Abstract The current study focuses on optimizing AC TIG welding parameters to achieve higher hardness and better control of the heat-affected zone (HAZ) using a Taguchi L16 orthogonal array on 10 mm-thick AA5083 alloy. The study establishes a heat-input-oriented optimization framework for AC TIG welding of thick AA5083 aluminum alloy by integrating statistical optimization with metallurgical characterization. The control parameters, including AC pulse modulation frequency, peak current, and welding speed, were selected to achieve improved hardness and a smaller heat-affected zone (HAZ). Qualitative microstructural characterization using optical microscopy, SEM, and XRD indicated comparatively finer weld morphology and fewer visible defects under lower heat-input conditions, while XRD confirmed the presence of the α-Al matrix together with Al₁₃Fe₄, Mg₂Si, and minor Si phases. The results indicate that lower heat input contributes to improved hardness retention and reduced HAZ width within the investigated parameter range, providing practical guidance for AC TIG welding of thick AA5083 aluminum alloy.
Abstract This study numerically investigates and compares the glass fiber–reinforced polymer (GFRP) reinforced concrete beams flexural behaviour with four concrete types: geopolymer concrete (GPC), ordinary Portland cement concrete (OPC), and fiber-reinforced geopolymer concrete (FRGC), fiber-reinforced concrete (FRC). Despite increasing interest in sustainable and advanced material systems, particularly those integrating polypropylene fibers with FRP reinforcement, comprehensive design provisions remain limited, and GFRP-reinforced fibrous geopolymer concrete has been insufficiently explored. No new experimental testing is conducted. Instead, the study develops high-fidelity finite element models calibrated and compared with previously published experimental data by the authors. The primary contribution centers on numerical modeling and interpretation. The validated models accurately capture load-deflection response, strain distribution, cracking, stiffness loss, deformability, and failure modes for all material systems. The applicability of ACI 440.11-22 provisions is also assessed. While the code yields conservative predictions of flexural capacity, the finite element analysis (FEA) results indicate strong agreement with experimental outcomes. Including fibers increases flexural capacity by up to 20% and deformability by 24–28% versus non-fibrous versions. The study demonstrates that validated numerical modeling can reliably assess emerging FRP-concrete systems and support future code development for sustainable structures.
The Central Area of Makkah faces severe seasonal environmental pressures from heavy vehicular traffic, particularly during Hajj. Using the DPSIR framework, this study conducts a spatial and environmental diagnosis of this high-density urban core. To assess the environmental conditions, spatial sampling and field measurements were conducted at 32 strategic locations, using a calibrated Extech VPC300 Video Particle Counter for PM2.5 and PM10, along with portable multi-parameter detectors for CO and CO2. Field measurements captured two seasonal operational extremes: Phase 1 (baseline peak congestion, 20 Dhu Al-Qi’dah 1446AH) and Phase 2 (strict traffic cordon restrictions, serving as a real-world LEZ proxy, 2 Dhu al-Hijjah 1446AH). Paired-samples t-test validation indicates that during peak congestion, localised CO2 reached 1,750 ppm and noise peaked at 95 dB, while localised PM2.5 and PM10 particle counts demonstrated elevated concentrations. Methodological baseline calculations reveal that the localised prayer shuttle fleet generates a carbon footprint four times larger (57,888 tons of CO2) than the inbound regional fleet, condition compounded by the dense ‘street canyon’ morphology. Conversely, spatial analysis identifies 2.5 million m2 of vacant land. As a targeted ‘Response’, this paper introduces the Makkah Low Emission Zone (M-LEZ) framework. Distinct from conventional urban LEZs, the M-LEZ introduces a dynamically phased seasonal framework that utilises underused peripheral land for green infrastructure and transit hubs. It aims to balance mass transit logistics with microclimate sustainability and promotes public health for pedestrians.
