Search Results (874)

Evacuated-tube solar collectors charged with hybrid nanofluids can raise thermal output, but their coupled thermal and hydraulic response depends on many interacting variables, making the design an optimisation rather than a prediction problem. This study recasts it as a constrained, mixed-variable, three-objective task that maximises thermal efficiency and the Nusselt number while minimising pumping power over the hybrid pair, base fluid, weight fraction, component-one share and flow rate, and develops a memetic metaheuristic: the Adaptive Memetic Hybrid (AMH). A histogram gradient-boosted surrogate trained on 54,432 reduced-order runs, with held-out coefficients of determination of at least 0.9999, provides a fast screen, while a continuous reduced-order model validated to within 0.02 percent serves as the objective; the surrogate is accurate off-grid for efficiency but not for pumping power or the Nusselt number. Nine optimisers, comprising four baselines, three recent metaheuristics, and two AMH variants, were validated on twelve ZDT, DTLZ, and constrained problems over thirty trials using the hypervolume, generational distances, and spacing, and analysed with Friedman, Nemenyi, and Holm-corrected Wilcoxon tests. AMH attained the best mean Friedman rank of 3.08 (chi-square 65.7, p = 3.6 × 10⁻¹¹), significantly outperforming the recent methods and NSGA-III and remaining competitive with the strongest classical algorithms. On the collector, the reduced-order front recovers the 1512-design brute-force maximum efficiency to within 0.02 percent and improves the trade-off through continuous flow rates. The study is a deterministic, model-based optimisation process: the surrogate serves as a tool for fast screening and diagnostics, while the reconstructed reduced-order model is the objective for the final continuous optimisation. The collector application has a low effective design dimension, being governed mainly by the base fluid and the loop flow rate, so the decisive separation of the algorithms is established on the benchmark suite rather than on the collector. Experimental validation remains a task for future work. Full article

Wireless Sensor Networks (WSNs) are fundamental to the expansion of the Internet of Things (IoT), yet severe node energy constraints remain the primary bottleneck for remote environmental monitoring where power infrastructure is unavailable. Because these devices rely on finite battery capacities, optimizing energy usage is critical for maximizing network longevity and architectural sustainability. Sourcing literature across the Scopus, IEEE Xplore, and Elsevier digital databases, this study executes a systematic review evaluating a final cohort of $n=86$ contemporary energy management frameworks published between 2020 and 2026. The analysis synthesizes advanced multi-tier optimization techniques, specifically focusing on hierarchical clustering methodologies, metaheuristic routing protocols, and advanced scheduling algorithms. Beyond traditional approaches, the technical findings investigate the cross-layer impacts of duty cycle scheduling, transmission power control, and sleep protocols on maintaining rigid network coverage and connectivity. Ultimately, this review identifies significant research gaps regarding topological fault tolerance and localized load imbalances near base stations. The findings highlight how the strategic integration of cohesive, cross-layer hybrid optimization strategies can mitigate active energy dissipation, providing actionable technical recommendations for future IoT-based WSN architectures. Full article

Traditional particle swarm optimization (PSO) easily falls into premature convergence, while differential evolution (DE) is highly sensitive to fixed control parameters. Existing PSO-DE hybrid frameworks suffer from static population sizes and insufficient cross-population information exchange. This paper proposes PSO-DE-ADP, a hybrid optimizer with sinusoidal adaptive parameters, elite-guided mutation, ring neighborhood-weighted PSO and fitness-driven dynamic dual-population allocation. Four complementary mechanisms are integrated: (i) sine-wave perturbation superimposed on linear decay adaptively adjusts PSO inertia weight, acceleration factors and DE scaling/crossover coefficients to balance search stages; (ii) global elite individuals are embedded into DE mutation to reduce blind random search; (iii) ring topology with weighted learning realizes bidirectional information interaction between PSO and DE subpopulations; (iv) the proportion of PSO/DE individuals is dynamically adjusted according to elite ratio to allocate computing resources. Experiments adopt the CEC2017 30-dimensional benchmark with 30 test functions covering unimodal, multimodal, hybrid and composite landscapes. Compared with 8 state-of-the-art metaheuristics, PSO-DE-ADP achieves the lowest Friedman rank (1.08 vs. 2.23–4.90 for PSO variants; 1.53 vs. 2.07–5.00 for non-PSO algorithms). Ablation tests prove each component significantly boosts accuracy; The algorithm only costs 0.172 s average runtime, superior to all competitors. Statistical Wilcoxon and Friedman tests verify its significant superiority. Future work extends this method to multi-objective, constrained and real engineering optimization tasks. Full article

