Search Results (185)

The increasing demand for sustainable and intelligent energy solutions in electric vehicles (EVs) has led to a significant interest in the development of advanced hybrid energy storage systems (HESS) and efficient wireless charging architectures. In this work, a dynamic sliding mode control (DSMC) technique is optimized through a swarming heuristics framework for a battery-ultracapacitor HESS integrated with a wireless power transfer (WPT) system. Leveraging an LCC-S topology, the WPT system enables high-efficiency, contactless energy transfer to the storage modules under varying load and alignment conditions. To address the nonlinearities and parameter uncertainties inherent in such systems, a robust DSMC approach is formulated to ensure smooth system tracking and disturbance rejection. The control design is further refined using a bio-inspired moth–flame optimization algorithm hybridized with gravitational search and fractional-order PSO (MFOGSAPSO)—enhanced with adaptive entropy regulation and fractal-based memory—to dynamically tune the sliding-surface coefficients and switching gains. The proposed methodology is validated through comprehensive simulations in MATLAB/Simulink and a controller hardware-in-the-loop (C-HIL) setup on TI F28379D LaunchPads. Among the three MFO variants, MFOGSAPSO-A achieves the fastest objective function convergence, stabilizing near 685 within 10 iterations and substantially outperforming the optimized PID (715) and Optimized SMC (708). The proposed DSMC attains an overall RMSE of $0.1081$, reducing the tracking error by $60.69\%$ relative to PID and $14.84\%$ relative to SMC, while shortening the settling time to $0.102$ ms against PID ($84.84\%$) and SMC ($23.88\%$) improvements. The C-HIL results closely match the offline simulation waveforms without retuning, confirming superior energy management, improved power sharing between the battery and ultracapacitor, and enhanced overall efficiency of the wireless charging process under realistic embedded execution. Full article

On-Orbit Servicing (OOS) represents a viable strategy toward a sustainable and extended exploitation of the Low-Earth-Orbit (LEO) environment. The design of OOS missions requires optimizing both the scheduling of visited objects and the transfer trajectory between each pair of orbits, resulting in the great complexity of the global mission planning problem. This research considers a servicing spacecraft equipped with a high-thrust propulsion system, required to perform multiple orbit transfers to visit several Resident Space Objects (RSOs) in a given time frame with minimum fuel consumption. The proposed method leverages a two-stage approach: (i) first, the optimal transfers are computed for all pairs of orbits and discretized dates, and the associated overall velocity changes are stored in a cost matrix; (ii) then, the problem of visiting all RSOs is cast as a search problem, and the solution space is explored through an ${\mathrm{A}}^{\ast }$ algorithm. The transfer strategy exploits intermediate drift orbits to increase the differential precession due to the $J_2$ harmonic of the Earth’s gravitational potential. Moreover, the ${\mathrm{A}}^{\ast }$ procedure leverages a heuristic function based on a modified version of the Held–Karp algorithm, which is proven to be admissible and consistent, meaning that the optimal solution is always reached. The proposed strategy is integrated within a flexible architecture, where operational constraints on phasing and servicing activities can be enforced as well. Finally, the methodology at hand is successfully applied to a case study from the literature involving three successive missions, in charge of visiting 5 RSOs each. Different discretization grids are considered, and the results are compared in terms of overall velocity change and computational time. Full article

To address the limitations of the standard Informed-RRT* algorithm in manipulator path planning, including low initial path search efficiency, susceptibility to local optima in complex obstacle environments, and high path redundancy, this paper proposes an improved Informed-RRT* algorithm tailored for manipulator applications. First, we construct a phased adaptive sampling framework that separates the initial path search and path optimization stages. A target region constraint strategy is introduced, and the sampling confidence probability is dynamically adjusted based on the current search phase and real-time path quality. This design significantly enhances the efficiency of feasible path discovery while effectively preventing premature convergence to local optima. Second, an adaptive step size mechanism guided by gravitational–repulsive coordination is developed. This mechanism dynamically adjusts the extension step size according to the local obstacle distribution, reducing invalid sampling and increasing the number of effective sampling points while strictly ensuring obstacle avoidance safety, thereby accelerating both path search and optimization processes. Finally, a dichotomy-based dynamic boundary path smoothing strategy is integrated to generate smooth intermediate path points near obstacle boundaries. This strategy eliminates redundant inflection points and reduces path length while maintaining a safe distance from obstacles. The performance of the proposed algorithm is comprehensively verified through multiple sets of comparative experiments in both 2D grid maps and ROS-based manipulator simulation environments. The experimental results demonstrate that compared with the standard Informed-RRT* algorithm, the proposed method achieves a relative reduction of 77.17% in the average time to first initial path in complex environments. The path planning success rate increases from 21% to 95%, corresponding to an absolute increase of 74 percentage points and a relative increase of 352.38%. Additionally, the average path length is relatively reduced by 24.81%. Full article

