Accurate parameters identification of photovoltaic(PV) models is essential for state assessment of PV systems, as well as for supporting maximum power point tracking and system control, thus holding significant importance. To precisely identify ...

The vehicular mobility speed has a crucial impact on the performance of the data communication through the Vehicular Ad hoc Networks. The mobility of nodes is more predictable due to the roads’ structure and constraints; therefore, mobility speed ...

The reptile search algorithm (RSA) is a well-known swarm-based metaheuristic algorithm inspired by the hunting behaviors of crocodiles. To overcome the problems of falling into local optima and premature convergence, this paper proposes a multi-...

Ensemble clustering methods incorporate multiple base clusterings to provide a more accurate and reliable result compared to traditional clustering methods and have consequently gained popularity in recent years. In this paper, we propose a novel ...

3-1) With the increasing demand for large-scale data management and analytics, applications have diverse I/O needs, and various storage devices are being adapted for database systems to meet these specific I/O requirements. For example, ...

Federated learning is a privacy-preserving distributed machine learning framework that trains models over a number of clients while keeping data localized. To better mitigate the risks associated with information leakage, one common technique is ...

The rapid advancement of edge-cloud technologies has made service deployment increasingly crucial. Additionally, benefiting from the reusability of services, complex applications are subdivided into different microservices. Given the constraints ...

Traditional cloud architectures struggle to effectively allocate resources to container-based workloads due to fluctuating usage patterns and potential interference among multi-tenants. Conventional scheduling methods, which primarily rely on user-...

As electric vehicle technology continues to mature, a growing number of electric engineering and transport vehicles have been deployed in practice, leading to sustained interest in the Multi-Depot Electric Vehicle Routing Problem with Time Windows,...

Improper waste disposal by humans has created significant environmental issues in the marine ecosystem, including endangering aquatic life and accelerating the extinction of certain marine species. Due to the floating nature of the marine debris, ...

The COVID-19 pandemic has accelerated the demand for more efficient Smart Health Monitoring Systems (SHMS) in healthcare. While clustering techniques are widely used to improve energy efficiency and reduce data latency in smart hospitals, existing ...

Feature selection is aimed at reducing the dimensionality of datasets while maintaining or enhancing classification accuracy. Recent studies have increasingly approached feature selection through multi-modal, multi-objective optimization. However, ...

With the emergence of quantum computers, traditional cryptographic methods are vulnerable to attacks, emphasizing the need for post-quantum cryptography to secure devices and networks. This review paper critically analyzes the challenges and ...

In recent years, the number of smart devices and wireless data transmissions has increased worldwide. These emerging applications and services require not only extensive computing capabilities and high battery power, but also elevated data ...

Deep learning-based methods have shown promising performance in CSI-based fingerprint localization. However, current approaches often adopt feature interaction across different base stations (i.e., channels) throughout the entire process to ...

This paper presents two modified versions of the Tasmanian Devil Optimization (TDO) for dealing with the optimal power flow (OPF) problem in both conventional and advanced power systems. The original TDO is enhanced with two distinct constraint ...

Aiming at the problems of premature convergence and insufficient diversity of multi-objective firefly algorithm, this paper proposes a role division approach for firefly algorithm based on t-distribution perturbation and differential mutation. The ...

Honey badger algorithm (HBA) is a novel meta-heuristic algorithm inspired by the foraging behaviors of honey badgers, designed to tackle single-objective continuous optimization problems. While the standard HBA demonstrates strong performance ...

According to the No Free Lunch theorem, we need to propose new metaheuristic algorithms with different optimization behaviors to better solve global optimization problems. Therefore, this paper presents a novel metaheuristic called Philoponella ...

The Exponential Distribution Optimizer (EDO) is a population-based optimizer inspired by exponential distribution theory. It’s widely used in real-world scenarios due to its simple architecture and strong optimization capabilities. However, ...