Search Results (62)

Adaptive filtering remains a cornerstone of modern signal processing but faces fundamental challenges when confronted with rapidly changing or nonlinear environments. This work investigates the integration of deep learning into adaptive-filter architectures to enhance tracking capability and robustness in non-stationary conditions. After reviewing and analyzing classical algorithms—LMS, NLMS, RLS, and a variable step-size LMS (VSS-LMS)—their theoretical stability and mean-square error behavior are formalized under a slow-variation system model. Comprehensive simulations using drifting autoregressive (AR(2)) processes, piecewise-stationary FIR systems, and time-varying sinusoidal signals confirm the classical trade-off between performance and complexity: RLS achieves the lowest steady-state error, at a quadratic cost, whereas LMS remains computationally efficient with slower adaptation. A stabilized VSS-LMS algorithm is proposed to balance these extremes; the results show that it maintains numerical stability under abrupt parameter jumps while attaining steady-state MSEs that are comparable to RLS (approximately 3 × 10⁻²) and superior robustness to noise. These findings are validated by theoretical tracking-error bounds that are derived for bounded parameter drift. Building on this foundation, a deep-learning-driven adaptive filter is introduced, where the update rule is parameterized by a neural function, $U_{\theta }$, that generalizes the classical gradient descent. This approach offers a pathway toward adaptive filters that are capable of self-tuning and context-aware learning, aligning with emerging trends in AI-augmented system architectures and next-generation computing. Future work will focus on online learning and FPGA/ASIC implementations for real-time deployment. Full article

This work proposes an Enhanced Chimp Optimization Algorithm (EChOA) for solving continuous and constrained data science and engineering optimization problems. The EChOA integrates a self-adaptive DE/current-to-pbest/1 (with jDE-style parameter control) variation stage with the canonical four-leader ChOA guidance and augments the search with three lightweight modules: (i) L’evy flight refinement around the incumbent best, (ii) periodic elite opposition-based learning, and (iii) stagnation-aware partial restarts. The EChOA is compared with more than 35 optimizers on the CEC2022 single-objective suite (12 functions). The results shows that the EChOA attains state-of-the-art results at both $D=10$ and $D=20$. At $D=10$, it ranks first on all functions (average rank $1.00$; $12/12$ wins) with the lowest mean objective and the smallest dispersion relative to the strongest competitor (OMA). At $D=20$, the EChOA retains the best overall rank and achieves top scores on most functions, indicating stable scalability with problem dimension. Pairwise Wilcoxon signed-rank tests ($\alpha =0.05$) against the full competitor set corroborate statistical superiority on the majority of functions at both dimensions, aligning with the aggregate rank outcomes. Population size studies indicate that larger populations primarily enhance reliability and time to improvement while yielding similar terminal accuracy under a fixed iteration budget. Four constrained engineering case studies (including welded beam, helical spring, pressure vessel, and cantilever stepped beam) further confirm practical effectiveness, with consistently low cost/weight/volume and tight dispersion. Full article

If S and T are two non-self-mappings, then a solution of equation $S{a}^{*}=T{a}^{*}=a^{*}$ does not necessarily exist. The common best proximity point problem is to find the approximate optimal solution of such type of equation and have a key role in theory of approximation and optimization. The primary goal of this paper is to introduce an inertial-type self-adaptive algorithm for solving the common best proximity point, generalized equilibrium and split variational inclusion problems in Hilbert spaces. The strong convergence of the proposed algorithm is given under some mild conditions. It is worth mentioning that the step size in many existing algorithms requires the prior knowledge of operator norms which is difficult to compute, whereas our proposed algorithm does not require this condition. Numerical examples are given to illustrate the efficiency and applicability of the proposed approach. We further apply the proposed algorithm to an image restoration problem and show that it achieves a higher signal-to-noise ratio compared with the existing algorithms considered in this study. Full article