Alzheimer’s disease (AD) is a serious neurodegenerative disorder that can severely affect behavior and thinking patterns, and is accompanied by frequent memory loss. The early diagnosis of AD is essential, as this can benefit the patient, but detecting AD is a complex process due to the nature of its associated clinical data. Electroencephalography (EEG) serves as a promising and cost-effective technique for analyzing AD-related brain activity patterns. In this work, a consolidated framework for detecting AD using EEG signals and hybrid models is proposed that uses a dataset that is available online. For the feature extraction module, five efficient techniques—Principal Component Analysis (PCA), Kernel Partial Least Squares (KPLS), Kriging Model, Isomap, and K-means clustering—are used. For feature selection, with the help of biomimetics-based concepts, three efficient algorithms are used: hybrid Cuckoo Search Optimization–Rat Swarm Optimization (CSO-RSO), Zebra Optimization (ZOA), and hybrid Gravitational Search Algorithm–Particle Swarm Optimization (GSA-PSO). Four interesting hybrid classifiers are utilized here to detect AD using EEG signals—hybrid Extreme Learning Machine–Adaboost (ELM–Adaboost), hybrid Classification and Regression Trees–Adaboost (CART–Adaboost), and hybrid weighted broad learning system-based Adaboost (HWBLSA), followed by a hybrid machine learning classification model with a soft voting technique—and, finally, these are compared with other standard machine learning classifiers. The highest classification accuracy of 98.71% is found when the Kriging Model feature extraction concept is combined with the hybrid GSA-PSO feature selection method and classified with the ELM–Adaboost classifier. Full article

Quantum optimization has recently drawn considerable attention as one of the possible applications of noisy intermediate-scale quantum computation, yet the problem of qubit requirement remains a major bottleneck when combinatorial optimization problems are converted into quantum circuits. This issue becomes especially critical in solving the capacitated vehicle routing problem (CVRP) with the quantum approximate optimization algorithm (QAOA), since the number of required qubits increases polynomially with respect to the number of nodes. This study investigates whether a heuristic divide-and-conquer strategy can be adapted to the quantum setting so as to improve qubit efficiency while preserving the optimization capability to a reasonable extent. The proposed method decomposes a single CVRP into multiple traveling salesman problems (TSPs) by the sweeping-based clustering method, searches for the sector configuration with the smallest angle sum by Grover’s search algorithm, and then solves each sector-wise TSP with the QAOA aided by the gravitational search algorithm. Experiments on five benchmark datasets show that the proposed approach attains feasible solutions within 3.4 to 12.7% of the reinforcement-learning baseline on the main test set. These results suggest that the proposed approach serves as a plausible quantum heuristic framework for constrained routing optimization, with the advantage of reducing the qubit burden by decomposing the original problem into smaller subproblems. Full article