This study addresses a large-scale location–allocation problem in a three-echelon automotive supply chain comprising 382 suppliers, candidate distribution centers, and six assembly plants. The planning task is to redesign the inbound consolidation network while minimizing transportation and distribution center operating costs, enforcing a 480 km supplier-to-center service radius, and achieving at least 90% demand-weighted coverage. We formulate a mixed discrete-continuous model with supplier-to-center assignment, center location, throughput, and flow decisions. A feasibility-oriented hybrid algorithm uses a genetic algorithm as the main search engine, ant colony construction to seed solutions near the feasible region, adaptive mutation and simulated annealing to preserve exploration and refine elite solutions, and an online neural surrogate to avoid a subset of costly exact fitness evaluations. The design differs from a simple collection of metaheuristics: all components share one variable-length encoding, the same feasibility metrics, and periodic exact reevaluation of candidate solutions. Using the competition case data, the redesigned network reduces total cost by 27.0% relative to the six-center baseline, decreases the demand-weighted average supplier-to-center distance from 461.3 km to 53.0 km, lowers the maximum distance from 2807.22 km to 441.78 km, and raises coverage from 45.0% to 100%. Across ten independent runs, the hybrid method obtains a mean cost 10.3% below that of a standard genetic algorithm, with lower run-to-run dispersion. The results show that feasibility-aware initialization, adaptive search, and selective surrogate evaluation can support practical redesign of a strongly constrained, national-scale inbound logistics network. The directly attached reproducibility package provides the MATLAB implementation and the seven supplied input workbooks used by the reported model. The evidence is limited to one deterministic competition instance, a fixed cost schedule, and fixed-topology sensitivity calculations; generalization under demand uncertainty, facility disruption, and alternative road conditions remains to be tested. Full article

High-dimensional environmental covariates are increasingly available for digital soil mapping (DSM), but their effective use depends on both the feature-selection strategy and the predictive model architecture. However, systematic evidence remains limited regarding how different metaheuristic feature-selection methods interact with standalone and hybrid learning models across multiple soil and groundwater prediction tasks. This study systematically evaluated the interactions between 10 metaheuristic feature-selection algorithms and 13 predictive models, including random forest (RF), convolutional neural network (CNN), recurrent architectures, CNN–recurrent neural network (RNN) hybrids, squeeze-and-excitation (SE)-enhanced hybrids, and iTransformer-based hybrids, across four prediction tasks involving soil organic carbon (SOC), soil–water extract electrical conductivity (ECe), apparent electrical conductivity (ECa), and groundwater level (GWL) in Xinjiang, China. A total of 149 candidate environmental covariates were considered for ECe, SOC, and ECa, whereas 122 candidate covariates were considered for GWL. The results showed that no single feature-selection method consistently performed best across all four targets; instead, predictive performance depended on the interaction among the feature-selection strategy, predictive architecture, and target variable. CNN–RNN hybrid architectures generally achieved higher predictive performance than standalone models, although their benefits varied among prediction targets. The best-performing combinations yielded coefficient of determination (R²) values of 0.9826, 0.6981, 0.8429, and 0.8085 for GWL, SOC, ECe, and ECa, respectively. These findings indicate that target-specific compatibility, rather than aggressive dimensionality reduction or a universally superior algorithm, is a key determinant of predictive performance in high-dimensional DSM. By demonstrating that feature-selection effectiveness is jointly influenced by model architecture and target characteristics, this study provides a methodological reference for developing target-specific digital soil mapping models in arid regions. 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

