Search Results (194)

This paper proposes a Fresnel-based Modified Moiré Varifocal Metalens (MMVL) addressing the inherent defocus at 0° rotation and significant focal quality degradation during varifocal operation in Traditional Moiré Varifocal Metalenses (TMVLs). The transmission function of the Fresnel-modified Moiré metalens combines a static term with a dynamic term, allowing the MMVLs to effectively overcome these limitations. Meanwhile, to minimize energy losses arising from polarization conversion and diffraction between the two metalenses, the nano-units on the metalenses are optimized by Particle Swarm Optimization (PSO) with FDTD simulations, maximizing the polarization conversion efficiency and transmittance. The simulation results demonstrate superior focal quality and stability in the MMVL throughout full rotational cycles, with super-diffraction-limited focusing maintained across all varifocal states. MMVLs have advantages in robustness; under axial distance variation (d = 0–20$d_0$, 0–3 μm), they maintain on-axis focus without deviation; with centering error (p = 0–10$p_0$, 0–3 μm), they sustain a clear focus at >36% efficiency. These results confirm that MMVLs have enhanced tolerance to manufacturing/assembly errors compared to TMVLs, delivering significantly stabilized optical performance. This advancement enables new possibilities for integrated micro-optics and optical tweezer applications. Full article

Particle Swarm Optimization (PSO) is a metaheuristic optimization technique based on population behavior, inspired by the movement of a flock of birds or a school of fish. In this method, particles move in a search space to find the global minimum of an objective function. In this work, a modified PSO algorithm written in Fortran 90 is proposed. The optimized structures obtained with this algorithm are compared with those obtained using the basin-hopping (BH) method written in Python (3.10), and complemented with density functional theory (DFT) calculations using the Gaussian 09 software. Additionally, the results are compared with the structural parameters reported from single crystal X-ray diffraction data for carbon clusters ${\mathrm{C}}_n$(n = 3–5), and tungsten–oxygen clusters, ${\mathrm{WO}}_n^{m-}$(n = 4–6, $m=2,4,6$). The PSO algorithm performs the search for the minimum energy of a harmonic potential function in a hyperdimensional space $\in {\mathbb{R}}^{3N}$ (where N is the number of atoms in the system), updating the global best position ( $g_{best}$) and local best position ( $p_{best}$), as well as the velocity and position vectors for each swarm cluster. A good approximation of the optimized structures and energies of these clusters was obtained, compared to the geometric optimization and single-point electronic energies calculated with the BH and DFT methods in the Gaussian 09 software. These results suggest that the PSO method, due to its low computational cost, could be useful for approximating a molecular structure associated with the global minimum of potential energy, accelerating the prediction of the most stable configuration or conformation, prior to ab initio electronic structure calculation. Full article

The performance of passive target localization is significantly influenced by the positions of unmanned aerial vehicle swarms (UAVs). In this paper, we investigate the problem of UAV path optimization to enhance the localization accuracy. Firstly, a passive target localization signal model based on the time difference of arrival (TDOA) algorithm, which is then improved by the Chan method and Taylor series expansion, is established. Secondly, the Cramer–Rao lower bound (CRLB) of the modified TDOA algorithm is derived and adopted as the evaluation criterion to optimize the UAVs’ positions at each time step. Different from the existing works, in this paper, we consider the UAVs to have variable speed; therefore, the feasible region of the UAVs’ positions is changed from a circle into an annular region, which will extend the feasible region, enhancing the localization accuracy while increasing the computation complexity. Thirdly, to improve the efficiency of the UAV path optimization algorithm, the particle swarm optimization (PSO) algorithm is applied to search for the optimal positions of the UAVs for the next time step. Finally, numerical simulations are conducted to verify the validity and effectiveness of the proposals in this paper. Full article

Cement–Soil Mixing (CSM) Pile is an important technology for soft ground reinforcement, and its as-formed compressive strength directly affects engineering design and construction quality. To address the significant discrepancy between laboratory-tested strength and field as-formed strength arising from differing environmental conditions, this study conducted modified laboratory experiments simulating key field formation characteristics. A cement–soil preparation system considering actual immersion conditions was established, based on controlling the initial water content state of the foundation soil before pile formation and applying submerged conditions post-formation. Utilizing data mining on 84 sets of experimental data with various preparation parameter combinations, a prediction model for the as-formed strength of CSM Pile was developed based on the Particle Swarm Optimization-Extreme Gradient Boosting (PSO-XGBoost) algorithm. Engineering validation demonstrated that the model achieved an RMSE of 0.138, an MAE of 0.112, and an R² of 0.961. It effectively addresses the issue of large prediction deviations caused by insufficient environmental simulation in traditional mix proportion tests. The research findings establish a quantitative relationship between as-formed strength and preparation parameters, providing an effective experimental improvement and strength prediction method for the engineering design of CSM Pile. Full article

