Search Results (247)

Breast cancer is a highly prevalent malignancy among women globally and arises from the uncontrolled proliferation of abnormal cells in breast tissue. Timely and precise diagnosis is critical for effective treatment and enhanced survival. Histopathological image analysis is considered the gold standard; nevertheless, manual assessment is labor-intensive and prone to variability. Existing deep learning and transformer-based approaches demonstrate strong effectiveness; however, they incur significant computational complexity and limited efficiency in capturing multi-scale features. To address these challenges, this research presents a framework that integrates ConvMixer, multi-scale patch learning, and an Adaptive Combined Runge–Kutta–Harris Hawks Optimization (AC-RUNHHO) algorithm. The model effectively captures both fine-grained cellular patterns and global tissue structures, while adaptive optimization improves convergence and hyperparameter tuning. The framework is evaluated on a breast cancer histology dataset comprising 4000 histopathological images across four classes. Experimental results demonstrate robust performance under the evaluated experimental conditions, achieving 98.63% accuracy, 98.63% precision, 98.62% recall, and 98.62% F1-score. Ablation and cross-validation analyses further confirm the generalization capability of the model. Overall, the developed framework demonstrates promising performance in computer-aided breast histopathological image classification, achieving high predictive accuracy and providing interpretable visual explanations. Full article

Multi-objective optimization problems (MOPs) are common in practical scenarios where decision-makers need to accomplish several competing goals. Single-objective optimization techniques do not guarantee applicability in these scenarios. As such, there has been a need for the development of metaheuristics capable of generating multiple trade-off solutions. Harris Hawks Optimization (HHO) has been shown to possess strong exploration and exploitation capabilities for the solution of optimization problems, owing to the collaborative hunting tactics of Harris’s hawks. Therefore, the Multi-objective Harris Hawks Optimization (MHHO) algorithm was suggested to generalize HHO to handle MOPs. By combining the mechanisms of Pareto dominance, diversity preservation, elitism, adaptiveness, and others, MHHO approaches the Pareto-optimal front and provides decision-makers with several high-quality nondominated solutions. This study comprehensively examines MHHO, elaborating on its theoretical background, algorithmic variants, and fields of application. MHHO has been implemented in different disciplines. Using the Scopus database to conduct a bibliometric study, the publication growth, research development, and the application of MHHO in various fields of study were analyzed. By classifying the extant contributions into original, modified, and hybrid versions, the study provides a detailed outline of the algorithm’s progression. Applications spanning engineering, cloud computing, scheduling, networking, bioinformatics, and energy systems are analyzed, illustrating the broad adaptability of MHHO. A constructive critique has been conducted to evaluate some limitations including premature convergence, scalability issues, and difficulty in addressing disconnected Pareto regions. This review shows the versatility and potential of MHHO in tackling different optimization problems. In addition, further research is needed on the development of more sophisticated hybrid methods, tailored improvements, and more refined techniques for the preservation of diversity. Full article

Power electronic DC–DC conversion stages play a pivotal role in photovoltaic (PV) energy conversion. Here, maximum power point tracking (MPPT) is necessary to regulate the operating point of the converter with high bandwidth and robustness in the presence of irradiance and temperature disturbances. This paper proposes an MPPT scheme based on an Intelligent Wave Algorithm (IWA) for a PV source connected to a flyback DC–DC converter. The proposed IWA is formulated as a population-based metaheuristic that updates the converter’s duty cycle to maximize PV power while reducing the oscillations commonly observed in classical methods. A unified MATLAB/Simulink test bench has been developed in which multiple MPPT algorithms—Perturb and Observe (P&O), Incremental Conductance (InC), Particle Swarm Optimization (PSO), Harris Hawks Optimization (HHO) and the proposed IWA—are implemented in parallel flyback subsystems that share the same PV module and converter parameters. The simulation results show that the IWA method achieved consistent convergence to the maximum power point more rapidly than both classical and advanced meta-heuristic methods, obtaining 12.5% better response time and 8.9% better steady-state output power than the method closest to it. Overall, the findings suggest that combining a flyback converter with IWA-based maximum power point tracking (MPPT) improves the efficiency and stability of energy harvesting, making this approach suitable for low- to medium-power photovoltaic (PV) applications within modern power electronics conversion systems. Full article

