Search Results (715)

This paper proposes the Quantum-behaved Loser Reverse-learning Differential Evolution (QLRDE) algorithm to address the inherent limitations of the standard Differential Evolution (DE) algorithm, including slow convergence speed and the premature stagnation in local optima. QLRDE incorporates three innovations: quantum-behaved mutation strategies suppress premature convergence by leveraging quantum mechanics, the Loser Reverse-Learning Mechanism enhances diversity by reconstructing inferior individuals through opposition-based learning, and an adaptive parameter adjustment mechanism balances exploration and exploitation to improve robustness and convergence efficiency. Experimental evaluations on twelve benchmark functions confirm that QLRDE demonstrates better performance than existing algorithms in terms of search capability and convergence speed. Furthermore, QLRDE is employed for the 3D UAV path planning problem. QLRDE can generate B-Spline-based smooth flight paths and incorporate real-world constraints into the cost function. Simulation results confirm that QLRDE outperforms several competing algorithms with respect to path quality, computational efficiency, and robustness. Full article

Modern power distribution networks are increasingly challenged with nonlinear operating conditions, the high penetration of distributed energy resources, and conflicting operational objectives such as loss minimization and voltage regulation. Existing load-flow optimization approaches often suffer from slow convergence, premature stagnation in non-convex search spaces, and limited robustness when handling conflicting multi-objective performance criteria under fixed network constraints. To address these challenges, this paper proposes a Fractional Multi-Objective Load Flow Optimizer (FMOLFO), which integrates a fractional-order numerical regularization mechanism with an adaptive Pareto-based Differential Evolution framework. The fractional-order formulation employed in FMOLFO operates over an auxiliary iteration domain and serves as a numerical regularization strategy to improve the sensitivity conditioning and convergence stability of the load-flow solution, rather than modeling the physical time dynamics or memory effects of the power system. The optimization framework simultaneously minimizes physically consistent active power loss and voltage deviation within existing network operating constraints. Extensive simulations on IEEE 33-bus and 69-bus benchmark distribution systems demonstrate that FMOLFO achieves an up to 27% reduction in active power loss, improved voltage profile uniformity, and faster convergence compared with classical Newton–Raphson and metaheuristic baselines evaluated under identical conditions. The proposed framework is intended as a numerically enhanced, optimization-driven load-flow analysis tool, rather than a control- or dispatch-oriented optimal power flow formulation. Full article

The time-scale mismatch between rapid influent fluctuations and slow biochemical responses hinders the stability of wastewater treatment plants (WWTPs). Existing models often fail to capture shock signals due to noise interference (“signal pollution”). To address this, we propose the HD-MAED-LSTM model, which employs a “decompose-and-conquer” strategy. Targeting the dynamic characteristics of different components, this study innovatively designs heterogeneous attention mechanisms: utilizing Long-term Dependency Attention to capture the global evolution of the trend component, employing Multi-scale Periodic Attention to reinforce the cyclic patterns of the seasonal component, and using Gated Anomaly Attention to keenly capture sudden shocks in the residual component. In a case study, the effectiveness of the proposed model was validated based on one year of operational data from a large-scale industrial WWTP. HD-MAED-LSTM outperformed baseline models such as Transformer and LSTM in the medium-to-long-term (10-h) prediction of COD, TN, and TP, clearly demonstrating the positive role of differentiated modeling. Notably, in the core task of shock load early warning, the model achieved an F1-Score of 0.83 (superior to Transformer’s 0.77 and LSTM’s 0.67), and a Mean Directional Accuracy (MDA) as high as 0.93. Ablation studies confirm that the specialized attention mechanism is the key performance driver, reducing the Mean Absolute Error (MAE) by 56.7%. This framework provides precise support for shifting WWTPs from passive response to proactive control. Full article

Over-the-Air updates have emerged as a critical competitive frontier in the Software-Defined Vehicle era. While offering value creation opportunities, automakers face strategic uncertainty regarding pricing models (e.g., subscription vs. one-time purchase). To clarify these dynamics, this study develops an evolutionary game model of duopolistic pricing competition. Unlike traditional studies with exogenous payoff assumptions, we innovatively employ the Hotelling model to endogenously derive firm profit functions based on consumer utility maximization. The highlights of this study include: (1) We establish an integrated “static–dynamic” framework connecting micro-level consumer choice with macro-level strategy evolution; (2) We identify that product differentiation is the decisive variable governing market stability; (3) We demonstrate that under moderate differentiation, the market exhibits a robust self-correcting tendency towards “Tacit Collusion” (mutual high pricing). However, simulation results also warn that an asymmetric disruptive strategy by a market leader can override this robustness, forcing the market into a low-profit equilibrium. These findings provide theoretical guidance for automakers to optimize pricing strategies and avoid value-destroying price wars. Full article