This paper presents a decentralized energy management system (EMS) based on Multilayer Perceptron Artificial Neural Networks (MLP-ANNs) and a Modified Cheetah Optimizer (MCO) to account for uncertainty in renewable generation and load demand. The proposed framework applies an MLP-ANN with Levenberg–Marquardt (LM) training for high-precision forecasts of photovoltaic/wind generation, ambient temperature, and load demand, greatly outperforming traditional statistical methods (e.g., time-series analysis) and resilient backpropagation (RP) in precision. The new MCO algorithm eliminates local trapping and premature convergence issues in classical optimization methods like Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs). Simulations on a test microgrid verily demonstrate the advantages of the framework, achieving a 26.8% cost-of-operation reduction against rule-based EMSs and classical PSO/GA, and a 15% improvement in forecast accuracy using an LM-trained MLP-ANN. Moreover, demand response programs embodied in the system reduce peak loads by 7.5% further enhancing grid stability. The MLP-ANN forecasting–MCO optimization duet is an effective and cost-competitive decentralized microgrid management solution under uncertainty. Full article

Mineralogical variability exerts a profound influence on the flotation performance of Platinum Group Metal (PGM) ores, particularly those from the Platreef deposit, where complex associations and textures influence recovery, grade, and kinetics. This study integrates the Mode of Occurrence (MOC) and mineral associations into a modified Kelsall flotation kinetics model, optimized using a Particle Swarm Optimization (PSO) algorithm, to improve prediction accuracy. Batch flotation tests were conducted on eight samples from two lithologies—Pegmatoidal Feldspathic Pyroxenite (P-FPX) and Feldspathic Pyroxenite (FPX)—with mineralogical characterization performed using MLA, QEMSCAN, and XRD. PGMs in liberated (L) and sulfide-associated (SL) forms accounted for up to 90.6% (FPX1), exhibiting high fast-floating fractions (θ_f = 0.77–0.84) and fast flotation rate constants (K_f = 1.45–1.78 min⁻¹). In contrast, PGMs locked in silicates (G class) showed suppressed kinetics (K_f < 0.09 min⁻¹, K_s anomalies up to 8.67 min⁻¹) and were associated with lower recovery (P-FPX3 = 83.25%) and increased model error (P-FPX4 = 57.3). FPX lithologies achieved the highest cumulative recovery (FPX4 = 90.35%) and the best concentrate grades (FPX3 = 116.5 g/t at 1 min), while P-FPX1 had the highest gold content (10.45%) and peak recovery (94.37%). Grade-recovery profiles showed steep declines after 7 min, particularly in slow-floating types (e.g., P-FPX2, FPX2), with fast-floating lithologies stabilizing above 85% recovery at 20 min. The model yielded R² values above 0.97 across all samples. This validates the predictive power of MOC-integrated flotation kinetics for complex PGM ores and supports its application in geometallurgical plant design. Model limitations in capturing complex locked ore textures (SAG, G classes) highlight the need for reclassification based on floatability indices and further integration of machine learning methods. Full article

Based on uncertainty theory, this paper studies the problem of unmanned aerial vehicle (UAV) combat mission assignment under an uncertain environment. First, considering both the target value, which is the combat mission benefit gained from attacking the target, and the unit fuel consumption of UAV as uncertain variables, an uncertain UAV combat mission assignment model is established. And according to decisions under the realization of uncertain variables, the first stage generates an initial mission allocation scheme corresponding to the realization of target value, while the second stage dynamically adjusts the scheme according to the realization of unit fuel consumption; a two-stage uncertain UAV combat mission assignment (TUCMA) model is obtained. Then, because of the difficulty of obtaining analytical solutions due to uncertainty and the complexity of solving the second stage, the TUCMA model is transformed into an expected value-effective deterministic model of the two-stage uncertain UAV combat mission assignment (ETUCMA). A modified particle swarm optimization (PSO) algorithm is designed to solve the ETUCMA model to get the expected value-effective solution of the TUCMA model. Finally, experimental simulations of multiple UAV combat task allocation scenarios demonstrate that the proposed modified PSO algorithm yields an optimal decision with maximum combat mission benefits under a maximum iteration limit, which are significantly greater benefits than those for the mission assignment achieved by the original PSO algorithm. The proposed modified PSO exhibits superior performance compared with the ant colony optimization algorithm, enabling the acquisition of an optimal allocation scheme with greater benefits. This verifies the effectiveness and superiority of the proposed model and algorithm in maximizing combat mission benefits. Full article