This study introduces a multi-stage, multi-objective optimization framework based on the Gravitational Search Algorithm (GSA) for determining the optimal sizing and placement of distributed energy resources (DERs) and associated infrastructure. The proposed approach considers solar distributed generation (DG) units with battery storage systems (BSSs), wind DGs, shunt capacitors (SCs) and electric vehicle charging stations (EVCSs). With the rapid adoption of electric vehicles as part of global decarbonization efforts, integrating EVCSs into already stressed distribution networks poses significant operational challenges, often requiring system reinforcement supported by renewable-based DGs. The uncoordinated deployment of EVCSs and DGs can exacerbate power losses and deteriorate voltage profiles. To address these issues, the first stage of the methodology employs GSA to optimally allocate solar DGs with BSSs, wind DGs and SCs, targeting objectives such as minimizing power losses, enhancing voltage stability and alleviating substation loading. The second stage identifies optimal locations and maximum feasible capacities for EVCS integration. Finally, the third stage upgrades the network to mitigate the impacts of EVCS integration. The effectiveness of the proposed approach is validated through simulations on a practical 52-bus, 11 kV distribution network under hourly varying load, solar irradiance and wind velocity conditions for all seasons. The simulation results show an 85% reduction in power losses during peak hours, with nodal voltages maintained above 0.95 p.u. under all scenarios. Additionally, net-zero grid power exchange during peak periods confirms the full islanded operation. Full article

Beamforming plays a central role in enhancing the performance of communication systems; however, suppressing sidelobes in planar antenna arrays (PAAs) while maintaining a compact aperture remains a challenging nonlinear optimization problem. This article presents a two-dimensional (2D) beamforming synthesis framework for PAAs based on the Fractional-Order African Vulture Optimization Algorithm (FO-AVOA), with the objective of minimizing the peak sidelobe level (PSLL) through the joint optimization of amplitude excitations and element placements. The proposed method is benchmarked against established metaheuristic optimizers, including Particle Swarm Optimization (PSO), the Gravitational Search Algorithm (GSA), hybrid PSO–GSA (PSOGSA), the Runge–Kutta Optimizer (RUN), the Slime Mould Algorithm (SMA), Harris Hawks Optimization (HHO), and the baseline African Vulture Optimization Algorithm (AVOA). Simulation results demonstrate that the FO-AVOA, coupled with the proposed 2D formulation, yields superior sidelobe suppression relative to the competing approaches, achieving a lower PSLL with fewer radiating elements, thereby reducing array complexity and overall implementation cost. The obtained results validate the suitability of the FO-AVOA for solving PAA in the context of BFA beamforming and suggest the potential utility of the FO-AVOA for pattern synthesis for other array shapes in various communication systems. Full article

Differential wavefront sensing (DWS) is widely adopted for high-precision angular detection in interferometric systems, yet its measurement range is constrained by the nonlinear implicit phase–angle relationship. This paper proposes a strategy-enhanced differential evolution algorithm, termed Bi-inheritance and Tournament-Selection-based Differential Evolution (BiTsDE), to suppress nonlinear angular errors. The method introduces fitness-guided inheritance of mutation and crossover factors and tournament-based elite parent selection, enabling adaptive balance between global exploration and local exploitation. Unlike conventional DE variants that mainly tune control parameters, BiTsDE optimizes the evolutionary search strategy, enhancing early-stage diversity and late-stage convergence stability. Simulations demonstrate angular resolution better than 0.06 nrad within ±1 mrad. Experiments show that up to 600 μrad, BiTsDE reduces demodulation error by 99.9% compared with linear DWS, achieving 17.9 nrad precision and 42% faster convergence. These results validate BiTsDE as an effective solution for nonlinear error suppression in DWS-based high-precision optical metrology, particularly for space-based gravitational wave detection. Full article

This paper presents an adaptive UAV path planning algorithm, A*-APF, which combines the A* algorithm with the artificial potential field method (APF) to overcome challenges such as lengthy paths, lack of smoothness, and local optima in traditional path planning algorithms within intricate environments. The A*-APF algorithm utilizes the global heuristic search abilities of A* and integrates a dynamic adaptive mechanism for gravitational and repulsive coefficients based on target distance, obstacle density, and path curvature. This mechanism enables real-time adjustments of potential field parameters, improving both global optimality and local path smoothness. Simulation results demonstrate that the A*-APF algorithm surpasses A*, RRT, PRM, and GWO algorithms in terms of path length, smoothness, computational efficiency, and stability. Specifically, it reduces the average path length by 15–25%, enhances smoothness by 30–45%, and decreases computation time by nearly 90%. Physical experiments confirm that the algorithm achieves the shortest path, optimal obstacle avoidance, and superior stability in real-world environments, highlighting its global optimization capability, real-time performance, and potential for engineering applications in complex dynamic environments. These results emphasize the algorithm’s ability to enhance UAV stability during task execution. Full article