Air-jet looms are widely used for weaving lightweight fabrics due to their outstanding high performance. To enhance the overall structural strength of air-jet looms and reduce operational vibration, dynamic analysis and structural parameter optimization of the loom frame system were carried in this study. First, after the structural designing and finite element modeling, the modal tests of the loom frame system were conducted. The modal results showed the high consistency between the simulation and experiment, confirming the accuracy of the dynamic model. Then, the dynamic characteristics and maximum stress data of the frame were obtained and analyzed through vibration tests and simulation calculations. The results indicated that the maximum deformation occurred at the middle of the beam while the maximum stress occurred at the connection between the lower beam and the wall panel. Moreover, the loom frame parameter optimization model was constructed based on the BA-HHO-SVR (Balanced Adaptive—Harris Hawks Optimization—Support Vector Regression) algorithm, demonstrating excellent learning and predictive capabilities. Eventually, the optimal combination of the frame structural parameters was obtained through above optimization algorithm. Furthermore, the effectiveness and reliability of the optimal combination were verified by finite element calculations. The vibration analysis method and optimization strategy proposed in this study provide valuable guidance for subsequent structural design and optimization of the high-end textile equipment. Full article

With the rapid development of low-altitude UAVs, accurate wind detection is crucial for ensuring flight safety and enabling broader applications. To address this need, this paper introduces a highly integrated CDWL system specifically designed for compact UAV platforms. The system incorporates a self-compensating Harris Hawks Optimization (SC-HHO) retrieval algorithm, which is tailored to the high-dynamic flight environment and stringent payload constraints of UAVs. This algorithm enables real-time wind retrieval with low dependence on external reference data while effectively compensating for platform motion. The performance of the proposed system was validated through the comparative experiment and the UAV-borne experiment. In the comparative experiment, the CDWL showed correlation coefficients above 0.976 in horizontal wind speed and 0.987 in horizontal wind direction relative to a benchmark airborne CDWL system, with corresponding root-mean-square errors better than 0.395 m/s and 4.135°, respectively. During the UAV-borne experiment, the CDWL retrieved platform velocity using the self-compensating mechanism, achieving a standard deviation of 0.080 m/s relative to global navigation satellite system (GNSS) measurements, and successfully acquired wind field information. These results confirm that the developed system provides a viable and practical technical solution for UAV-based remote wind sensing. Full article

In incentive-based mobile edge computing (MEC) systems, task execution depends on volunteer devices which are often limited in battery capacity. Continuous computation on such devices directly increases energy consumption and may reduce their operational lifetime. These issues become more critical in delay-sensitive and deadline-driven applications, where even small delays can affect system reliability and usefulness of the results. Therefore, efficient and energy-aware task scheduling becomes an important requirement. In this paper, we propose an enhanced whale optimization algorithm (WOA)-based scheduling framework for heterogeneous MEC environments. The proposed method considers multiple objectives including energy consumption, deadline satisfaction, and economic reward. It integrates energy awareness, a deadline-sensitive fitness formulation, constraint-repair mechanisms, and local search refinement to ensure feasible and efficient task allocation. Simulation experiments are conducted using real-world Bitbrains workload traces with heterogeneous device configurations. The results show that the proposed method reduces energy consumption by about 15–20% compared to genetic algorithm (GA), Particle Swarm Optimization (PSO), Harris Hawks Optimization (HHO), and DTOME variants. It also reduces deadline violations by approximately 20–25% and improves net income by around 10–18%. In addition, the proposed framework is able to maintain a higher number of active devices in later stages of scheduling. To ensure reliability, repeated experiments are performed, and results are reported with 95% confidence intervals. The ablation analysis further shows that energy awareness and the repair mechanism play a major role in achieving improved performance. Full article

Automated Guided Vehicles (AGVs) play a crucial role in intelligent warehousing; however, effective path planning remains challenging because of obstacles, safety constraints, and the risk of suboptimal routes. This study proposes an improved Harris Hawks Optimization algorithm for AGV path planning, introducing strategies to enhance initial solution quality, balance global and local search, and avoid local optima. The proposed algorithm generates shorter, smoother, and safer paths, as demonstrated through benchmark tests, multi-scale grid-map simulations, and real-world AGV experiments. In terms of path length and computational efficiency, the enhanced algorithm significantly outperforms the original HHO, reducing average path length by 10.81% and average travel time by 11.94%. These results demonstrate that the proposed method provides a practical and reliable solution for autonomous warehouse navigation and significantly improves AGV path-planning performance. Full article