In recent years, wireless sensor networks (WSNs) have been employed across various domains, including military services, healthcare, disaster response, industrial automation, and smart infrastructure. Due to the absence of fixed communication infrastructure, WSNs rely on ad hoc connections between sensor nodes to transmit sensed data to target nodes. Within a WSN, a sensor node whose failure partitions the network into disconnected segments is referred to as a critical node or cut vertex. Identifying such nodes is a fundamental step toward ensuring the reliability of WSNs. The critical node detection problem (CNDP) focuses on determining the set of nodes whose removal most significantly affects the network’s connectivity, stability, functionality, robustness, and resilience. CNDP is a significant challenge in network analysis that involves identifying the nodes that have a significant influence on connectivity or centrality measures within a network. However, achieving an optimal solution for the CNDP is often hindered by its time-consuming and computationally intensive nature, especially when dealing with large-scale networks. In response to this challenge, we present a method based on particle swarm optimization (PSO) for the detection of critical nodes. We employ discrete PSO (DPSO) along with the modified position equation (MPE) to effectively solve the CNDP, making it applicable to various k-vertex variations of the problem. We examine the impact of population size on both execution time and result quality. Experimental analysisusing different neighborhood topologies—namely, the star topology and the dynamic topology—was conducted to analyze their impact on solution effectiveness and adaptability to diverse network configurations. We consistently observed better result quality with the dynamic topology compared to the star topology for the same population size, while the star topology exhibited better execution time. Our findings reveal the promising efficacy of the proposed solution in addressing the CNDP, achieving high-quality solutions compared to existing methods. Full article

Recently, artificial intelligence (AI) has become a promising solution to the optimization of the energy harvesting and performance of photovoltaic (PV) systems. Traditional maximum power point tracking (MPPT) algorithms have several drawbacks on tracking the global maximum power point (GMPP) under partial shading conditions (PSCs). To track the GMPP, AI enabled methods stand out over other traditional solutions in terms of faster tracking dynamics, lesser oscillation, higher efficiency. However, such AI-based MPPT methods differ significantly in various applications, and thus, a full picture of AI-based MPPT methods is of interest to further optimize the PV energy harvesting. In this paper, various AI-based global maximum power point tracking (GMPPT) techniques are then implemented and critically compared by highlighting the advantages and disadvantages of each technique under dynamic weather conditions. The comparison demonstrates that the hybrid AI techniques are more reliable, which offer higher efficiency and better dynamics to handle PSCs. According to the benchmarking, a modified particle swarm optimization (PSO) GMPPT algorithm is proposed, and the experimental results validate its ability to achieve GMPPT with faster dynamics and higher efficiency. This paper is intended to motivate engineers and researchers by offering valuable insights for the selection and implementation of GMPPT techniques and to explore the AI techniques to enhance the efficiency and reliability of PV systems by providing fresh perspectives on optimal AI-based GMPPT techniques. Full article

Photovoltaic (PV) energy generation has seen rapid growth in recent years due to its sustainability and environmental benefits. However, accurately identifying PV panel parameters is crucial for enhancing system performance, especially under varying environmental conditions. This study presents an enhanced approach for estimating PV panel parameters using a Modified Quasi-Opposition-Based Killer Whale Optimization (MQOB-KWO) technique. The research aims to improve parameter extraction accuracy by optimizing the one-diode model (ODM), a widely used representation of PV cells, using a modified metaheuristic optimization technique. The proposed algorithm leverages a Quasi-Opposition-Based Learning (QOBL) mechanism to enhance search efficiency and convergence speed. The methodology involves implementing the MQOB-KWO in MATLAB R2021a and evaluating its effectiveness through experimental I-V data from two unlike photovoltaic panels. The findings are contrasted to established optimization techniques from the literature, such as the original Killer Whale Optimization (KWO), Improved Opposition-Based Particle Swarm Optimization (IOB-PSO), Improved Cuckoo Search Algorithm (ImCSA), and Chaotic Improved Artificial Bee Colony (CIABC). The findings demonstrate that the proposed MQOB-KWO achieves superior accuracy with the lowest Root Mean Square Error (RMSE) compared to other methods, and the lowest error rates (Root Mean Square Error—RMSE, and Integral Absolute Error—IAE) compared to the original KWO, resulting in a better value of the coefficient of determination (${\mathrm{R}}^2$), hence effectively capturing PV module characteristics. Additionally, the algorithm shows fast convergence, making it suitable for real-time PV system modeling. The study confirms that the proposed optimization technique is a reliable and efficient tool for improving PV parameter estimation, contributing to better system efficiency and operational performance. Full article

