Feature Selection (FS) is a crucial component of machine learning and data mining. Its goal is to eliminate redundant and irrelevant features from a datasets, thereby enhancing the classifier's performance. The Grey Wolf Optimizer (GWO) is a well-known meta-heuristic algorithm rooted in swarm intelligence. It is widely used in various optimization problems due to its fast convergence and minimal parameter requirements. However, in the context of solving high-dimensional classification problems, GWO’s global search capability is limited, and it is susceptible to getting trapped in local optima. To address this, we introduce an Adaptive Mechanism-based Grey Wolf Optimizer (AMGWO) for FS in high-dimensional classification. This approach encompasses a novel nonlinear parameter control strategy to balance exploration and exploitation effectively, thereby preventing the algorithm from converging prematurely. Additionally, an adaptive fitness distance balancing mechanism is proposed to prevent premature convergence and enhance search efficiency by selecting high-potential solutions. Lastly, an adaptive neighborhood mutation mechanism is designed to adjust mutation intensity adaptively during the search process, allowing AMGWO to more effectively find the global optimum. To validate the proposed AMGWO method, we assess its performance on 15 high-dimensional datasets and compare it with the original GWO and five of its variants in terms of classification accuracy, feature subset size, and execution speed, thus confirming the superiority of AMGWO.

Fire smoke detection plays a pivotal role in our life. For the optical fire smoke detector, it is easy to produce false alarms due to the misidentification of non-fire aerosols, such as dust and water mist. In this research, four multilayer perceptron (MLP) models are established and applied to discriminate the fire smokes and non-fire aerosols. A new hybrid algorithm (HBOBES) is proposed to optimize the parameters of MLP, which incorporates three search phases, including space selecting, promiscuous and restrictive mating, and consortship and extra-group mating, which are derived from the basic bonobo optimizer (BO) and bald eagle search algorithm (BES). Moreover, a adaptive tent chaos mapping technique is introduced into the first two phases to increase the population diversity. In the experiments, eight standard classification datasets and sixteen self-established aerosol classification datasets are applied to evaluate HBOBES’s optimization performance on training the MLP models. The results show that the proposed HBOBES ranks first overall, showing the merits in training MLP models compared to eight other algorithms. For the aerosol classification problem, it is found that both the optimized 3-7-2 and 3-7-3 MLP models have achieved the highest classification accuracy of approximately 95%. Black smokes can be identified with 100% classification accuracy. Therefore, this paper provides an effective and feasible approach to distinguish fire and non-fire aerosols by using the MLP classifier optimized by optimization algorithm, which has practical significance for the development of intelligent optical smoke detector.

Accurate parameter estimation is essential for gravitational wave data analysis. In extreme mass-ratio inspiral binary systems, orbital eccentricity is a critical parameter for parameter estimation. However, the current software for the parameter estimation of the gravitational wave often neglects the direct estimation of orbital eccentricity. To fill this gap, we have developed the MatBYIB, a MATLAB-based software (Version 1.0) package for the parameter estimation of the gravitational wave with arbitrary eccentricity. The MatBYIB employs the Analytical Kludge waveform as a computationally efficient signal generator and computes parameter uncertainties via the Fisher Information Matrix and the Markov Chain Monte Carlo. For Bayesian inference, we implement the Metropolis–Hastings algorithm to derive posterior distributions. To guarantee convergence, the Gelman–Rubin convergence criterion (the Potential Scale Reduction Factor $\widehat{R}$) is used to determine sampling adequacy, with MatBYIB dynamically increasing the sample size until $\widehat{R}<1.05$ for all parameters. Our results demonstrate strong agreement between predictions based on the Fisher Information Matrix and full MCMC sampling. This program is user-friendly and allows for the estimation of the gravitational wave parameters with arbitrary eccentricity on standard personal computers.

Extreme mass-ratio inspirals (EMRIs) are among the most promising sources for future space-based gravitational wave (GW) observatories and provide sensitive probes of additional fundamental fields in the strong-gravity regime. In the present work, we investigate eccentric equatorial EMRIs around Kerr black holes in the presence of a massive scalar field. We assume that the inspiraling object carries a scalar charge and, therefore, emits scalar radiation in addition to GWs. By solving the massive scalar perturbation equation in the frequency domain, we compute the relativistic scalar energy fluxes at the event horizon and at infinity and incorporate them into an adiabatic inspiral model. We investigate the impact of the scalar charge and scalar-field mass on the orbital evolution and gravitational waveforms by analyzing the accumulated phase differences and waveform mismatches relative to both general relativity and the massless scalar limit. Our results show that the massive scalar radiation can produce significant GW dephasing, with the effect becoming increasingly pronounced for more eccentric orbits. In addition, the scalar flux is suppressed for larger scalar-field masses as fewer scalar harmonics satisfy the propagation condition at infinity. Finally, using both Fisher information matrix forecasts and Bayesian parameter estimation, we assess the capability of the Laser Interferometer Space Antenna to constrain the scalar charge and scalar-field mass. The two inference approaches gives consistent constraints, demonstrating that eccentric EMRIs provide a promising avenue for probing massive scalar fields and scalar-tensor extensions of gravity in the strong-field regime.