Late deliveries represent one of the most persistent operational disruptions in global supply chains, eroding service reliability, triggering contractual penalties, and undermining the resilience of logistics networks. As supply chains become increasingly digitalized, the integration of advanced predictive analytics into operational decision-making offers a pathway toward proactive rather than reactive disruption management. This study develops and evaluates a digital analytics framework in which eXtreme Gradient Boosting (XGB), a high-performance ensemble learning algorithm, is optimized by three recent population-based metaheuristic algorithms: the weighted mean of vectors algorithm (INFO), Harris Hawks Optimization (HHO), and the Red-Billed Blue Magpie Optimizer (RBMO). Four critical XGB hyperparameters, number of estimators, maximum tree depth, learning rate, and complexity penalty, are tuned on a supply chain dataset. A population-size sensitivity analysis at two swarm configurations reveals that all three optimizers converge to functionally equivalent solutions at sufficient population diversity, providing practical guidance for computational resource allocation. The best-performing configuration, HHO-XGB, achieves a test accuracy of 97.47% and a Matthews correlation coefficient of 0.949, substantially outperforming the baseline XGB and other benchmark classifiers. To ensure transparency and support data-driven decision-making, SHapley Additive exPlanations (SHAP) analysis is applied to the optimized model, revealing that shipping mode, scheduled shipment days, shipping date, order day, order status, and order month are the dominant predictive features, confirming that late-delivery risk is primarily driven by shipment configuration and temporal patterns. The proposed framework demonstrates that integrating metaheuristic intelligence with machine learning delivers better predictive performance. Interpretability is essential to trustworthy, resilient supply chain decision-support systems. Full article

In practical power system operation scenarios, extreme natural weather conditions and fluctuations at both the supply and demand sides pose significant challenges to the stable operation and the formulation of operational decision-making for power systems. Particularly in extreme scenarios involving faults, it may lead to power supply–demand imbalances and instability in the power system. To address this issue, this paper proposes a decision-making approach for the secure and stable operation of power systems using a multi-scenario-based optimization model. Initially, a joint scenario set is generated using historical operational data to accurately depict multiple complex scenarios. Building on this, a multi-scenario-based optimization model is constructed, with responses facilitated by flexible adjustment resources within the system. Considering the non-convex and nonlinear characteristics of the model, an improved Harris Hawks Optimization (HHO) algorithm is employed to search for the global optimal solution. Finally, a modified IEEE-33 bus test system is utilized to demonstrate the feasibility and effectiveness of the proposed method. Full article

Multi-UAV path planning in dynamic and complex environments is a challenging constrained optimization problem because it must simultaneously consider path efficiency, obstacle avoidance, altitude feasibility, flight smoothness, and inter-UAV path diversity. Existing methods often struggle to maintain search diversity, balance exploration and exploitation, and avoid premature convergence in high-dimensional search spaces. To address this issue, this paper proposes a Q-learning-guided Harris Hawk Optimization-Genetic Algorithm (QHHO_GA), which integrates Genetic Algorithm (GA), Harris Hawk Optimization (HHO), Q-learning, prioritized experience replay, entropy-based state partitioning, and a Rapidly exploring Random Tree (RRT)-based stagnation adjustment mechanism. In the proposed framework, GA enhances population quality and diversity, HHO performs the core search, Q-learning adaptively guides HHO behaviors, and stagnation monitoring with RRT-based stagnation adjustment improves the ability to escape locally trapped regions. Experimental results on the CEC2017 benchmark suite and a multi-UAV path planning task demonstrate the effectiveness of the proposed method. On the CEC2017 benchmark, QHHO_GA ranks among the top two on 18 out of 30 test functions and achieves the best overall ranking among the compared algorithms. In the UAV path planning experiments, it achieves an average ranking of 3.44 and also achieves the best overall rank among all compared methods. These results indicate that QHHO_GA is a robust and competitive method for high-dimensional constrained optimization, and is particularly effective for complex multi-UAV path planning tasks. Full article

Influenced by factors such as residents’ living habits, commuting patterns, and commercial activity cycles, the generation of domestic waste exhibits a distinct double-peak distribution. To meet the high demand during peak periods, collection companies typically deploy excess transportation capacity, which leads to severe resource idleness during off-peak periods, imposing significant economic and environmental burdens. To address this issue, this study develops a dynamic smart waste collection routing model aimed at minimizing the coordinated collection cost between self-owned and outsourced multi-compartment vehicles, and designs a two-phase algorithm to solve it. In the pre-optimization phase, an improved Harris Hawks Optimization algorithm integrated with multiple heuristic algorithms is employed to generate initial collection routes. In the re-optimization phase, a hybrid strategy combining periodic and continuous re-optimization is used to dynamically update collection routes. Finally, the effectiveness of the proposed model and algorithm is validated through case studies. Furthermore, a systematic sensitivity analysis is conducted to investigate the impact of key parameters, yielding practical insights for waste collection management. Full article