In the control of robot manipulators, input torque constraints and system nonlinearities present significant challenges for precise trajectory tracking. However, fuzzy adaptive logic control (FALC) often fails to generate the optimal membership functions or function intervals. This paper proposes a modified greater cane rat algorithm (MGCRA) to optimize a fuzzy adaptive logic controller (FALC) for minimizing input torques during trajectory tracking tasks. The main innovation lies in integrating the improved MGCRA with FALC, which enhances the controller’s adaptability and performance. For benchmarking, several state-of-the-art swarm intelligence algorithms—including particle swarm optimization (PSO), artificial bee colony (ABC), ant colony optimization (ACO), gray wolf optimization (GWO), covariance matrix adaptation evolution strategy (CMA-ES), adaptive guided differential evolution (AGDE), the basic greater cane rat algorithm (GCRA), and a trial-and-error method—are compared under identical conditions. Experimental results show that the MGCRA-tuned FALC achieves lower input torques and improved trajectory tracking accuracy compared to other methods. The findings demonstrate the effectiveness and potential of the proposed MGCRA-FALC framework for advanced robotic manipulator control. Full article

In this study, a brand-new algorithm called the Comprehensive Adaptive Enterprise Development Optimizer (CAED) is proposed to overcome the drawbacks of the Enterprise Development (ED) algorithm in complex optimization tasks. In particular, it aims to tackle the problems of slow convergence and low precision. To enhance the algorithm’s ability to break free from local optima, a lens imaging reverse learning approach is incorporated. This approach creates reverse solutions by utilizing the concepts of optical imaging. As a result, it expands the search range and boosts the probability of finding superior solutions beyond local optima. Moreover, an environmental sensitivity-driven adaptive inertial weight approach is developed. This approach dynamically modifies the equilibrium between global exploration, which enables the algorithm to search for new promising areas in the solution space, and local development, which is centered on refining the solutions close to the currently best-found areas. To evaluate the efficacy of the CAED, 23 benchmark functions from CEC2005 are chosen for testing. The performance of the CAED is contrasted with that of nine other algorithms, such as the Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO), and the Antlion Optimizer (AOA). Experimental findings show that for unimodal functions, the standard deviation of the CAED is almost 0, which reflects its high accuracy and stability. In the case of multimodal functions, the optimal value obtained by the CAED is notably better than those of other algorithms, further emphasizing its outstanding performance. The CAED algorithm is also applied to engineering optimization challenges, like the design of cantilever beams and three-bar trusses. For the cantilever beam problem, the optimal solution achieved by the CAED is 13.3925, with a standard deviation of merely 0.0098. For the three-bar truss problem, the optimal solution is 259.805047, and the standard deviation is an extremely small 1.11 × 10⁻⁷. These results are much better than those achieved by the traditional ED algorithm and the other comparative algorithms. Overall, through the coordinated implementation of multiple optimization strategies, the CAED algorithm exhibits high precision, strong robustness, and rapid convergence when searching in complex solution spaces. As such, it offers an efficient approach for solving various engineering optimization problems. Full article

