As maritime transportation develops, the pressure of port traffic increases. To improve the management of ports and the efficiency of their operations, vessel scheduling must be optimized. The vessel scheduling problem can be divided into channel scheduling and berth allocation. We considered the complex problem of vessel scheduling in a restricted channel and the berth allocation problem, and a combined model that considers carbon emissions was developed. This model should reduce vessel waiting times, improve the quality of the berth loading and unloading service, meet the requirements of “green” shipping, and improve the overall scheduling efficiency and safety of ports. An adaptive, double-population, multi-objective genetic algorithm NSGA-II-DP is proposed to calculate the mathematical model. In the case study, the rationality verification and sensitivity analysis of the model and algorithm are conducted, and the NSGA-II-DP and NSGA-II were compared. Results demonstrate that the overall convergence of the NSGA-II-DP algorithm is better than that of NSGA-II, demonstrating that the NSGA-II-DP algorithm is a useful development of NSGA-II. In terms of port scheduling, the results of our model and algorithm, compared with the decisions provided by the traditional First Come First Service (FCFS) strategy, are more in line with the requirements for efficiency and cost in the actual port management, and more dominant in the port management can provide better decision support for the decision-makers.

Recently, robust adaptive filtering approaches relied on the Maximum Versoria Criterion (MVC) have gained the attention of researchers and have been widely studied. In this brief, with the energy conservation approach, transient and steady-state mean-square deviation (MSD) analysis of the standard constrained MVC (CMVC) are derived under both Gaussian and non-Gaussian noise distributions. For a Gaussian noise condition, an accurate solution is obtained, while for non-Gaussian noise conditions, we used approximate Taylor's expansion to derive the transient and steady-state MSD. Finally, we evaluated the theoretical analysis with some numerical simulations of system identification in different Gaussian and non-Gaussian noise scenarios to validate the finding.

In recent years, algorithms based on the Maximum Versoria Criterion (MVC) and a relative logarithmic cost function have gained the attention of researchers because they improve the filtering accuracy and robustness towards non-Gaussian/impulsive noises. To further enhance the steady-state convergence behavior of these aforementioned algorithms in impulsive noise scenarios, we introduce a generalized, robust logarithmic family (GRLF) framework that underlies the relative logarithmic cost function and MVC, and the corresponding GRLF-based adaptive filters called least mean square based-GRLF (GRLF-LMS) and least absolute difference based-GRLF (GRLF-LAD) algorithms are designed. With an approximate solution using Taylors expansion, the steady-state behavior of the GRLF-LMS and GRLF-LAD methods is presented. Then, algorithms are used in practical simulation studies in system identification to compare the performance of GRLF-LMS and GRLF-LAD filtering in Gaussian and non-Gaussian impulsive environments in terms of accuracy and robustness to that of their competitors.

In adaptive filtering for direction-of-arrival (DOA) estimation, traditional bias-compensated models often suffer from significant performance degradation in the presence of impulsive noise affecting both input and output signals. This paper addresses this challenge by introducing new algorithms for robust DOA estimation tailored for environments contaminated by impulsive noise. We initially employ the error-in-variable (EIV) model to derive a generalized framework for robust adaptive DOA estimation algorithms. Our methodology incorporates the Maximum Versoria (MV) criterion as a cost function, facilitating the development of the robust Gradient Total MV (GTMV) algorithm. To further enhance performance, we introduce a variable step-size mechanism within the GTMV framework (VSS-GTMV), which dynamically adjusts the step size based on the signal-to-noise ratio (SNR) to optimize the trade-off between convergence speed and steady-state error. The convergence properties of the GTMV algorithm are analytically examined, and its effectiveness is substantiated through extensive numerical simulations in impulsive noise environments. Comparative results demonstrate the superiority of our proposed methods over existing competitive approaches in terms of accuracy and robustness.

To solve the problem that the traditional ambiguity function cannot well reflect the time-frequency distribution characteristics of linear frequency modulated (LFM) signals due to the presence of impulsive noise, two robust ambiguity functions: correntropy-based ambiguity function (CRAF) and fractional lower order correntropy-based ambiguity function (FLOCRAF) are defined based on the feature that correntropy kernel function can effectively suppress impulsive noise. Then these two robust ambiguity functions are used to estimate the direction of arrival (DOA) of narrowband LFM signal under an impulsive noise environment. Instead of the covariance matrix used in the ESPRIT algorithm by the spatial CRAF matrix and FLOCRAF matrix, the CRAF-ESPRIT and FLOCRAF-ESPRIT algorithms are proposed. Computer simulation results show that compared with the algorithms only using ambiguity function and the algorithms only using the correntropy kernel function-based correlation, the proposed algorithms using ambiguity function based on correntropy kernel function have good performance in terms of probability of resolution and estimation accuracy under various circumstances. Especially, the performance of the FLOCRAF-ESPRIT algorithm is better than the CRAF-ESPRIT algorithm in the environment of low generalized signal-to-noise ratio and strong impulsive noise.

In recent years, the spotlight of research in the adaptive learning era has turned towards distributed adaptive filtering algorithms for sparse systems over wireless sensor networks (WSN) leveraging error nonlinearities. This interest has been sparked by their exceptional efficiency in both Gaussian and non-Gaussian/impulsive noise environments, leading to the development of many adaptive filtering algorithms. Notably, among these approaches is the proportionate robust diffusion recursive exponential hyperbolic cosine (PR-DREHC) algorithm, a recent method based on the exponential hyperbolic cosine function (EHCF). While the PR-DREHC algorithm is robust against impulsive noise distortion, it remains afflicted by notable shortcomings such as pronounced computational burden and notable steady-state misalignment. To address these limitations, this study introduces a novel approach by introducing a new cost function termed the generalized correntropy-based modified sigmoid function (GCMSF). Our findings substantiate that the GCMSF holds the potential to engender robust algorithms distinguished by enhanced performance in terms of steady-state misalignment and expedited convergence. Capitalizing on this insight, we present novel robust diffusion recursive algorithms formulated through the maximization of the proposed GCMSF cost function. This ensemble encompasses the robust proportionate diffusion recursive generalized correntropy-based modified sigmoid function (RP-DRGCMSF) algorithm. The mean and mean square stability conditions of the introduced approach are also derived. Finally, the proposed algorithm is applied to practical simulation studies to examine its performance under different Gaussian and non-Gaussian noise environments and compare it to its respective competitors.