Abstract The use of Buton rock asphalt paving blocks as alternative surface layers of road structures can be a solution. As construction materials, paving blocks come in various sizes and shapes, as well as different constituent materials. This study aims to evaluate the effects of crumb rubber size and paving block thickness on the flexural strength, volumetric characteristics, and immersion resistance of asphalt concrete materials used as road surface layers. Flexural strength and volumetric tests were conducted on paving blocks with thicknesses of 60, 80, and 100 mm using three-point flexural tests and Marshall tests. Data analysis used an exponential model, response surface methodology, and ANOVA. The results revealed that variations in crumb rubber particle size decrease penetration by 10–11% and increase the softening point by 9–10%, as described by a quadratic model (R² = 0.94–0.99). This change affects volumetric characteristics: as the crumb rubber granule size decreases, the voids in the mix and the voids in the mineral aggregate decrease, whereas the voids filled with asphalt and the density increase (R² = 0.94–0.95). The flexural strength increases by 5–10% as the crumb rubber decreases. The residual flexural strength index is influenced by the CR particle size: the smaller the CR particle size is, the greater the resistance to immersion. Increasing the thickness of the paving block increases flexural strength.
The transition from fossil fuels to renewable alternatives has accelerated research on biodiesel utilization in industrial combustion systems. However, the high viscosity and low volatility of biodiesel often result in poor atomization and unstable flames. This study investigates the effects of injection pressure on the combustion and flame characteristics of biodiesel-n-butanol blends, aiming to enhance burner efficiency. Experiments were conducted using B40 and B100 biodiesel blended with 20
Abstract This paper investigates full-duplex (FD) short-packet transmission (SPT) in underlay networks (UNs) with energy harvesting (EH) - referred to as FDSPTUNEH - to enhance spectrum utilization, reduce latency, and improve reliability, energy efficiency, and spectral efficiency. By integrating cognitive radio, radio frequency EH, FD, and SPT, FDSPTUNEH holds promise for future wireless networks. However, its broadcast nature makes it highly susceptible to eavesdropping attacks. To assess security of different multiple access (MA) schemes in FDSPTUNEH, this work develops a security analysis that accounts for practical impairments, comprising self-interference, imperfect successive interference cancellation, arbitrary fading severity, imperfect channel state information, and hardware limitations. Computer simulations confirm the accuracy of the analysis and reveal the considerable secrecy mitigation caused by these impairments. The study further shows that secrecy capability is ameliorated by optimizing the energy transmitter’s power, energy conversion efficiency, and maximum allowable interference. Notably, under these conditions, orthogonal MA demonstrates better security performance than nonorthogonal MA in FDSPTUNEH.
Abstract Heat exchangers are central to thermal management in industrial, energy, and HVAC systems, motivating continued interest in advanced working fluids that enhance thermal performance. This study presents a computational investigation of the thermo-hydraulic performance of three nanofluids (MWCNT, SiO₂, and ZnO) flowing through a double-tube heat exchanger under identical geometrical and boundary conditions. An experimentally validated CFD model is used to isolate the effects of flow rate, nanoparticle concentration, and operating temperature on heat-transfer enhancement and pressure drop. Relative to water, nanofluids enhance heat transfer rates by approximately 6–40%, with a pressure drop of 8–106%. Increasing the volumetric flow rate to 600 l/h and the nanoparticle concentration to 0.25% yields the most significant thermal enhancement among all nanofluids. However, thermo-hydraulic evaluation shows that maximum heat-transfer enhancement alone does not guarantee optimal performance. When thermal and hydraulic effects are combined using the performance evaluation criterion (PEC), SiO₂ at 0.25% concentration yields a thermo-hydraulic improvement of approximately 11% relative to water, whereas ZnO, despite enhanced heat-transfer performance, incurs substantially higher-pressure losses. At high operating temperatures, heat transfer is further intensified, with ZnO exhibiting the highest sensitivity to thermal loading. The results establish a controlled reference for nanofluid comparison and provide practical guidance for selecting working fluids for compact, high-efficiency heat exchangers in HVAC, energy recovery, and industrial thermal management applications.
Abstract This paper presents a decentralized formation control system for a swarm of quadcopters that combines an improved artificial potential field (IAPF) with an event-based reconfiguration control (ERC) mechanism to enable safe navigation in cluttered and constrained environments. The framework is implemented in a hierarchical architecture, where a swarm-level planner generates position setpoints and a low-level cascaded PID controller stabilizes each vehicle. To support practical 3D missions, the planner is extended with autonomous takeoff logic, dynamic 3D goal/waypoint navigation, nonlinear trajectory tracking, and a formation-orientation mechanism that aligns the topology with the direction of motion. High-fidelity simulations with a five-agent swarm demonstrate that ERC preserves nominal performance in a baseline obstacle field while significantly improving feasibility in narrow passages, where the static IAPF strategy fails. In the narrow-gap scenario, ERC completes the mission in 59.735 s with $$\overline{RMSE}_{\textrm{total}}=0.692$$ m and $$\overline{\Phi}=0.878$$ , and it maintains mission success under periodic wind gusts with nearly unchanged traversal time and improved aggregate coordination metrics ( $$\overline{RMSE}_{\textrm{total}}=0.694$$ m, $$\overline{\Phi}=0.925$$ ). A U-shaped trap case highlights a limitation of purely reactive planners, while waypoint and circular-tracking scenarios validate the proposed extensions for complex navigation tasks.