Background/Objectives: Conventional solidification methods for liquid self-nanoemulsifying drug delivery systems face significant limitations. This includes complex manufacturing processes, high costs, and environmental concerns. This study aimed to develop and optimize a thermoresponsive self-nanoemulsifying drug delivery system (T-SNEDDS) for dapagliflozin as a sustainable alternative solidification technique. Methods: Oil and surfactant were selected based on solubility and emulsification studies. The Box–Behnken approach was used to examine the impacts of three independent variables (pluronic F127, propylene glycol, and dapagliflozin concentrations) on liquefying temperature and time. Optimized T-SNEDDS was characterized in terms of particle size, zeta potential, and dissolution performance. Stability assessment included centrifugation testing and a six-month storage evaluation. The green pharmaceutical performance was comparatively evaluated against five conventional solidification methods using ten adapted parameters. Results: Imwitor 308 and Cremophor EL were selected as optimal excipients for SNEDDS formulation. In addition, Pluronic F127 and propylene glycol were used to induce solidification during storage. The optimized formulation (8.60% w/w Pluronic F127, 10% w/w propylene glycol, and 5% w/w dapagliflozin) exhibited a liquefying temperature of 33.5 °C with a liquefying time of 100.3 s and a particle size of 96.64 nm. T-SNEDDS significantly enhanced dissolution efficiency of dapagliflozin (95.7%) compared to raw drug (42.4%) and marketed formulation (91.3%). Green pharmaceutical evaluation revealed that T-SNEDDS achieved the highest score compared to conventional approaches. Conclusions: T-SNEDDS represents a superior sustainable approach for SNEDDS solidification that offers enhancement in drug dissolution while addressing manufacturing, environmental, and economic challenges through its solvent-free and single-step preparation process with excellent scalability potential. Full article

This paper presents a novel Ripple Evolution Optimizer (REO) that incorporates adaptive and diversified movement—a population-based metaheuristic that turns a coastal-dynamics metaphor into principled search operators. REO augments a JADE-style current-to-p-best/1 core with jDE self-adaptation and three complementary motions: (i) a rank-aware that pulls candidates toward the best, (ii) a time-increasing that aligns agents with an elite mean, and (iii) a scale-aware sinusoidal that lead solutions with a decaying envelope; rare Lévy-flight kicks enable long escapes. A reflection/clamp rule preserves step direction while enforcing bound feasibility. On the CEC2022 single-objective suite (12 functions spanning unimodal, rotated multimodal, hybrid, and composition categories), REO attains 10 wins and 2 ties, never ranking below first among 34 state-of-the-art compared optimizers, with rapid early descent and stable late refinement. Population-size studies reveal predictable robustness gains for larger N. On constrained engineering designs, REO achieves outperforming results on Welded Beam, Spring Design, Three-Bar Truss, Cantilever Stepped Beam, and 10-Bar Planar Truss. Altogether, REO couples adaptive guidance with diversified perturbations in a compact, transparent optimizer that is competitive on rugged benchmarks and transfers effectively to real engineering problems. Full article

We propose a tri-fold learning algorithm, called Neuroevolution of Hybrid Neural Networks in a Robotic Agent (acronym in Spanish, NRNH-AR), based on deep reinforcement learning (DRL), with self-supervised learning (SSL) and unsupervised learning (USL) steps, specifically designed to be implemented in a small autonomous navigation robot capable of operating in constrained physical environments. The NRNH-AR algorithm is designed for a small physical robotic agent with limited resources. The proposed algorithm was evaluated in four critical aspects: computational cost, learning stability, required memory size, and operation speed. The results obtained show that the performance of NRNH-AR is within the ranges of the Deep Q Network (DQN), Deep Deterministic Policy Gradient (DDPG), and Twin Delayed Deep Deterministic Policy Gradient (TD3). The proposed algorithm comprises three types of learning algorithms: SSL, USL, and DRL. Thanks to the series of learning algorithms, the proposed algorithm optimizes the use of resources and demonstrates adaptability in dynamic environments, a crucial aspect of navigation robotics. By integrating computer vision techniques based on a Convolutional Neuronal Network (CNN), the algorithm enhances its abilities to understand visual observations of the environment rapidly and detect a specific object, avoiding obstacles. Full article