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

Stochastic metaheuristic feature selection (FS) may generate unstable feature subsets across repeated runs, undermining reproducibility in Internet of Things (IoT) intrusion detection systems (IDSs). This paper presents Head–Tail Population Initialization (HTPI), a feature-importance-guided initialization approach for enhancing the stability of Adaptive Hybrid Genetic Algorithm–Simulated Annealing (AHGA-SA)-based FS. HTPI uses feature-importance scores to split candidate features into Head and Tail groups and initializes candidate subsets by prioritizing Head features and sampling Tail features with importance-based weights. HTPI is integrated into AHGA-SA as an incremental extension, termed HTPI-AHGA-SA, and modifies only the initialization and reinitialization steps. Experiments on eight IoT-oriented IDS datasets using 50 runs per configuration, with seeds paired across methods, showed significantly higher Nogueira stability under HTPI-AHGA-SA on all datasets after Holm correction, with non-overlapping 95% leave-one-run-out jackknife confidence intervals in every case. These results characterize algorithmic cross-run stability under a fixed data partition. All absolute differences in dataset-level mean F1 Macro remained below $0.003$; formal equivalence at this margin was supported for six datasets, while dataset-specific security-metric trade-offs remained. On three representative datasets, post hoc explainable artificial intelligence (XAI) analyses indicated generally higher permutation importance (PI)-based cross-run consistency and measurable predictive utility in the selected Head and Tail portions under retraining. Full article

The need for intelligent optimization of supply chain operations has become imperative for organizations seeking to adapt quickly to an increasingly complex logistics environment. The complexity of predicting delivery delays stems from the large number of interdependent operational and external factors that may affect delivery performance. In this study, we propose a hybrid machine learning framework that employs both the Random Forest Classifier (RF) and the Particle Swarm Optimization (PSO) to provide more accurate predictions of delivery delays through automated hyperparameter tuning. We evaluated our proposed PSO–RF model using a publicly available benchmark dataset of shipment records, along with their respective operational characteristics and contextual risk indicator values. In the comparative experiments PSO–RF produced the best single configuration among the metaheuristic-tuned models, reaching an accuracy of 94.6% and a Matthews correlation coefficient of 0.739 at a swarm size of 10. This advantage is specific to the small-swarm setting: at swarm sizes of 20 and 40 the four optimizers examined the (PSO, SCA, GWO, DE) cluster within a narrow MCC band of 0.71–0.74, and SCA–RF is the most consistent, so no optimizer is uniformly superior across swarm sizes. Against the conventionally configured baseline classifiers the improvement is far larger and holds in every configuration, which indicates that the principal gain arises from optimizing the Random Forest hyperparameters rather than from the choice of search algorithm. Additionally, to provide actionable supply chain intelligence, we employed SHapley Additive exPlanations (SHAP) to conduct model interpretability analyses. The SHAP analysis identified that the most influential features within the model’s delay classification decisions include shipping costs, order weights, geopolitical risks, scheduled lead-time days, transportation mode, and base lead-time days. Full article

Algorithmic team formation is a well-studied problem in workforce analytics. Most prior research has focused on team-oriented outcomes, such as cost and performance, while individual-oriented outcomes have been limited to operational constraints. We formally define and solve a new variant, the growth-aware team formation problem (GATFP), which forms teams to maximize individual skill growth. Our formulation lets organizations define their own notion of growth and their own policy for allocating growth opportunities, and integrates standard team formation constraints such as covering a portfolio of projects, capping projects per individual, and meeting each project’s skill, level, and coverage requirements. We prove the problem NP-hard and develop a family of metaheuristics: randomized and greedy construction, a ruin-and-recreate large neighborhood search (RR), and a hybrid evolutionary algorithm (HE). Where the objective is linear, a mathematical program provides an exact baseline. We evaluate them under an equal runtime budget on a synthetic benchmark and a real-world-derived dataset. Both metaheuristics surpass the construction baselines and are closely matched, and which one leads depends on the growth model: RR is attractive for its solution quality and implementation simplicity, whereas HE is the stronger choice when an individual’s growth depends on their team’s composition. Full article