Abstract Urban public transport is a key component of sustainable mobility in developing cities, where ownership and use of individual motorized vehicles continues to increase, while congestion raises concerns about accessibility and environmental impacts. A key challenge in delivering better services is understanding the nature of service prformance characteristics and their roles in influencing user satisfaction, thereby improving operational efficiency and system effectiveness. However, there is a shortage of empirical research integrating performance-based SERVPERF with Structural Equation Modeling (SEM) to understand service performance influences passenger satisfaction and future usage intention in irregular, resource constrained bus and minibus service systems, especially in Middle Eastern cities. This study uses a performance-only SERVPERF framework integrated with Structural Equation Modeling (SEM) to test the relationships between perceived service quality, user satisfaction, and future use intention for the bus and minibus systems of Baghdad City. Six underlying service dimensions were conceptualized: comfort, perceived time adherence, safety & security, operations & availability, accessibility, and fare and affordability. Due to limited discriminative power of fare affordability, a conclusion based on 550 valid survey responses, it was excluded from the final model. The structural model demonstrated good fit (CFI = 0.961, RMSEA = 0.039) and exhibited moderate-to-substantial explanatory power, accounting for 58.9% of the variance in satisfaction (R² = 0.589) and 36.6% of the variance in behavioral intention (R² = 0.366). Perceived time adherence (β = 0.589, p < 0.001) and operations & availability (β = 0.487, p < 0.001) are the most predictive of passenger satisfaction, compared to other factors. Satisfaction acts as the central mechanism linking service performance to behavioral intention. Model comparison supported retaining a full mediation structure at the global level, while path-level results revealed a mixed mediation pattern, with partial mediation for Time Adherence, full mediation for Operations & Availability, and no mediation effects for Comfort, Safety, and Accessibility. The findings imply that, in informal and resource-constrained transit systems such as Baghdad City operational reliability and service availability should be prioritized through basic operational standards and performance monitoring such as headway stability, vehicle availability, and actual waiting time to achieve the largest gains in satisfaction and sustained public transport use. Conclusively, the validated SERVPERF–SEM framework provides a robust and transferable tool for engineering-oriented performance assessment and operational improvement of public transport systems in resource-scarce developing cities.
Abstract Progressive deployment of active safety systems requires precise tools to assess collision mitigation performance. Virtual Forward Simulation (VFS) plays a pivotal role in the prospective assessment of Advanced Driver Assistance Systems (ADAS). Reliable VFS routines depend on validated models capable of resolving collision mechanics, particularly when ADAS interventions fail to avoid a collision and the focus shifts to evaluating impact mitigation. However, software tools often omit exact vehicle geometries in accident reconstruction practice. Consequently, when graphical data is compiled for detailed accident records, specifically in the Pre-Crash Matrix (PCM) format, vehicle shapes are typically simplified. Although encoding realistic vehicle shapes demands substantial technical effort during database compilation, this study demonstrates that such fidelity is essential for ensuring high-fidelity outcomes. Data fidelity requirements are identified by investigating the influence of vehicle shape on the reliability of 2D car-to-car collision simulations. A validated tool based on a Reduced Order Dynamic Model (RODM) was used to simulate road collisions using progressively simplified vehicle geometries. Simulations were statistically analyzed and verified through a real-world case study. Findings indicate that while simpler momentum-based models remain insensitive to geometric variations, advanced models like RODM require detailed shapes to accurately resolve impact dynamics. Neglecting this leads to a significant underestimation of key parameters, specifically $$\Delta V$$ (by more than 5 km/h) and occupant injury risk (by up to 60% in real-world scenarios). By defining these data fidelity requirements, this work provides a methodological framework to bridge the gap between current database standards and high-fidelity virtual safety research.
