Nature-inspired metaheuristic algorithms have gained great attention over the last decade due to their potential for finding optimal solutions to different optimization problems. In this study, a metaheuristic based on the dwarf mongoose optimization algorithm (DMOA) is presented for the parameter estimation of an autoregressive exogenous (ARX) model. In the DMOA, the set of candidate solutions were stochastically created and improved using only one tuning parameter. The performance of the DMOA for ARX identification was deeply investigated in terms of its convergence speed, estimation accuracy, robustness and reliability. Furthermore, comparative analyses with other recent state-of-the-art metaheuristics based on Aquila Optimizer, the Sine Cosine Algorithm, the Arithmetic Optimization Algorithm and the Reptile Search algorithm—using a nonparametric Kruskal–Wallis test—endorsed the consistent, accurate performance of the proposed metaheuristic for ARX identification.

In this article, a chaotic computing paradigm is investigated for the parameter estimation of the autoregressive exogenous (ARX) model by exploiting the optimization knacks of an improved chaotic grey wolf optimizer (ICGWO). The identification problem is formulated by defining a mean square error-based fitness function between true and estimated responses of the ARX system. The decision parameters of the ARX model are calculated by ICGWO for various populations, generations, and noise levels. The comparative performance analyses with standard counterparts indicate the worth of the ICGWO for ARX model identification, while the statistical analyses endorse the efficacy of the proposed chaotic scheme in terms of accuracy, robustness, and reliability.

Swarm intelligence-based metaheuristic algorithms have attracted the attention of the research community and have been exploited for effectively solving different optimization problems of engineering, science, and technology. This paper considers the parameter estimation of the control autoregressive (CAR) model by applying a novel swarm intelligence-based optimization algorithm called the Aquila optimizer (AO). The parameter tuning of AO is performed statistically on different generations and population sizes. The performance of the AO is investigated statistically in various noise levels for the parameters with the best tuning. The robustness and reliability of the AO are carefully examined under various scenarios for CAR identification. The experimental results indicate that the AO is accurate, convergent, and robust for parameter estimation of CAR systems. The comparison of the AO heuristics with recent state of the art counterparts through nonparametric statistical tests established the efficacy of the proposed scheme for CAR estimation.

This research paper provides a comprehensive overview of the challenges and potential solutions related to enabling haptic communication over the Tactile Internet in the context of 6G networks. The increasing demand for multimedia services and device proliferation has resulted in limited radio resources, posing challenges in their efficient allocation for Device-to-Device (D2D)-assisted haptic communications. Achieving ultra-low latency, security, and energy efficiency are crucial requirements for enabling haptic communication over TI. The paper explores various methodologies, technologies, and frameworks that can facilitate haptic communication, including backscatter communications (BsC), non-orthogonal multiple access (NOMA), and software-defined networks. Additionally, it discusses the potential of unmanned aerial vehicles (UAVs), network slicing, and wireless communication beyond 100 GHz and THz levels in improving haptic communication performance. The research emphasizes the importance of addressing security risks, optimizing resource allocation, and minimizing network congestion to unlock the potential of future networks and services. Aerial, ground, and underwater communication technologies are highlighted as key components of 6G networks, each with their advantages and challenges. The need for specialized equipment in remote areas to meet the bandwidth and latency requirements of haptic communication is underscored. The findings of this research contribute to a deeper understanding of haptic communication in the context of 6G networks and provide insights into potential solutions for overcoming the associated challenges.

The efficient parameter estimation of harmonics is required to effectively design filters to mitigate their adverse effects on the power quality of electrical systems. In this study, a fractional order swarming optimization technique is proposed for the parameter estimation of harmonics normally present in industrial loads. The proposed fractional order particle swarm optimization (FOPSO) effectively estimates the amplitude and phase parameters corresponding to the first, third, fifth, seventh and eleventh harmonics. The performance of the FOPSO was evaluated for ten fractional orders with noiseless and noisy scenarios. The robustness efficiency of the proposed FOPSO was analyzed by considering different levels of additive white Gaussian noise in the harmonic signal. Monte Carlo simulations confirmed the reliability of the FOPSO for a lower fractional order ($\lambda$ = 0.1) with a faster convergence rate and no divergent run compared to other fractional orders as well as to standard PSO ($\lambda$ = 1).

In recent years, the wind energy conversion system faces significant challenges for stable and secure operations, especially in the weak grid due to high penetration of wind energy into the grid. Doubly fed induction generator (DFIG) is one of the most popular wind turbine generators due to its cost‐effective variable speed and low‐cost partial size power converter. Impedance interaction between series or parallel compensated weak grid and the DFIG system causes sub‐synchronous resonance (SSR) and high‐frequency resonance (HFR). A comprehensive review of the state‐of‐the‐art SSR and HFR in the DFIG system under a weak grid is presented. First, based on features, such as supplementary hardware, linear and non‐linear control strategies, the resonance damping techniques are classified into different types. Then, the advantages and disadvantages of SSR analysis and damping techniques techniques are put forward. Moreover, the latest development of state‐of‐the‐art SSR and HFR analysis and damping techniques are supplemented. Finally, the contemporary and future trends of the SSR and HFR techniques are discussed.