The Flexible Job Shop Scheduling Problem (FJSP) is central to smart manufacturing, yet standard algorithms often prioritize productivity (makespan) at the expense of cost and reliability. This paper introduces t-MOHHO, a collaborative optimization framework designed to equilibrate machine load, processing costs, and delivery timeliness alongside throughput. By incorporating an adaptive Student’s t-distribution mutation operator and a non-linear energy escape mechanism, t-MOHHO effectively navigates high-dimensional search spaces. Extensive validation on 10 MK benchmark instances reveals that t-MOHHO demonstrates significant advantages over classic HHO, MOPSO, and MOEA/D across most metrics. Notably, in comparison to the state-of-the-art NSGA-III, t-MOHHO executes a clear trade-off: it trades marginal makespan efficiency for substantial reductions in cost and tardiness. Specifically, on the large-scale MK10 instance, t-MOHHO reduces total tardiness by 56.2% and lowers processing costs by 3.4% compared to NSGA-III. These results demonstrate that t-MOHHO can strategically sacrifice maximum speed to secure superior punctuality and cost-efficiency, making it a robust decision-support tool for Just-in-Time (JIT) production environments. Full article

To address the challenges of low-carbon economic optimization and collaborative management for multiple Virtual Power Plants (VPPs), this paper proposes a low-carbon economic optimization and collaborative management method based on a Stackelberg game framework. Firstly, a Stackelberg game model is constructed with the Distribution System Operator (DSO) as the leader and multiple VPPs as followers. The leader (DSO) guides the followers’ behavior through dynamic pricing strategies to maximize its own utility. Meanwhile, the followers (VPPs) develop energy management strategies to minimize their individual costs, taking into account factors such as energy transaction costs, fuel costs, carbon trading costs, operation and maintenance (O&M) costs, compensation costs, and renewable energy generation revenues. Furthermore, the strategy spaces of all participants are defined, and an optimization model is established subjected to constraints including energy balance, energy storage operation, power conversion, and flexible load response. The CPLEX solver and Nonlinear-based Chaotic Harris Hawks Optimization (NCHHO) algorithm are employed to solve the proposed game model. Simulation results demonstrate that the proposed method effectively facilitates collaboration between the DSO and multiple VPPs. While ensuring the safe operation of the system, it balances the profit between the DSO and VPPs, and incentivizes renewable energy consumption and indirect carbon reduction, thereby validating the effectiveness and superiority of the method and providing reliable technical support for the low-carbon collaborative operation of multiple VPPs. Full article

Particle swarm optimization (PSO) improves classification performance and reduces computational complexity in feature selection. However, it frequently experiences from premature convergence and insufficient exploration. To address these constraints, this paper suggests an adaptive binary PSO (ABPSO) algorithm specifically designed for feature selection. First, an adaptive transfer function and two adaptive learning coefficients are introduced to achieve a better balance between exploration and exploitation during the search process. Second, a hybrid learning mechanism that integrates personal best, global best, and elite solutions is utilized to enhance population diversity. Finally, a simulated annealing (SA)–based local search strategy is employed to further refine candidate solutions and improve convergence behavior. Experimental results demonstrate that ABPSO outperforms binary PSO (BPSO), harris hawks optimization (HHO), whale optimization algorithm (WOA), and ant colony optimization (ACO) in classification accuracy. In particular, ABPSO achieves the lowest classification error rates on the Dermatology (0.0106), Ionosphere (0.0705), Lung (0.1521), Sonar (0.0996), Spambase (0.0758), Statlog (0.1446), and Wine (0.0280) datasets. Full article

This paper proposes a Black Kite Algorithm (BKA)-based hyperparameter optimization method for Artificial Neural Network (ANN) training, mitigating local minimum issues associated with conventional training techniques. The resulting BKA-ANN model is then employed to estimate PID controller parameters for DC motor speed regulation. A large-scale dataset of 100,000 samples was generated via MATLAB simulation, with reference speed and load torque stochastically varied, and optimal PID parameters determined by minimizing the ITAE criterion for each operating condition. The optimized controller was evaluated under various operating conditions including transient response, frequency domain analysis (phase margin and bandwidth), parametric robustness, and load disturbance suppression, along with control effort and energy consumption assessments. The proposed BKA-ANN approach was benchmarked against nine algorithms: hybrid atom search optimization-simulated annealing (hASO-SA), harris hawks optimization (HHO), Henry gas solubility optimization with opposition-based learning (OBL/HGSO), atom search optimization (ASO), henry gas solubility op-timization (HGSO), stochastic fractal search(SFS), grey wolf optimization (GWO), sine–cosine algorithm (SCA), and Standard ANN. Simulation results indicate that BKA-ANN achieves stable performance across all tested scenarios, with minimal oscillation and competitive settling time compared to the evaluated algorithms. Full article