Search Results (4,511)

With the increasing conflict between economic and environmental objectives in power systems, the economic emission dispatch (EED) problem has become a highly constrained, nonlinear, and strongly non-convex multi-objective optimization problem due to valve-point effects and nonlinear constraints such as network losses. To address this challenge, this paper proposes an improved multi-objective Grey Wolf Optimizer (IMOGWO). The proposed method enhances search performance through four strategies: a hybrid initialization scheme combining circle chaotic mapping and Latin hypercube sampling to improve population diversity, a dream-inspired group perturbation mechanism to strengthen global exploration, a nonlinearly decreasing convergence factor to dynamically balance exploration and exploitation, and a hybrid update strategy incorporating Lévy flight to avoid local optima. Experimental results demonstrate that IMOGWO can effectively balance the trade-off between generation cost and pollutant emissions while exhibiting competitive performance in terms of convergence behavior, solution quality, and stability. Full article

With the increasing complexity of engineering optimization and intelligent decision-making problems, traditional metaheuristic algorithms often suffer from premature convergence, loss of population diversity, and insufficient adaptability to complex fitness landscapes. To address these issues, this paper proposes a Multi-strategy Symmetry-Aware Improved Golden Sine Algorithm (MIGoldSA). The proposed algorithm introduces a symmetry-guided multi-strategy framework in which multiple complementary search operators are organized in a structurally balanced manner. Specifically, a strategy pool consisting of the original golden sine update rule, three differential evolution mutation strategies, and an elite-based quadratic interpolation local search operator is constructed. An adaptive strategy selection mechanism is further developed to dynamically regulate the selection probabilities of different strategies according to their historical success rates, forming a dynamic probabilistic symmetry that balances global exploration and local exploitation throughout the optimization process. The numerical performance of the resulting method is assessed using the CEC2014, 30-dimensional CEC2017, and 20-dimensional CEC2022 test collections. Comparative and statistical findings confirm that MIGoldSA generally delivers more accurate final solutions, more consistent outcomes across independent trials, and stronger convergence behavior than established algorithms and recently developed competitors. Its applicability is further examined in corporate insolvency forecasting by employing MIGoldSA to determine the hyperparameter configuration of a K-nearest neighbors classifier. Tests conducted on the Wieslaw financial database show that the resulting MIGoldSA-KNN system outperforms the selected reference models in classification accuracy, Matthews correlation coefficient, F1-score, and recall. These findings suggest that the proposed symmetry-inspired architecture offers an effective means of coordinating diversified search and intensive refinement, thereby providing a valuable computational approach for challenging global optimization and financial classification tasks. Full article

Cutterhead driving torque is the primary load indicator of earth-pressure-balance shield machines, yet its dependence on strongly coupled multi-source operating parameters limits the reliability of empirical formulations. This study proposes IALA-edRVFL-FIS-Reg, a fuzzy ensemble deep random vector functional link regression model optimized by an improved artificial lemming algorithm (IALA). The base learner maps continuous operating parameters into fuzzy-state features through a Gaussian-membership Sugeno inference layer, propagates the concatenated raw and fuzzified inputs through stacked randomized hidden layers with direct input links, and obtains layer-wise output weights by regularized closed-form least squares before ensembling, thereby combining fuzzy-state representation with deep random feature mapping without gradient back-propagation. Distinct from the standard ALA, IALA introduces three explicitly defined mechanisms: an error-feedback exploration–exploitation transition factor normalized by the initial-population loss, which replaces the fixed energy factor; an adaptive step size coupling sigmoid error-gating with cosine annealing to preserve jumping capability while refining local search; and a stagnation-counter-triggered directional-disturbance jump for escaping local optima. Using 48,646 valid tunnelling records from 301 rings of Beijing Metro Line 22 and 65 raw and mechanism-based engineered features, the model attains R² = 0.9555, RMSE = 382.52 kN·m, MAE = 302.95 kN·m and MAPE = 9.18%, outperforming eleven benchmarks on a ring-disjoint holdout, previously unseen rings of the same section, IALA yields an R² gain of 0.0104 over ALA. Full article

