Late acceptance hill climbing aided chaotic harmony search for feature selection: An empirical analysis on medical data

In today's era of data-driven digital society, there is a huge demand for optimized solutions that essentially reduce the cost of operation, thereby aiming to increase productivity. Processing a huge amount of data, like the Microarray based gene expression data, using machine learning and data mining algorithms has certain limitations in terms of memory and time requirements. This would be more concerning, when a dataset comes with redundant and non-important information. For example, many report-based medical datasets have several non-informative attributes which mislead the classification algorithms. To this end, researchers have been developing several feature selection algorithms that try to discard the redundant information from the raw datasets before feeding them to machine learning algorithms. Metaheuristic based optimization algorithms provide an excellent option to solve feature selection problems. In this paper, we propose a music-inspired harmony search (HS) algorithm based wrapper feature selection method. At the beginning, we use a chaotic mapping to initialize the population of the HS algorithm in order to better coverage of the search space. Further to complement the inferior exploitation of the HS algorithm, we integrate it with the Late Acceptance Hill Climbing (LAHC) method. Thus the combination of these two algorithms provides a good balance between the exploration and exploitation of the HS algorithm. We evaluate the proposed feature selection method on 15 UCI datasets and the obtained results are found to be better than many state-of-the-art methods both in terms of the classification accuracy and the number of features selected. To evaluate the effectiveness of our algorithm, we utilize a combination of precision, recall, F1 score, fitness value, and execution time as performance indicators. These metrics enable us to obtain a comprehensive assessment of the algorithm's abilities and limitations. We also apply our method on 3 microarray based gene expression datasets used for prediction of cancer to ensure the scalability and robustness as a feature selection method in real-life scenarios. In addition to this, we test our approach using the COVID-19 dataset, and it performs better than several metaheuristic based optimization techniques. © 2023

A novel hybrid DMHS-GMDH algorithm to predict COVID-19 pandemic time series

In this paper, a novel hybrid method called DMHS-GMDH is presented to predict the time series of COVID-19 outbreaks. In this way, a new version of Harmony Search (HS) algorithm, named Double Memory HS (DMHS), is designed to optimize the structure of a Group Method of Data Handling (GMDH) type neural network. We conduct a series of experiments by applying proposed method on real COVID-19 dataset to forecast new cases and deaths of COVID-19. The statistical analysis indicates that the DMHS-GMDH algorithm on average provides better results than other competitors and the results demonstrate how our approach at least improves coefficient of determination and RMSE by 21% and 45%, respectively. © 2021 IEEE.

Annealing Harmony Search Algorithm to Solve the Nurse Rostering Problem

A real-life problem is the rostering of nurses at hospitals. It is a famous nondeterministic, polynomial time (NP) -hard combinatorial optimization problem. Handling the real-world nurse rostering problem (NRP) constraints in distributing workload equally between available nurses is still a difficult task to achieve. The international shortage of nurses, in addition to the spread of COVID-19, has made it more difficult to provide convenient rosters for nurses. Based on the literature, heuristic-based methods are the most commonly used methods to solve the NRP due to its computational complexity, especially for large rosters. Heuristic-based algorithms in general have problems striking the balance between diversification and intensification. Therefore, this paper aims to introduce a novel metaheuristic hybridization that combines the enhanced harmony search algorithm (EHSA) with the simulated annealing (SA) algorithm called the annealing harmony search algorithm (AHSA). The AHSA is used to solve NRP from a Malaysian hospital. The AHSA performance is compared to the EHSA, climbing harmony search algorithm (CHSA), deluge harmony search algorithm (DHSA), and harmony annealing search algorithm (HAS). The results show that the AHSA performs better than the other compared algorithms for all the tested instances where the best ever results reported for the UKMMC dataset. © 2022 Tech Science Press. All rights reserved.