To address the obstacle-avoidance path-planning requirements of dual-arm robots operating in complex environments, such as chemical laboratories and biomedical workstations, this paper proposes ODSN-RRT (optimization-direction-step-node RRT), an efficient planner based on rapidly-exploring random trees (RRT). ODSN-RRT integrates three key optimization strategies. First, a two-stage sampling-direction strategy employs goal-directed growth until collision, followed by hybrid random-goal expansion. Second, a dynamic safety step-size strategy adapts each extension based on obstacle size and approach angle, enhancing collision detection reliability and search efficiency. Third, an expansion-node optimization strategy generates multiple candidates, selects the best by Euclidean distance to the goal, and employs backtracking to escape local minima, improving path quality while retaining probabilistic completeness. For collision checking in the dual-arm workspace (self and environment), a cylindrical-spherical bounding-volume model simplifies queries to line-line and line-sphere distance calculations, significantly lowering computational overhead. Redundant waypoints are pruned using adaptive segmental interpolation for smoother trajectories. Finally, a master-slave planning scheme decomposes the 14-DOF problem into two 7-DOF sub-problems. Simulations and experiments demonstrate that ODSN-RRT rapidly generates collision-free, high-quality trajectories, confirming its effectiveness and practical applicability. Full article

Variational inequality problems (VIPs) provide a versatile framework for modeling a wide range of real-world applications, including those in economics, engineering, transportation, and image processing. In this paper, we propose a novel iterative algorithm for solving VIPs in real Hilbert spaces. The method integrates a double-inertial mechanism with the two-subgradient extragradient scheme, leading to improved convergence speed and computational efficiency. A distinguishing feature of the algorithm is its self-adaptive step size strategy, which generates a non-monotonic sequence of step sizes without requiring prior knowledge of the Lipschitz constant. Under the assumption of monotonicity for the underlying operator, we establish strong convergence results. Numerical experiments under various initial conditions demonstrate the method’s effectiveness and robustness, confirming its practical advantages and its natural extension of existing techniques for solving VIPs. Full article

Herein, we present two hybrid inertial self-adaptive iterative methods for determining the combined solution of the split variational inclusions and fixed-point problems. Our methods include viscosity approximation, fixed-point iteration, and inertial extrapolation in the initial step of each iteration. We employ two self-adaptive step sizes to compute the iterative sequence, which do not require the pre-calculated norm of a bounded linear operator. We prove strong convergence theorems to approximate the common solution of the split variational inclusions and fixed-point problems. Further, we implement our methods and results to examine split variational inequality and split common fixed-point problems. Finally, we illustrate our methods and compare them with some known methods existing in the literature. Full article

In this work, we study a class of variational inequality problems defined over the intersection of sub-level sets of a countable family of convex functions. We propose a new iterative method for approximating the solution within the framework of Hilbert spaces. The method incorporates several strategies, including inertial effects, a self-adaptive step size, and a relaxation technique, to enhance convergence properties. Notably, it requires computing only a single projection onto a half space. Using some mild conditions, we prove that the sequence generated by our proposed method is strongly convergent to a minimum-norm solution to the problem. Finally, we present some numerical results that validate the applicability of our proposed method. Full article

In this paper, an Improved Manta Ray Foraging Optimization (IMRFO) algorithm is proposed to address the challenge of parameter tuning in traditional PID controllers for artillery stabilization systems. The proposed algorithm introduces chaotic mapping to optimize the initial population, enhancing the global search capability; additionally, a sigmoid function and Lévy flight-based dynamic adjustment strategy regulate the selection factor and step size, improving both convergence speed and optimization accuracy. Comparative experiments using five benchmark test functions demonstrate that the IMRFO algorithm outperforms five commonly used heuristic algorithms in four cases. The proposed algorithm is validated through co-simulation and physical platform experiments. Experimental results show that the proposed approach significantly improves control accuracy and response speed, offering an effective solution for optimizing complex nonlinear control systems. By introducing heuristic optimization algorithms for self-tuning artillery stabilization system parameters, this work provides a new approach to enhancing the intelligence and adaptability of modern artillery control. Full article