Modern power systems are increasingly exposed to uncertainties and face rising demands for operational resilience. To address this challenge, this paper investigates the optimal placement of thyristor-controlled series compensation (TCSC) devices within flexible AC transmission systems (FACTS). A comprehensive vulnerability evaluation index is developed by integrating network structure, load impact, and branch disconnection factors, enabling a holistic identification of vulnerable transmission links. Subsequently, a multi-objective TCSC optimization model is formulated to simultaneously minimize the system-wide comprehensive vulnerability index and the total investment cost. To solve this model, an improved multi-objective particle swarm optimization (MOPSO) algorithm is devised, incorporating chaotic initialization and adaptive inertia weight adjustment to enhance both global exploration and local exploitation capabilities. The proposed method is validated using the IEEE 39-bus and IEEE 118-bus test systems. The results demonstrate that the optimized placement significantly reduces system vulnerability, maintains a favorable economic balance and improves the system security margin. Furthermore, uncertainty tests involving load variations, line parameter perturbations, and wind power fluctuations, as well as malicious attacks, confirm the robustness of the proposed placement strategy. This work provides a practical and effective framework for resilience-oriented TCSC planning, contributing to mitigating cascading failure risks and enhancing power system security. Full article

Classification is a fundamental technique in data mining that predicts categorical labels by analyzing input features. However, training Artificial Neural Networks (ANNs) using traditional methods often encounters challenges, such as getting stuck in local minima and slow convergence. To address these issues, this study proposes a novel hybrid model, IPA-ANN, which integrates the Immune Plasma Algorithm (IPA) to optimize the ANN’s connection weights and biases. The IPA, inspired by the immune plasma treatment process, utilizes a unique donor-receiver mechanism to balance exploration and exploitation in the search space. The proposed model was evaluated on nine benchmark datasets from the UCI repository and compared with 18 state-of-the-art metaheuristic algorithms, including Grey Wolf Optimization (GWO), Differential Evolution (DE), and Particle Swarm Optimization (PSO). Experimental results were analyzed using accuracy, F1-score, confusion matrices, and convergence graphs. The findings indicate that IPA-ANN achieves competitive and stable classification performance across different datasets while demonstrating favorable convergence characteristics in several cases. Furthermore, the study investigates the influence of donor–receiver parameters on the optimization process, highlighting the adaptability of the proposed framework. The reliability of these findings was further examined through repeated stratified 5-fold cross-validation and paired Wilcoxon signed-rank tests with Holm–Bonferroni correction on representative datasets, confirming that a subset of the observed performance differences are statistically significant, and through a computational cost analysis showing that IPA-ANN incurs no additional overhead relative to the majority of the compared algorithms. This study contributes to the literature by presenting the first documented application of IPA in ANN training and by providing a modular infrastructure for future metaheuristic-based ANN optimization studies. Full article

The Grey Wolf Optimizer (GWO) is a popular metaheuristic, yet it often suffers from premature convergence and rapid diversity loss in complex, high-dimensional, or highly constrained optimization problems. This paper introduces HAGWO, a novel Hierarchical Adversarial Grey Wolf Optimizer that addresses these limitations through three synergistic enhancements: dynamic hierarchical population stratification, adaptive Levy flight perturbation, and hierarchical adversarial-like position updating. These mechanisms enable adaptive balancing of global exploration and local exploitation while preserving population diversity throughout the search process. Extensive experiments on the CEC 2017 bound-constrained benchmark suite across 30D, 50D, and 100D dimensions demonstrate that HAGWO achieves superior overall performance among eight state-of-the-art algorithms, with statistically significant advantages confirmed by Friedman mean ranks and Wilcoxon signed-rank tests. When adapted to the strongly NP-hard three-dimensional bin packing problem with identical bins (3D-SBSBPP), HAGWO delivers highly competitive results, outperforming the well-established BRKGA and most other metaheuristics while closely approaching the original GWO in solution quality and exhibiting exceptional run-to-run stability. By rigorously evaluating HAGWO across both high-dimensional continuous benchmarks and a practical constrained combinatorial application, this study validates the effectiveness of its hierarchical adversarial-like framework and provides valuable insights into algorithm design and transferability across different problem domains. Full article