Maintaining frequency stability in islanded microgrids (MGs) has become increasingly challenging due to the growing penetration of renewable energy sources, particularly photovoltaic systems, wind turbine generators (WTGs), and plug-in hybrid electric vehicles (PHEVs). The intermittent nature of renewable generation and continuous load variations introduces significant power imbalances, resulting in frequency deviations and degraded system stability. Although the classical Ziegler–Nichols (ZN) tuning method is attractive because of its simplicity and ease of implementation, it is generally limited to conventional proportional–integral–derivative (PID) controllers and is often inadequate for renewable-dominated MGs. To overcome these limitations, this paper proposes a modified ZN-based tuning strategy for a novel multistage PID (MPID) controller. Unlike the conventional ZN method, the proposed approach extends its applicability to the MPID structure by introducing an additional proportional gain (KPP), enabling the tuning of five controller parameters while preserving low computational complexity and practical implementation. The proposed controller is implemented and validated using a detailed MATLAB/Simulink model of an isolated MG comprising PV systems, WTG, diesel generators, and PHEVs. Its performance is comprehensively evaluated under multi-step load disturbances, renewable power fluctuations, combined disturbances, and different PHEV charging/discharging modes and battery state-of-charge levels. Furthermore, the proposed controller is benchmarked against conventional ZN-PID, ZN-FOPID, and both PID- and MPID-based controllers tuned using Particle Swarm Optimization, Cuckoo Search Algorithm, Moth–Flame Optimization, and Grasshopper Optimization Algorithm. Simulation results demonstrate that the proposed ZN-MPID controller achieves the best overall dynamic performance, with a settling time of 4.109 s, zero overshoot, a maximum frequency undershoot of 1.801 × 10⁻⁴ Hz, and the lowest error indices (ISE = 3.073 × 10⁻⁶, ITSE = 0.697 × 10⁻⁶, and ITAE = 3.40 × 10⁻⁴). Compared with the investigated metaheuristic-based PID controllers, the proposed controller reduces the settling time by up to 86.1% and the error indices by up to 95.5%. It also consistently outperforms all investigated MPID tuning methods, confirming the effectiveness of the proposed modified ZN tuning strategy. Overall, the proposed methodology provides an efficient, low-complexity, and practical solution for frequency regulation in renewable-dominated isolated MGs. Full article

Wireless Sensor Network (WSN) underpins numerous applications, including environmental surveillance, industrial control, and smart cities, where reliable connectivity is vital for continuous sensing and data transmission. On the other hand, a small number of failed or compromised Critical Nodes (CNs) can badly fragment the network topology, interfere with communication, and impair performance. Consequently, devising effective solutions to the CN Detection Problem (CNDP) while taking connectivity, energy, and reliability into account remains a serious research endeavor. To tackle this challenge, this work proposes a Genetic Algorithm-assisted Damped Yo-Yo Grey Wolf Optimization framework with a Revitalized Boltzmann Distribution connectivity model (GA-DY-RBD-GWO). The CNDP is formulated as a node-elimination optimization problem that identifies the top-(k) CNs, where each search agent represents a candidate subset of k nodes. To realistically characterize network connectivity, a Revitalized Boltzmann Distribution (RBD)-based pairwise connectivity model is developed by jointly considering hop distance, residual path energy, and distance-based link reliability. Based on the resulting connectivity matrix, Total Pairwise Connectivity (TPC) is computed, and node criticality is quantified by the reduction in TPC after removing a candidate node set. To effectively explore the combinatorial search space, the Grey Wolf Optimization is augmented with a damped Yo-Yo control mechanism that adaptively balances exploration and exploitation during the optimization process. Furthermore, Genetic Algorithm-inspired crossover and mutation operators improve population diversity and avoid premature convergence, while elitist retention keeps the best-so-far candidate solution. By integrating realistic RBD-based connectivity modeling with an adaptive hybrid metaheuristic, GA-DY-RBD-GWO accurately identifies CNs whose deletion induces maximal TPC degradation. Extensive experiments under diverse network topologies, deployment scenarios, and spatial distributions demonstrate that the GA-DY-RBD-GWO exhibits superior performance over representative baselines, revealing that it is an efficient topology-aware solution for CNDP to improve the reliability of WSNs. Full article