Bone drilling is an important process in orthopaedic and dental surgery, where excessive heat and poor surface integrity can lead to thermal osteonecrosis and loss of implant stability. Thermal response and post-drilling surface roughness are frequently examined independently, even though their interdependence remains poorly understood despite extensive analysis of their individual characteristics. This study combines an experimental investigation of maximum temperature (Tmax) and surface roughness (Ra Rz) during drilling under the same conditions. A full factorial design was adopted with SS316L and ZrO2 drill bits of 2.5, 3.0, and 3.5 mm diameter, a feed rate of 30–50 mm/min, and a spindle speed of 900–1300 rpm. The findings demonstrate that the spindle speed is the most important factor affecting Tmax and surface roughness. SS316L exercises produced increased temperatures (Tmax of 61 °C) and roughness (Ra of 17 μm), often higher than the 47 °C thermal limit. By comparison, ZrO2 drills had lower Tmax (< 42.5 °C) and were more uniform in surface quality. The Pearson correlation analysis showed that Tmax and Ra had a moderate, diameter-dependent correlation with SS316L, whereas weaker correlations were observed with ZrO2. The results indicate that combined thermo-surface testing is important and suggest that ZrO2 drills perform better under controlled experimental conditions.
Abstract This study comparatively investigates the effects of graphite and titanium electrodes on process performance, energy consumption, and product quality in the electrolysis-based production of biodiesel from waste cooking oil (WCO). To optimize the production parameters, a Taguchi L18 orthogonal array and Response Surface Methodology (RSM) were systematically employed. The results demonstrated that while graphite electrodes operate effectively at lower voltages (approximately 20 V), titanium electrodes require higher voltage levels (60 V) to achieve significant conversion. Under optimized conditions, titanium electrodes achieved a remarkably high biodiesel conversion yield of 93.00% with a specific energy consumption (SEC) of approximately 1.19 kWh/kg, whereas graphite electrodes yielded 83.45% with a significantly lower SEC of 0.25 kWh/kg. Statistical analysis (ANOVA) revealed that the KOH catalyst concentration was not a statistically significant factor within the narrow range tested (0.5-1.0 wt%), suggesting that electrolysis operates through a different catalytic mechanism involving in-situ ion generation. Fuel property characterizations, including GC-MS analysis and cold-flow properties (CFPP of -26.86 C for graphite and − 23.67 C for titanium), confirmed that biodiesels produced with both electrode types show a close alignment with EN 14,214 standards. This research highlights that electrode material selection is a decisive factor influencing not only the conversion efficiency and energy intensity but also the molecular-level characteristics of the resulting fuel.
Abstract Carbon dioxide hydrogenation over iron-based catalysts is governed by a complex interplay between thermodynamic driving forces, kinetic constraints, dynamic phase evolution, and promoter-induced electronic effects. Despite extensive mechanistic studies, quantitative separation of activity- and selectivity-controlling domains remains unresolved due to multicollinearity in experimental datasets. Here, we develop an interpretable machine learning framework to statistically disentangle thermodynamic and compositional control in Fe-based CO₂ hydrogenation toward light hydrocarbons. A curated dataset of 184 fixed-bed reactor experiments (2022–2025) was analyzed using ensemble learning algorithms with SHAP-based interpretability. XGBoost achieved robust predictive performance ( $$R_{test}^2 = 0.78$$ for selectivity; 0.59 for conversion). Importantly, SHAP analysis reveals that CO₂ conversion is predominantly governed by thermokinetic parameters (temperature, residence time), whereas hydrocarbon selectivity is primarily dictated by catalyst composition, especially cobalt and alkali promoter loadings. This quantitative separation provides statistical evidence for partial decoupling between RWGS-driven conversion and Fischer–Tropsch chain-growth probability. Optimal operating windows (300–320 °C, ≤3 MPa) emerge from multivariate interactions rather than monotonic trends. The study establishes interpretable machine learning as a hypothesis-generating tool for multiscale catalytic systems and provides a transferable framework for disentangling structure–process coupling in (CO₂) utilization technologies.