The dispatching data network and information management system in smart grids constitute a critical communication infrastructure that requires continuous security monitoring. However, network attacks exhibit multi-scale temporal characteristics ranging from microsecond-level bursts to slow intrusions lasting minutes, making single-scale detection models insufficient. Moreover, the hyperparameter space of deep learning models is large and highly non-convex, rendering traditional manual tuning inefficient. To address these challenges, this paper proposes an intrusion detection method based on the Dynamic Social Gray Wolf Optimizer (DSGWO) and the Multi-Scale Temporal Convolutional Network (MSTCN). The DSGWO maintains population diversity via an underdog alliance and breaks elite monopoly through a rank promotion challenge mechanism, balancing exploration and exploitation to avoid premature convergence. The MSTCN employs multi-scale parallel branches whose key training hyperparameters are optimized by the DSGWO, with residual connections and feature fusion for robust temporal modeling. Experiments on UNSW-NB15 and CIC-IDS-2017 demonstrate that DSGWO-MSTCN achieves F1-scores of 0.9933 and 0.9892, respectively, outperforming GWO, PSO, and NGO-based optimization approaches. Full article

The Anatolian Caracal Optimization Algorithm (ACOA), a swarm-based optimization approach inspired by nature, approximately describes the characteristic hunting strategy of the Anatolian caracal, shaped by its superior auditory–visual perception and extraordinary agility, using mathematical models. The algorithm structures this process into three main stages by integrating and modifying established search mechanisms within a sequential framework: (i) wide-area scanning (exploration), which supports a broad exploration of the solution space through prey-focused movements; (ii) tracking–approach, which uses Brownian (continuous, small steps) and Lévy (occasional long jumps) movements to support the transition from exploration to exploitation; and (iii) predatory leap (exploitation), which represents the Anatolian caracal’s jump with a dimensionless projectile-inspired formulation and supports the refinement of candidate solutions. ACOA has been benchmarked across the CEC 2017, CEC 2019, CEC 2020, and CEC 2022 test suites, including unimodal and multimodal functions with fixed and varying dimensions, as well as real-world engineering design problems, for a total of 131 functions. It has been compared against 11 recent and well-known optimization algorithms. The best mean performance is achieved by ACOA in 111 out of 131 cases. In terms of stability, consistently low standard deviation is maintained by ACOA across the majority of the evaluated functions, indicating a strong reproducibility of the obtained solutions across independent runs. These findings indicate that the proposed three-stage framework provides a competitive exploration–exploitation behavior and can offer effective and stable solutions for a wide range of benchmark and engineering optimization problems. Full article

Partial shading conditions (PSCs) in photovoltaic (PV) systems generate multiple local maximum power points (LMPPs) and a single global maximum power point (GMPP) in the power–voltage (P–V) characteristics, challenging conventional maximum power point tracking (MPPT) methods. This study presents an FPGA-in-the-Loop (FIL) co-simulation of a Systematic-Sequencing Adaptive Particle Swarm Optimization (SS-APSO) algorithm for MPPT under dynamically varying shading conditions. The proposed method combines deterministic particle initialization, adaptive particle reordering, and switching among wide exploration, re-exploration and exploitation modes to enhance global search capability. The controller is implemented on a Xilinx Artix-7 FPGA using fixed-point arithmetic and a finite-state-machine architecture in VHDL and is evaluated through MATLAB/Simulink–FIL co-simulation for two PV configurations: four series-connected modules (4S) and two parallel-connected strings of two series modules (2S2P). The results demonstrate tracking efficiencies generally exceeding 98% under different shading within 0.181 s for both configurations, while in FIL co-simulation, it reaches the GMPP within 0.203. The close agreement between simulation and FIL co-simulation results demonstrates the effectiveness of the proposed SS-APSO-MPPT controller for PV systems. Full article