The automatic and accurate segmentation of bladder tumors is a key step in assisting urologists in diagnosis and analysis. At present, existing Transformer-based methods have limited ability to restore local detail features and insufficient boundary segmentation capabilities. We propose FEBE-Net, which aims to effectively capture global and remote semantic features, preserve more local detail information, and provide clearer and more precise boundaries. Specifically, first, we use PVT v2 backbone to learn multi-scale global feature representations to adapt to changes in bladder tumor size and shape. Secondly, we propose a new feature exploration attention module (FEA) to fully explore the potential local detail information in the shallow features extracted by the PVT v2 backbone, eliminate noise, and supplement the missing fine-grained details for subsequent decoding stages. At the same time, we propose a new boundary enhancement and refinement module (BER), which generates high-quality boundary clues through boundary detection operators to help the decoder more effectively preserve the boundary features of bladder tumors and refine and adjust the final predicted feature map. Then, we propose a new efficient self-attention calibration decoder module (ESCD), which, with the help of boundary clues provided by the BER module, gradually and effectively recovers global contextual information and local detail information from high-level features after calibration enhancement and low-level features after exploration attention. Extensive experiments on the cystoscopy dataset BtAMU and five colonoscopy datasets have shown that FEBE-Net outperforms 11 state-of-the-art (SOTA) networks in segmentation performance, with higher accuracy, stronger robust stability, and generalization ability. Full article

Aiming to address the problems of nonlinearity, a large time delay, poor adjustment ability, and a difficult parameter setting process of the tension control system of belt conveyor tensioning devices, an adaptive Proportional-Integral-Derivative (PID) parameter self-tuning algorithm based on an improved seeker optimization algorithm (ISOA) is proposed in this paper. The algorithm uses inertia weight random mutation to determine step size. An improved boundary reflection strategy avoids the defect of a large number of out-of-bound individuals gathering on the boundary in a traditional algorithm, and projects the individual reflection beyond the boundary into the boundary, which increases the diversity of the population and improves the convergence accuracy of the algorithm. To improve the system response speed and suppress the overshoot problem of the control target, coefficients related to the proportional term are introduced into the fitness function to accelerate the convergence of the algorithm. The improved algorithm is tested on three test functions such as Sphere and compared with other classical algorithms, which verify that the proposed algorithm is better in accuracy and stability. Finally, the interference and tracking performance of the ISOA-PID controller are verified in industrial experiments, which show that the PID controller optimized using the ISOA has good control quality and robustness. Full article

In this paper, we investigate a class of hierarchical variational inequalities (HVIPs, i.e., strongly monotone variational inequality problems defined on the solution set of multiple-set split common fixed-point problems) with quasi-pseudocontractive mappings in real Hilbert spaces, with special cases being able to be found in many important engineering practical applications, such as image recognizing, signal processing, and machine learning. In order to solve HVIPs of potential application value, inspired by the primal-dual algorithm, we propose a novel accelerated cyclic iterative algorithm that combines the inertial method with a correction term and a self-adaptive step-size technique. Our approach eliminates the need for prior knowledge of the bounded linear operator norm. Under appropriate assumptions, we establish strong convergence of the algorithm. Finally, we apply our novel iterative approximation to solve multiple-set split feasibility problems and verify the effectiveness of the proposed iterative algorithm through numerical results. Full article

In this paper, two Halpern-type inertial iteration methods with self-adaptive step size are proposed for estimating the solution of split common null point problems (${\mathrm{S}}_{\mathrm{p}}\mathrm{CNPP}$) in such a way that the Halpern iteration and inertial extrapolation are computed simultaneously in the beginning of each iteration. We prove the strong convergence of sequences driven by the suggested methods without estimating the norm of bounded linear operator when certain appropriate assumptions are made. We demonstrate the efficiency of our iterative methods and compare them with some related and well-known results using relevant numerical examples. Full article