In smart homes, heavy reliance on appliance automation has increased, along with the energy demand in developing urban areas, making efficient energy management an important factor. To address the scheduling of appliances under Demand-Side Management, this article explores the use of heuristic-based optimization techniques (HOTs) in smart homes (SHs) equipped with renewable and sustainable energy resources (RSERs) and energy storage systems (ESSs). The optimal model for minimization of the peak-to-average ratio (PAR), considering user comfort constraints, is validated by using different techniques, such as the Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), Wind-Driven Optimization (WDO), Bacterial Foraging Optimization (BFO) and the Genetic Modified Particle Swarm Optimization (GmPSO) algorithm, to minimize electricity costs, the PAR, carbon emissions and delay discomfort. This research investigates the energy optimization results of three real-world scenarios. The three scenarios demonstrate the benefits of gradually assembling RSERs and ESSs and integrating them into SHs employing HOTs. The simulation results show substantial outcomes, as in the scenario of Condition 1, GmPSO decreased carbon emissions from 300 kg to 69.23 kg, reducing emissions by 76.9%; bill prices were also cut from an unplanned value of 400.00 cents to 150 cents, a 62.5% reduction. The PAR was decreased from an unscheduled value of 4.5 to 2.2 with the GmPSO algorithm, which reduced the value by 51.1%. The scenario of Condition 2 showed that GmPSO reduced the PAR from 0.5 (unscheduled) to 0.2, a 60% reduction; the costs were reduced from 500.00 cents to 200.00 cents, a 60% reduction; and carbon emissions were reduced from 250.00 kg to 150 kg, a 60% reduction by GmPSO. In the scenario of Condition 3, where batteries and RSERs were integrated, the GmPSO algorithm reduced the carbon emission value to 158.3 kg from an unscheduled value of 208.3 kg, a reduction of 24%. The energy cost was decreased from an unplanned value of 500 cents to 300 cents with GmPSO, decreasing the overall cost by 40%. The GmPSO algorithm achieved a 57.1% reduction in the PAR value from an unscheduled value of 2.8 to 1.2. Full article

Background/Objectives: Lung cancer is a leading cause of cancer-related mortalities, with early diagnosis crucial for survival. While biopsy is the gold standard, manual histopathological analysis is time-consuming. This research enhances lung cancer diagnosis through deep learning-based feature extraction, fusion, optimization, and classification for improved accuracy and efficiency. Methods: The study begins with image preprocessing using an adaptive fuzzy filter, followed by segmentation with a modified simple linear iterative clustering (SLIC) algorithm. The segmented images are input into deep learning architectures, specifically ResNet-50 (RN-50), ResNet-101 (RN-101), and ResNet-152 (RN-152), for feature extraction. The extracted features are fused using a deep-weighted averaging-based feature fusion (DWAFF) technique, producing ResNet-X (RN-X)-fused features. To further refine these features, particle swarm optimization (PSO) and red deer optimization (RDO) techniques are employed within the selective feature pooling layer. The optimized features are classified using various machine learning classifiers, including support vector machine (SVM), decision tree (DT), random forest (RF), K-nearest neighbor (KNN), SoftMax discriminant classifier (SDC), Bayesian linear discriminant analysis classifier (BLDC), and multilayer perceptron (MLP). A performance evaluation is performed using K-fold cross-validation with K values of 2, 4, 5, 8, and 10. Results: The proposed DWAFF technique, combined with feature selection using RDO and classification with MLP, achieved the highest classification accuracy of 98.68% when using K = 10 for cross-validation. The RN-X features demonstrated superior performance compared to individual ResNet variants, and the integration of segmentation and optimization significantly enhanced classification accuracy. Conclusions: The proposed methodology automates lung cancer classification using deep learning, feature fusion, optimization, and advanced classification techniques. Segmentation and feature selection enhance performance, improving diagnostic accuracy. Future work may explore further optimizations and hybrid models. Full article

Accurate performance estimation of the entrained-flow pulverized coal gasification unit is essential for production scheduling and process optimization, but these are often hindered by inaccurate or insufficient measurements in the industrial system. This paper proposes a data reconciliation-based method to address this challenge. The thermodynamic equilibrium model is employed as constraints of the gasification and quench processes, and the Particle Swarm Optimization (PSO) algorithm is applied for parameter estimation. Measured data under stable and variable operating conditions are reconciled, detecting and eliminating a 12% error in syngas flow rate at the scrubber outlet, thereby improving gasification performance accuracy. Two characteristic models concerning carbon conversion rate and the flow rate of reacted quench water are derived from the reconciled results. By combining these models with thermodynamic equilibrium models, the modified R² of offline predicted syngas flow rate exceeds 0.92, and those of syngas compositions reach 0.72–0.85. Additionally, an Artificial Neural Network (ANN) model, trained on reconciled and predicted data, is proposed for real-time performance estimation. The ANN model calculates performance metrics within 10 s and achieves R² values above 0.95 for most parameters. This method can be integrated into control systems and serves as a valuable tool for gasification process monitoring and optimization. Full article