Optimal sensor placement (OSP) is critical to building a full-lifecycle health monitoring network for high-aspect-ratio unmanned aerial vehicle wings, as it directly affects the deformation sensing of the wing shape. To address this challenge, this paper proposes a novel sensor placement method for wings based on a chaotic strengthened aquila optimizer (CSAO) that integrates chaotic mapping and a nonlinear search strategy. Specifically, the proposed method introduces a uniform initialization strategy based on the piecewise chaotic map and a nonlinear criterion for switching between exploration and exploitation in the basic aquila optimizer (AO). These enhancements increase the diversity of the initial population and raise the probability of global search in later iterations, thereby accelerating convergence and strengthening global optimization capability. First, the performance of the CSAO is compared with that of other popular intelligent algorithms on 10 benchmark functions. The results show that the proposed method exhibits superior convergence speed, higher-quality solutions, stronger global search ability, and better robustness, making it suitable for OSP problems involving tens of thousands of candidate points. Next, the CSAO is applied to sensor placement on a wing-shaped plate. Compared with other OSP methods, the proposed method offers significant advantages in terms of sensor distribution, computational time, and hardware cost. Finally, experimental validation is conducted using a wing test platform equipped with fiber Bragg grating (FBG) strain sensors. The measurement results demonstrate that the reconstructed shape is in excellent agreement with the measured shape. Therefore, the proposed CSAO-based OSP method, combined with the FBG-based structural monitoring system, offers a promising solution for health monitoring of deformable structures in extreme environments. Full article

Lithology prediction is a fundamental component of geological exploration and hydrocarbon reservoir characterization, playing a critical role in improving subsurface structural interpretation and enhancing resource prediction accuracy. However, well log data are typically characterized by high dimensionality, strong nonlinearity, severe class imbalance, and asymmetric geological feature distributions, which significantly restrict the predictive accuracy and generalization capability of conventional machine learning methods. To address these challenges, this study proposes a symmetry-aware lithology classification framework based on a Hybrid Grey Wolf Optimizer–Genetic Algorithm optimized Extreme Gradient Boosting (GGA-XGB) model. The proposed framework establishes a symmetric collaborative optimization mechanism by integrating the global exploration capability of the Grey Wolf Optimizer (GWO) with the local exploitation ability of the Genetic Algorithm (GA), thereby achieving a balanced optimization strategy between exploration and exploitation. Specifically, GWO first performs coarse-grained global hyperparameter optimization of XGBoost to improve search efficiency and optimization stability, while GA subsequently refines the parameter space to further enhance local optimization accuracy. Experimental results on a multi-class well logging dataset demonstrate that the proposed method achieves outstanding classification performance, with precision, recall, and F1-score all reaching 0.9862. Compared with several conventional machine learning methods, the proposed GGA-XGB framework exhibits superior predictive accuracy. The symmetry-aware optimization strategy provides an effective solution for intelligent lithology prediction under complex geological conditions and offers both theoretical insights into symmetry-aware optimization mechanisms and practical value for intelligent geoscience and hydrocarbon exploration. Full article

Background/Objectives: Accurate segmentation of histopathological images while preserving cellular morphology in computer-aided diagnostic systems is critically important for the diagnosis and staging of colon cancer. However, conventional metaheuristic algorithms performing multi-level thresholding on such complex tissues often suffer from premature convergence by becoming trapped in local optima as the search space increases. To address this limitation, this study proposes a new label-independent hybrid optimization algorithm focused on colon adenocarcinoma segmentation. Methods: The proposed algorithm hybridizes the global exploration capability of the Artificial Rabbit Optimization (ARO) algorithm with the local exploitation ability of the Ant Lion Optimization (ALO) algorithm through an optimized fixed transition ratio, thereby enabling efficient localization of cellular density valleys. Results: The principal findings obtained from the LC25000 colon cancer dataset demonstrate that the ARO-ALO algorithm achieves stable performance with high SSIM (0.8043) and FSIM (0.8595) scores while preserving the histopathological hierarchy. Furthermore, the preservation of diagnostic morphology after segmentation is statistically validated by the high Pearson (0.9870) and Spearman (0.9948) correlation coefficients. In addition, supplementary generalization experiments are conducted on the Oral Squamous Cell Carcinoma (OSCC) and pulmonary circulation vessels datasets to verify the tissue-agnostic nature of the algorithm. Conclusions: Consequently, the ARO-ALO algorithm emerges as an efficient alternative for clinical decision support systems. Full article