The Lunar Gateway is a planned small space station that will orbit the Moon and serve as a central hub for NASA’s Artemis program to return humans to the lunar surface and to prepare for Mars missions. This work presents a hybrid optimization strategy for designing minimum-fuel transfers from an Earth orbit to a Lunar Near-Rectilinear Halo Orbit. The corresponding optimal control problem—crucial for missions to NASA’s Lunar Gateway—is characterized by a high-dimensional, non-convex solution space due to the multi-body gravitational environment. To tackle this challenge, a two-stage hybrid optimization scheme is employed. The first stage uses a Genetic Algorithm heuristic as a global search strategy, to identify promising feasible trajectory solutions. Subsequently, the initial solution guess (or guesses) produced by GA are improved by a local optimizer based on a Sequential Quadratic Programming method: from a suitable initial guess, SQP rapidly converges to a high-precision feasible solution. The proposed methodology is applied to a representative cargo mission case study, demonstrating its efficiency. Our numerical results confirm that the hybrid optimization strategy can reliably generate mission-grade quality trajectories that satisfy stringent constraints while minimizing propellant consumption. Our analysis validates the combined GA-SQP optimization approach as a robust and efficient tool for space mission design in the cislunar environment. Full article

This paper examines the performance of nine physics-based metaheuristic algorithms—Electromagnetism-Like Algorithm (EMLA), Fluid Search Algorithm (FSA), Gravitational Search Algorithm (GSA), and six hybrids (EMLA + FSA, EMLA + GSA, FSA + ELMA, FSA + GSA, GSA + EMLA, GSA + FSA) for vehicle pathfinding. Performance is evaluated using four metrics: travel distance, time, energy consumption, and number of obstacles encountered, along with a weighted multi-objective cost combining these metrics. Simulation results show that hybrid algorithms generally outperform their individual counterparts. The ranking of algorithms varies with the weighting of the metrics. The hybrids involving EMLA consistently achieve the best overall performance across grid sizes. FSA + EMLA performs best when minimizing distance. EMLA + GSA is most effective when time is the priority. EMLA + GSA also performs best when energy use and obstacle avoidance dominate. It is recommended to use the cases FSA + GSA or FSA + EMLA for energy-efficient and obstacle-aware navigation, GSA or GSA + EMLA for achieving global optimality on complex maps, and EMLA + FSA and FSA + EMLA for dynamic environments requiring high safety. Full article

Home energy management systems have emerged as a crucial solution for enhancing energy efficiency, reducing carbon emissions, and facilitating the integration of renewable energy sources into homes. To fully realize their potential, these systems’ performance must be optimized, which involves addressing multiple objectives, such as minimizing costs and environmental impact. The Pareto frontier is a tool widely adopted in multi-objective optimization within home energy management systems’ operation, where a range of optimal solutions are produced. This study uses the Pareto curve to optimize the operational performance of home energy management systems, considering the state health of the battery to determine the best answer among the optimal solutions in the curve. The main reason for considering the state of health is the effects of the battery’s operation on the performance of energy systems, especially for long-term optimization outcomes. In this study, the performance of the battery is measured through a physical model named PyBaMM that is tuned based on swarm intelligence techniques, including the Whale Optimization Algorithm, Grey Wolf Optimization, Particle Swarm Optimization, and the Gravitational Search Algorithm. The proposed framework automatically identifies the optimal solution out of the ones in the Pareto curve by comparing the performance of the battery through the tuned physical model. The effectiveness of the proposed algorithm is demonstrated for a home, including four distinct energy carriers along with a 12 V 128 Ah LFP chemistry Li-ion battery module, where the overall cost and carbon emissions are the metrics for comparisons. Implementation results show that tuning the physical model based on the Whale Optimization Algorithm reaches the highest accuracy compared to the other methods. Moreover, considering the state of health of the battery as the selecting criterion will improve home energy management systems’ performance, particularly in long-term operation models, because it guarantees a longer battery lifespan. Full article