Abstract Fused Deposition Modeling (FDM) slicers typically apply uniform process parameters across entire parts, leading to inefficient material usage and limited adaptation to local geometric or functional requirements. This work presents a post-slicing G-code optimization framework that operates directly at the instruction level without modifying the original CAD model or re-running the slicer. The framework integrates three modules: (1) curvature-aware adaptive layer height control, (2) function-specific reinforcement for load-bearing regions, and (3) AI-based weak-zone detection using unsupervised clustering. Physically correct extrusion is enforced through incremental ΔE handling in relative mode (M83). The method is validated through three case studies: a quadcopter frame, an ASTM D638 tensile test specimen, and a complex mounting bracket, all printed using a Creality Ender-3 printer (0.4 mm nozzle, Nylon filament) with baseline G-code generated by Ultimaker Cura 5.0. Compared to the original toolpaths, the optimized G-code preserves geometric fidelity (RMS deviation < 0.05 mm) while reducing effective material extrusion by approximately 22–25% across the three geometries. Print time increased by 7–9% due to feedrate smoothing and localized reinforcement. These results suggest that instruction-level, post-slicing optimization can improve material efficiency for functional FDM components across diverse geometries, though further validation across additional printers, materials, and part complexities is needed to establish full generalizability.
Abstract This paper presents a robust Wide Strip Transition Matrix (WSTM) method for analyzing the vibration behavior of a restrained orthotropic plate subjected to in-plane compression and resting on Winkler-Pasternak foundations. This study highlights the complexity of the mathematical analysis of this advanced plate, which has significant applications across various engineering fields, including aerospace panels, composite bridge decks, pavements, and machine foundations. WSTM integrates analytical nodal lines and wide strips into transition matrices via Winkler-Pasternak foundation coupling, yielding a lower-order eigenvalue problem across a limited number of wide strips. This process achieves superior modal accuracy compared to other advanced analytical and numerical methods, while significantly reducing computational costs and processor memory. The innovation of the WSTM lies in integrating the wide strip concept with a precise transition matrix derived directly from the modal ordinary differential equations, based on the plate's partial differential equation of motion. The numerical results are obtained by employing the WSTM method as an initial value algorithm that utilizes an improved transition matrix operating on panels divided into wide strips. Compared to numerical methods like finite element, finite difference, or finite strip methods, the proposed approach eliminates the need for extensive element formulation and the solution of large systems of algebraic equations. The advantages of the present method are discussed, and its convergence, stability, and accuracy are demonstrated. The findings strengthen the reliability of predicting dynamic responses and stability limits. This evidence, in turn, enables the optimized design of plate-like structural components in engineering applications. .
Abstract The proliferation of machine-type communications in 5G New Radio networks introduces challenges for uplink resource scheduling due to heterogeneous quality of service requirements and diverse traffic patterns. This paper proposes a QoS-aware uplink scheduler that balances throughput, fairness, and resource utilization. The proposed algorithm employs a recursive fitness function that integrates channel quality, priority, fairness, and a temporal urgency factor to satisfy quality of service constraints while mitigating user equipment starvation. Extensive simulation results demonstrate that the proposed scheduler achieves a near-Pareto-optimal trade-off among throughput, fairness, resource utilization, buffer occupancy, delay, and priority satisfaction compared to baseline scheduling algorithms, while maintaining polynomial-time computational complexity.
Abstract Digital Elevation Models (DEMs) are fundamental to engineering projects, influencing the accuracy of hydrologic modeling, earthwork calculations, and infrastructure design. The resolution and quality of a DEM are primarily determined by the density of survey points and the interpolation algorithm used. This study presents a comparative evaluation of four common interpolation techniques—Natural Neighbor (NN), Kriging, Inverse Distance Weighting (IDW), and Spline—to generate a high-accuracy local DEM for a bare land area, typical of civil engineering project sites, on the Najran University campus, Saudi Arabia. A total of 7,026 high-precision GPS points were collected and divided into training (80%) and validation (20%) datasets. The vertical accuracy was assessed using Root Mean Square Error (RMSE) and the coefficient of determination (R2). The results demonstrated that the Natural Neighbor interpolation method achieved superior performance with the lowest RMSE of 0.124 m and the highest R2 of 0.969. Critically, the study evaluated the impact of data density by thinning the training dataset by 0% to 75%. It was found that a 75% reduction in data points—which equates to a significant saving in surveying time and cost—increased the RMSE by only ~ 2 cm when using the NN algorithm. This finding indicates that the Natural Neighbor method is not only the most accurate but also the most robust and cost-effective solution for generating reliable DEMs. The outcomes of this research provide a practical framework for engineers to optimize surveying efforts and produce high-fidelity terrain models essential for precise earthwork volume calculation, drainage design, and flood risk assessment in local-scale projects.