The semantic interpretation of remote sensing imagery through segmentation has become indispensable for a wide range of applications, including resource exploration, environmental assessment, and land-use analysis. Yet, accurate parsing of such images remains challenging because complex object boundaries and large scale differences often weaken the ability of conventional Convolutional Neural Network (CNN)-based methods to preserve local details. In response, this study constructs a segmentation framework that couples wavelet convolution with the Mamba architecture. To strengthen feature learning in the intermediate stages, an Auxiliary Segmentation Module (ASM) is employed to provide additional supervisory guidance, which supports optimization and encourages the representation of subtle semantic details. Wavelet-transform convolution is also introduced into the downsampling path, enabling spatial cues and frequency-related information to be exploited in a more coordinated manner for finer boundary and texture modeling. Experiments on public remote sensing datasets and mining area imagery further confirm the effectiveness of the method. Compared with several existing segmentation approaches, the proposed model delivers better overall performance in mIoU, F1-score, and recognition accuracy, particularly in scenes where multiple land-cover categories are heavily interlaced. Moreover, these gains are obtained with relatively low model complexity, suggesting good potential for practical deployment in land monitoring and ecological management. Full article

Software-defined satellite networks (SDSNs) enhance resource utilization and flexibility in space-based networks by leveraging a global view and programmability. A highly reliable control plane is essential to sustain network operations. However, the highly dynamic topology and physical failures in Low Earth Orbit (LEO) environments can cause satellite node outages or inter-satellite link disruptions, leading to control plane interruptions and local load imbalances. To address this, we propose a switch migration mechanism for failure recovery and establish a multi-objective migration model that jointly optimizes control link delay, controller load variance, and normalized migration ratio. To accommodate distinct dynamic characteristics such as frequent topology changes, failure-intensive periods, and stable periods, we design two algorithms: a robust migration algorithm, DNSGA-II, which features population diversity maintenance and environmental awareness, and an efficient migration algorithm, IHAOAVOA, which integrates strong global exploration with powerful local exploitation. Simulation results show that IHAOAVOA rapidly converges under large-scale failures, achieving millisecond-level delay recovery and low normalized migration ratio overhead during failure-intensive periods, while DNSGA-II focuses on long-term load balancing and system stability during stable periods, effectively suppressing localized controller overload. By adopting IHAOAVOA during topology fluctuations or high-failure phases to reduce delay, and switching to DNSGA-II during stable phases to optimize load distribution, the overall network robustness can be improved under the evaluated failure scenarios. This work provides effective support for achieving highly reliable control in SDSNs under failure scenarios. Full article

The economic scheduling of grid-connected microgrids requires the coordinated dispatch of renewable energy sources, controllable distributed generators, battery energy storage systems, and power exchange with the utility grid while satisfying various operational constraints. Owing to the time-varying nature of renewable generation and load demand, this problem often exhibits strong nonlinearity, temporal coupling, and complex constraint characteristics. To enhance the optimization capability of the original Coati Optimization Algorithm (COA) for such constrained scheduling tasks, this paper proposes an Improved Coati Optimization Algorithm, termed ICOA. Different from the original COA, which mainly depends on random initialization, single-best individual guidance, and simple local perturbation, the proposed ICOA redesigns the search process through several urban-traffic-inspired mechanisms. First, a road-network stratified initialization strategy is employed to improve the spatial coverage and diversity of the initial population. Second, a traffic-signal-guided exploration strategy adaptively adjusts the search direction by considering population congestion and elite information. Third, a lane-changing local exploitation operator is introduced to refine promising solutions with the aid of neighborhood information. Finally, a traffic-rule-based repair mechanism is incorporated to enhance the feasibility of candidate scheduling solutions under operational constraints. The performance of ICOA is first assessed on the CEC2017 benchmark suite with 10-, 30-, 50-, and 100-dimensional test settings. The results obtained from convergence curves, boxplots, Wilcoxon signed-rank tests, and Friedman mean rank tests demonstrate that ICOA achieves competitive performance in terms of convergence accuracy, robustness, and scalability when compared with 11 advanced algorithms. In addition, ICOA is applied to a 24 h grid-connected microgrid economic scheduling case. The simulation results show that ICOA obtains the lowest mean operating cost of 1393.58, which is 13.10% lower than that of the best competing algorithm in terms of mean cost. These results suggest that ICOA is an effective and reliable optimization method for both benchmark function optimization and constrained microgrid scheduling problems. Full article