To address challenges in agent path planning within complex environments—particularly slow convergence speed, high path redundancy, and insufficient smoothness—this paper proposes KDB-RRT*, a novel algorithm built upon RRT.* This method integrates a bidirectional search strategy with a three-layer optimization framework: ① accelerated node retrieval via KD-tree indexing to reduce computational complexity; ② enhanced exploration efficiency through goal-biased dynamic circle sampling and a bidirectional gravitational field guidance model, coupled with adaptive step size adjustment using a Sigmoid function for directional expansion and obstacle avoidance; and ③ trajectory optimization employing DP algorithm pruning and cubic B-spline smoothing to generate curvature-continuous paths. Additionally, a multi-level collision detection framework integrating Separating Axis Theorem (SAT) pre-judgment, R-tree spatial indexing, and active obstacle avoidance strategies is incorporated, ensuring robust collision resistance. Extensive experiments in complex environments (Z-shaped map, loop-shaped map, and multi-obstacle settings) demonstrate KDB-RRT’s superiority over state-of-the-art methods (Optimized RRT*, RRT*-Connect, and Informed-RRT*), reducing average planning time by up to 97.9%, shortening path length by 5.5–21.4%, and decreasing inflection points by 40–90.5%. Finally, the feasibility of the algorithm’s practical application was further verified based on the ROS platform. The research results provide a new method for efficient path planning of intelligent agents in unstructured environments, and its three-layer optimization framework has important reference value for mobile robot navigation systems. Full article

Exoskeletons offer promising solutions for enhancing human mobility; however, personalizing assistance parameters to optimize physiological outcomes remains challenging. Human-in-the-loop (HIL) optimization has emerged as an effective strategy for tailoring device control, often using electromyography (EMG) as a real-time proxy for metabolic cost. This study simulates HIL optimization using surrogate models built from the average root mean square of the muscles’ activations (EMG-RMS) derived from treadmill walking trials with a robotic waist tether. Nine surrogate models were evaluated for prediction accuracy, including gradient boosting (GB), random forest, support vector regression, and Gaussian process variants. Seven global optimization algorithms were compared based on convergence time, EMG-RMS at optimum, and efficiency metrics. GB achieved the highest predictive accuracy (1.57% RAEP). Among optimizers, the gravitational search algorithm (GSA) produced the lowest EMG-RMS value (0.17 normalized units) and the fastest convergence (0.32 s), while particle swarm optimization (PSO) achieved 0.36 EMG-RMS in 1.61 s. These findings demonstrate the value of EMG-based simulation frameworks in guiding algorithm selection for HIL optimization, ultimately reducing the experimental burden in developing personalized exoskeleton assistance strategies. Full article

To address the issue of frequent power frequency overvoltage disconnection accidents in offshore WF caused by the capacitive effect of submarine cables, this paper proposes a double-layer RP optimal configuration method for large-scale offshore WF based on an adaptively improved GSA. Firstly, this paper considers both the RP capabilities of offshore WT themselves and RP compensation equipment, designing a two-layer “configuration-control” optimization framework for RP. The upper layer establishes an optimization configuration model with the objective of minimizing the total investment cost and operational expenses of the equipment. The lower layer establishes a RP optimization operation model with the objective of minimizing a weighted index that comprehensively considers system network losses, voltage deviations, and RP capacity margins. Then, to address the issue of traditional GSA being prone to local optima, this paper introduces a random factor into the mass calculation, combines elite concepts to selectively synthesize gravitational forces based on fitness values, and assigns larger random numbers to forces corresponding to superior particles. By introducing control parameters to adaptively update particle positions, an adaptively improved GSA is proposed, which is employed to solve the established double-layer RP optimization configuration model for large-scale offshore WF. Finally, simulation analysis is conducted on a large-scale offshore WF constructed using MATLAB R2020a. Compared with the basic GSA algorithm, the proposed method reduces the system loss by 50.59% and the voltage deviation by 64.75%. The research demonstrates that the proposed method can effectively enhance the stability of grid voltage and proves the effectiveness of the improved GSA and the proposed two-layer “configuration-control” optimization model. Full article