Abstract This paper presents a stability analysis of a strongly nonlinear damped cubic-quintic oscillator using three approaches: the classical multiple scales (CMS) method, an enriched multiple scales (EMS) method based on homotopy perturbation, and numerical continuation via MatCont. Comparisons across different nonlinearity regimes reveal that CMS accuracy degrades substantially when the perturbation parameter $$\varepsilon$$ ε is not small. In the cubic-dominant case ( $$\alpha _3 = 10$$ α 3 = 10 , $$\alpha _5 = 1$$ α 5 = 1 , $$\varepsilon = 1$$ ε = 1 ), CMS overestimates peak amplitudes by approximately 60% and mislocates bifurcation points, whereas EMS predictions remain within 1–2% of numerical results. Even under strong cubic nonlinearity ( $$\alpha _3 = 100$$ α 3 = 100 ), EMS maintains agreement within 3% while CMS errors reach 25%. The EMS method also accurately captures both stable and unstable solution branches, with stability boundaries matching Floquet-based numerical detection to within 0.1%. These results suggest that EMS may serve as a useful analytical tool for strongly nonlinear oscillators where traditional perturbation methods lose accuracy.
Abstract Voltage unbalance factor in three-phase power systems adversely affects induction motor performance and efficiency, particularly in industrial settings. Conventional efficiency tests based on IEEE 112 are intrusive and impractical for in-situ applications. This paper proposes a non-intrusive efficiency estimation approach for three-phase induction motors operating under unbalanced voltages, combining positive/negative sequence modeling, IEEE 112-F1/Form F2 loss calculations, and heuristic optimization. Four equivalent circuit parameters (stator leakage reactance, magnetizing reactance, core-loss resistance, and rotor resistance) are identified from terminal measurements and nameplate data. The framework is implemented using three heuristic algorithms, Particle Swarm Optimization (PSO), Harris Hawks Optimization (HHO), and Fox Optimization Algorithm (FOX), and experimentally validated on a 1.1 kW induction motor for four load factors (25%, 50%, 75%, 100%) and Voltage Unbalance Factor (VUF) levels between 0 and 5%. IEEE 112-A is used as the reference method. The results show that PSO and HHO reproduce IEEE 112-A efficiencies with high accuracy for medium and high load factors (LF ≥ 50%), with full-load deviations typically below 1% for all VUF levels, while FOX exhibits larger errors, particularly at light load. Furthermore, the experiments confirm that efficiency degradation due to VUF is most pronounced at low load factors, even when VUF remains within the 5% NEMA limit. The proposed framework enables practical in-situ efficiency monitoring under realistic unbalanced supply conditions without motor shutdown.
Abstract Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for seamless integration with future sixth-generation (6G) wireless systems. However, their performance critically depends on the optimization of numerous phase shifts across RIS elements, making efficient phase shift design crucial. Existing optimization methods often face limitations, including unbalanced exploration and exploitation, slow convergence, suboptimal channel gain, high computational overhead, poor scalability, and reduced robustness. Among them, the Genetic Algorithm (GA) is a widely used meta-heuristic for RIS phase shift optimization, valued for its strong exploration capabilities in complex, constrained, non-convex problems. Nevertheless, GA suffers from limited exploitation and high sensitivity to control parameters, leading to performance degradation. To address these limitations, this paper proposes an enhanced GA that integrates pheromone-guided selection and heuristic-driven recombination—adapted from ant colony optimization (ACO)—into the GA’s crossover operation only. This novel lightweight hybridization strengthens exploitation without compromising exploration. A new entropy-based mathematical framework is developed to rigorously analyze the exploration–exploitation trade-off. Simulations are performed to assess the enhanced GA while comparing it with the standard GA and another popular optimizer, the particle swarm optimizer (PSO). The results demonstrate that the enhanced GA consistently outperforms both the standard GA and PSO. In small-scale system, its exploration–exploitation balance enables it to reach the 95% balance point in only 6 iterations, compared to 66 and 43 for the original GA and PSO, respectively. It also converges faster with 7 against 62 and 43, achieving up to 1.6 dB higher channel gain. With a minimum runtime of 55 ms, it is more computationally efficient than the original GA (140 ms) and PSO (95 ms), reducing runtime by up to 2.6 times. Its scalability is evident, as its channel gain advantage grows from 1.6 dB to 2.4 dB with increasing network size. Furthermore, the enhanced GA demonstrates superior robustness under algorithmic parameter variations and channel estimation errors.
