameter. Thus, the Differential Evolution (DE) mutation was used instead. In this paper the DE crossover was also used with the Differential HS for further search space exploitation, the produced global search is named Crossover DHS (CDHS). Moreover, DE crossover (Cr) and mutation (F) probabilities are dynamically tuned through generations. The Memetic optimization was used to enhance the local search capability of CDHS. The proposed ACMDHS was compared to other document clustering techniques using HS, DHS, and K-means methods. It was also compared to its other two variants which are the Memetic DHS (MDHS) and the Crossover Memetic Differential Harmony Search (CMDHS). Moreover, two state-of-the-art clustering methods were also considered in comparisons, the Chaotic Gradient Artificial Bee Colony (CGABC) and the Differential Evolution Memetic Clustering (DEMC). From the experimental results, it was shown that CMDHS variant (the non-adaptive version of ACMDHS) and ACMDHS were highly competitive while both CMDHS and ACMDHS were superior to all other methods

Robust Kernelized Bayesian Matrix Factorization for Video Background/Foreground Separation

© Springer Nature Switzerland AG 2019. Development of effective and efficient techniques for video analysis is an important research area in machine learning and computer vision. Matrix factorization (MF) is a powerful tool to perform such tasks. In this contribution, we present a hierarchical robust kernelized Bayesian matrix factorization (RKBMF) model to decompose a data set into low rank and sparse components. The RKBMF model automatically infers the parameters and latent variables including the reduced rank using variational Bayesian inference. Moreover, the model integrates the side information of similarity between frames to improve information extraction from the video. We employ RKBMF to extract background and foreground information from a traffic video. Experimental results demonstrate that RKBMF outperforms state-of-the-art approaches for background/foreground separation, particularly where the video is contaminated