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In this study, a new hybrid metaheuristic algorithm named Chaotic Sand Cat Swarm Optimization (CSCSO) is proposed for constrained and complex optimization problems. This algorithm combines the features of the recently introduced SCSO with the concept of chaos. The basic aim of the proposed algorithm is to integrate the chaos feature of non-recurring locations into SCSO's core search process to improve global search performance and convergence behavior. Thus, randomness in SCSO can be replaced by a chaotic map due to similar randomness features with better statistical and dynamic properties. In addition to these advantages, low search consistency, local optimum trap, inefficiency search, and low population diversity issues are also provided. In the proposed CSCSO, several chaotic maps are implemented for more efficient behavior in the exploration and exploitation phases. Experiments are conducted on a wide variety of well-known test functions to increase the reliability of the results, as well as real-world problems. In this study, the proposed algorithm was applied to a total of 39 functions and multidisciplinary problems. It found 76.3% better responses compared to a best-developed SCSO variant and other chaotic-based metaheuristics tested. This extensive experiment indicates that the CSCSO algorithm excels in providing acceptable results. [ABSTRACT FROM AUTHOR]

The node deployment problem is a non-deterministic polynomial time (NP-hard). This study proposes a new and efficient method to solve this problem without the need for predefined circumstances about the environments independent of terrain. The proposed method is based on a metaheuristic algorithm and mimics the grey wolf optimizer (GWO) algorithm. In this study, we also suggested an enhanced version of the GWO algorithm to work adaptively in such problems and named it Mutant-GWO (MuGWO). Also, the suggested model ensures connectivity by generating topology graphs and potentially supports data transmission mechanisms. Therefore, the proposed method based on MuGWO can enhance resources utilization, such as reducing the number of nodes, by maximizing the coverage rate and maintaining the connectivity. While most studies assume classical rectangle uniform environments, this study also focuses on custom (environment-aware) maps in line with the importance and requirements of the real world. The motivation of supporting custom maps by this study is that environments can consist of custom shapes with prioritized and critical areas. In this way, environment awareness halts the deployment of nodes in undesired regions and averts resource waste. Besides, novel multi-purpose fitness functions of the proposed method satisfy a convenient approach to calculate costs instead of using complicated processes. Accordingly, this method is suitable for large-scale networks thanks to the capability of the distributed architecture and the metaheuristic-based approach. This study justifies the improvements in the suggested model by presenting comparisons with a Deterministic Grid-based approach and the Original GWO. Moreover, this method outperforms the fruit fly optimization algorithm, bat algorithm (BA), Optimized BA, harmony search, and improved dynamic deployment technique based on genetic algorithm methods in declared scenarios in literature, considering the results of simulations. [ABSTRACT FROM AUTHOR]

Purpose: Efficient resource utilization in wireless sensor networks is an important issue. Clustering structure has an important effect on the efficient use of energy, which is one of the most critical resources. However, it is extremely vital to choose efficient and suitable cluster head (CH) elements in these structures to harness their benefits. Selecting appropriate CHs and finding optimal coefficients for each parameter of a relevant fitness function in CHs election is a non-deterministic polynomial-time (NP-hard) problem that requires additional processing. Therefore, the purpose of this paper is to propose efficient solutions to achieve the main goal by addressing the related issues. Design/methodology/approach: This paper draws inspiration from three metaheuristic-based algorithms; gray wolf optimizer (GWO), incremental GWO and expanded GWO. These methods perform various complex processes very efficiently and much faster. They consist of cluster setup and data transmission phases. The first phase focuses on clusters formation and CHs election, and the second phase tries to find routes for data transmission. The CH selection is obtained using a new fitness function. This function focuses on four parameters, i.e. energy of each node, energy of its neighbors, number of neighbors and its distance from the base station. Findings: The results obtained from the proposed methods have been compared with HEEL, EESTDC, iABC and NR-LEACH algorithms and are found to be successful using various analysis parameters. Particularly, I-HEELEx-GWO method has provided the best results. Originality/value: This paper proposes three new methods to elect optimal CH that prolong the networks lifetime, save energy, improve overhead along with packet delivery ratio. [ABSTRACT FROM AUTHOR]

The performance of a model in machine learning problems highly depends on the dataset and training algorithms. Choosing the right training algorithm can change the tale of a model. While some algorithms have a great performance in some datasets, they may fall into trouble in other datasets. Moreover, by adjusting hyperparameters of an algorithm, which controls the training processes, the performance can be improved. This study contributes a method to tune hyperparameters of machine learning algorithms using Grey Wolf Optimization (GWO) and Genetic algorithm (GA) metaheuristics. Also, 11 different algorithms including Averaged Perceptron, FastTree, FastForest, Light Gradient Boost Machine (LGBM), Limited memory Broyden Fletcher Goldfarb Shanno algorithm Maximum Entropy (LbfgsMxEnt), Linear Support Vector Machine (LinearSVM), and a Deep Neural Network (DNN) including four architectures are employed on 11 datasets in different biological, biomedical, and nature categories such as molecular interactions, cancer, clinical diagnosis, behavior related predictions, RGB images of human skin, and X-rays images of Covid19 and cardiomegaly patients. Our results show that in all trials, the performance of the training phases is improved. Also, GWO demonstrates a better performance with a p-value of 2.6E-5. Moreover, in most experiment cases of this study, the metaheuristic methods demonstrate better performance and faster convergence than Exhaustive Grid Search (EGS). The proposed method just receives a dataset as an input and suggests the best-explored algorithm with related arguments. So, it is appropriate for datasets with unknown distribution, machine learning algorithms with complex behavior, or users who are not experts in analytical statistics and data science algorithms. [Display omitted] • The proposed method can improve the performance of the machine learning model independent from the composition of datasets. • It assists to gain a better performance faster than tuning by blindly and randomly chosen hyperparameters. • The idea of this method is general and it is not specific to a limited number of training algorithms and optimizers. • This method is convenient to use for users who are not experts in algorithm designing, also for real-world problems. • Generally, metaheuristic methods give better result and converge faster than blind approaches like Exhaustive Grid Search. [ABSTRACT FROM AUTHOR]