Unmanned Aerial Vehicles (UAVs) are used in numerous practical applications in industry, science, and ecology; however, the large-scale use of UAVs is hampered by the limited onboard energy capacity. Increasing the energy efficiency of UAVs has thus become one of the most important tasks in UAV research. This review examines approaches to energy optimization of UAVs using algorithms and artificial intelligence (AI). It evaluates and compares algorithms and methods used to optimize energy efficiency of trajectory planning, adaptive speed control, battery management in mission planning, and navigation that accounts for environmental characteristics. It differs from those focusing on hardware solutions by highlighting optimization problems where energy usage is considered the key target for optimization and not a limiting constraint. The analyzed methods have been categorized into five groups, including classical optimization, metaheuristics, machine learning (ML), reinforcement learning (RL), and hybrid approaches. Key approaches such as RL, Model Predictive Control, evolutionary algorithms, and data-driven energy modeling have been outlined and compared with regard to energy-model accuracy, type of validation, scalability, and deployment readiness. Additionally, it emphasizes practical aspects such as the accuracy of energy modeling, real-time capabilities, scalability to multiple UAVs, and robustness to environmental uncertainty. Finally, this review provides directions for future research that will help develop sustainable, intelligent, and energy-efficient UAVs. Full article

Population-based bio-inspired metaheuristics are the dominant tools for tuning hybrid decomposition–deep-learning forecasters, yet their relative behavior on a common problem is rarely assessed with a leakage-free, physically meaningful protocol. We benchmark eight metaheuristics on the joint nine-dimensional hyperparameter optimization of a discrete-wavelet-transform bidirectional-LSTM (DWT–BiLSTM) architecture for short-term wind speed forecasting, using 409,152 hourly observations from eight meteorological stations. The set comprises six nature-inspired methods (Artificial Bee Colony, ABC; genetic algorithm, GA; Particle Swarm Optimization, PSO; Grey Wolf Optimizer, GWO; Hippopotamus Optimization, HO; and the Raindrop Optimizer) together with two recent metaphor-free or social variants (the Farthest-better Nearest-worse Optimizer, FNO; and the Tuckman Optimization Algorithm, TOA). A multi-stage protocol covers 30 independent runs per algorithm, a joint-versus-sequential comparison, a genuine rolling-origin out-of-sample evaluation, and component ablation. Friedman testing reveals significant differences (χ² = 49.76; p < 10⁻⁸), with the Grey Wolf Optimizer attaining the best mean rank (2.27) and Pareto-dominant run-time; ablation shows the DWT front-end is essential (Cohen’s d = 13.09) and bidirectionality negligible at the one-hour horizon (p = 0.674). Critically, evaluating forecasts in reconstructed physical units reveals that the per-component advantage does not persist: at the one-hour horizon the reconstructed forecast does not exceed a naive persistence baseline (skill ≈ −0.5 in m/s versus +0.44 in normalized component space), a discrepancy independent of decomposition leakage that we report transparently. This work thus contributes a rigorous, leakage-controlled bio-inspired benchmark and a cautionary evaluation methodology. Full article

Designing time-optimal trajectories for solar sail spacecraft is highly difficult due to the strong nonlinearity of the solar radiation pressure model, the attitude–orbit coupling, and the prevalence of local minima, which often lead to the failure of gradient-based solvers lacking adequate initial guesses. To address this issue, the present study proposes a two-stage hybrid optimization framework: a coarse-search stage uses B-spline-parameterized metaheuristics to identify a dynamically feasible trajectory, which then serves as a physics-informed warm start for direct-collocation-based gradient refinement, strictly satisfying the full nonlinear dynamics and terminal constraints. The methodology is validated through three increasingly difficult space missions: a rendezvous between Earth and Mars (Case A), a near-Earth asteroid rendezvous considering non-ideal optical reflection (Case B), and a Solar Polar Orbiter mission necessitating an 82.75° inclination adjustment with a thermal safety constraint (Case C). Statistical evaluations reveal that no single metaheuristic dominates universally; each algorithm’s suitability is contingent on the problem’s constraint structure. The hybrid framework further shows a consistent advantage over stand-alone direct collocation: by redirecting the gradient solver toward favorable convergence basins, it locates transfer solutions that remain inaccessible from a cold start. Full article