Currently, certain complex issues are classified as NP-hard problems, for which there is no exact solution, or they cannot be solved in a reasonable amount of time. As a result, metaheuristic algorithms have been developed as an alternative. These algorithms aim to approximate the optimal solution rather than providing a definitive one. Over recent years, these algorithms have gained considerable attention from the research community. Nature and its inherent principles serve as the primary inspiration for the development of metaheuristic algorithms. A notable subgroup of these algorithms is evolutionary algorithms, which are modeled based on the behavior of social and intelligent animals and organisms. However, each metaheuristic algorithm typically excels only with specific types of problems. As a result, researchers continuously endeavor to develop new algorithms. This study introduces a novel metaheuristic algorithm known as the Farmer Ants Optimization Algorithm (FAOA). The algorithm is inspired by the life of farmer ants, which cultivate mushrooms for food, protect them from pests, and nourish them as they grow. These behaviors, based on their social dynamics, serve as the foundation for the proposed algorithm. Experiments conducted on various engineering and classical problems have demonstrated that the FAOA provides acceptable solutions for discrete optimization problems.

Today, some complex problems are known as NP-hard problems. For this category of problems, there is no exact solution or they are not solvable in a reasonable time. For this reason, metaheuristic algorithms have been introduced and developed. These algorithms attempt to find an optimal solution to the problem instead of finding a definite solution. In recent years, these algorithms have gained significant attention from researchers. The major inspiration for metaheuristic algorithms is nature and its laws. An important category of these algorithms is evolutionary algorithms. These algorithms are inspired by the behavior of animals and living organisms that exhibit social and intelligent behavior. However, each metaheuristic algorithm may optimally solve just some types of problems. Therefore, researchers continuously try to introduce new algorithms. In this study, a new metaheuristic algorithm called Farmer Ants Optimization Algorithm (FAOA) is introduced. This algorithm is based on the intelligent life of farmer ants. Farmer ants cultivate mushrooms to provide food for themselves. They also protect them against various pests, and after growth, feed them. These special behaviors of farmer ants, which are based on their social life, are the source of inspiration for the proposed method. Experiments on some engineering and classical problems have shown that FAOA can provide an acceptable solution for discrete optimization problems.

Cardiac arrhythmia is a disorder caused by disruptions in the regular heart rhythm. Arrhythmias are categorized into two classes: sinus and non-sinus rhythms. Whereas sinus rhythms are generally low-risk, non-sinus rhythms are associated with higher risks of morbidity and mortality, including stroke and death. This research proposes a novel method, Optimized Hierarchical Fused Fuzzy Deep Reinforcement Learning OHFFDRL, which incorporates three steps for arrhythmia prediction: data preprocessing, reinforcement learning, and fuzzy deep learning. We evaluated the proposed method using a 12-lead electrocardiogram dataset comprising 10,646 patients. Our approach leverages recent advances in machine learning and medical science to achieve an accuracy of 94% in predicting non-sinus rhythms. Furthermore, the area under the ROC curve for OHFFDRL was 0.91, and the empirical ROC area was 0.90. In addition, the interpretability of the model has been analyzed with SHAP, LIME, Calibration Curve, Adversarial vulnerability, and Integrated Gradients. Our experimental results, among other findings, indicate that the most important feature for distinguishing heart rhythms is TAxis (the movement range in ventricular repolarization). These results demonstrate the potential of machine learning for the early prevention of heart disease through non-sinus rhythm prediction. The source code is available at (https://github.com/arman-daliri/OHFFDRL).

Proper grouping in classifier algorithms is a critical element of supervised learning. The first step in this is to have the correct data. Data that has a problem is worse than not having it. One of the biggest problems inherent in natural data is its imbalance. For a classifier algorithm to achieve its best performance, the first step is to fix the problem of data imbalance. To work with real datasets, the first step is to balance the data. The main problem with existing algorithms is to duplicate minority data and generate data that make outlines part of the primary data. The Statistic Deviation Mode Balancer (SDMB) algorithm solves this problem by making samples that adhere to the original data structure. Our proposed algorithm generates data that is very similar to the original data with the help of standard deviation and the amount of minor data mode and moving away from the majority part. Using these two parameters, the SDMB algorithm avoids Outlier data and generates clean data. The output of this algorithm is a balance datasheet that helps classifier algorithms learn the best way from the data. Different classifier algorithms with entirely different methods have been tested to prove this point. First, we balanced the different datasets with our method. Then, with varying classifier algorithms, we compared it with other existing algorithms. This experiment showed that our proposed algorithm is superior to other competitors and can be used in the work process of real datasets.