Global optimization represents a fundamental challenge in computer science and engineering, as it aims to identify high-quality solutions to problems spanning from moderate to extremely high dimensionality. The Differential Evolution (DE) algorithm is a population-based algorithm like Genetic Algorithms (GAs) and uses similar operators such as crossover, mutation and selection. The proposed method introduces a set of methodological enhancements designed to increase both the robustness and the computational efficiency of the classical DE framework. Specifically, an adaptive termination criterion is incorporated, enabling early stopping based on statistical measures of convergence and population stagnation. Furthermore, a population sampling strategy based on k-means clustering is employed to enhance exploration and improve the redistribution of individuals in high-dimensional search spaces. This mechanism enables structured population renewal and effectively mitigates premature convergence. The enhanced algorithm was evaluated on standard large-scale numerical optimization benchmarks and compared with established global optimization methods. The experimental results indicate substantial improvements in convergence speed, scalability and solution stability. Full article

Water resources constitute a foundational strategic resource, and the efficiency of their spatial allocation profoundly impacts national sustainable development. This study integrates multi-regional input–output modeling, complex network analysis, and exploratory spatiotemporal data analysis methods to systematically examine the patterns, network structures, and spatiotemporal evolution characteristics of virtual water flows across 30 Chinese provinces from 2010 to 2023. Findings reveal the following: Virtual water flows underwent a three-stage evolution—“expansion–convergence–stabilization”—forming a “core–periphery” structure spatially: eastern coastal and North China urban clusters as input hubs, while East–Northeast–Northwest China served as primary output regions; The virtual water flow network progressively tightened and segmented, evidenced by increased network density, shorter average path lengths, and enhanced clustering coefficients and transitivity. PageRank analysis reveals significant Matthew effects and structural lock-in within the network; LISA time paths indicate stable spatial structures in most provinces, yet dynamic characteristics are prominent in node provinces like Guangdong and Jiangsu. Spatiotemporal transition analysis further demonstrates high overall system resilience (Type₀ transitions accounting for 47%), while abrupt transitions in provinces like Hebei and Liaoning are closely associated with national strategies and industrial restructuring. This study provides theoretical and empirical support for establishing a coordinated allocation mechanism between physical and virtual water resources and formulating differentiated regional water governance policies. Full article

To address the challenges of low precision, slow convergence, and poor anti-interference in traditional Particle Swarm Optimization (PSO) for Permanent Magnet Synchronous Motor (PMSM) speed control, a new Adaptive Hybrid Particle Swarm Optimization (AM-PSO) algorithm is proposed. This algorithm integrates adaptive dynamic inertia weight, hybrid local search mechanisms, neural network-based adjustments, multi-stage optimization, and multi-objective optimization. The adaptive dynamic inertia weight improves the balance, boosting both convergence speed and accuracy. The inclusion of Simulated Annealing (SA) and Differential Evolution (DE) strengthens local search and avoids local optima. Neural network adjustments improve search flexibility by intelligently modifying search direction and step size. Additionally, the multi-stage strategy allows broad exploration initially and refines local searches as the solution approaches, speeding up convergence. The multi-objective optimization further ensures the simultaneous improvement of key performance metrics like precision, response time, and robustness. Experimental results demonstrate that AM-PSO outperforms traditional PSO in PMSM speed control, achieving a 40% reduction in speed error, 25% faster convergence, and enhanced robustness. Notably, the speed error increased only marginally from 0.03 RPM to 0.05 RPM, showcasing the algorithm’s superior ability to reject disturbances. Full article

Promoting the synergistic development of green finance (GF) and green technology innovation (GTI) is crucial for achieving sustainable economic development. Based on the sample data of 30 provinces in China from 2010 to 2023, this study first investigates the theoretical mechanism of interactive coupling and then employs methods including Dagum Gini coefficient, spatial kernel density estimation, spatial correlation analysis, and a GTWR model to explore the spatiotemporal pattern, evolution trend, and driving factors of the coupling coordination between GF and GTI. The findings are as follows: (1) The coupling coordination degree (CCD) is about to transition from the moderate imbalance stage to the near imbalance stage, presenting a distinct spatial pattern of “higher levels and faster development in the east, and lower levels and slower development in the west”. (2) The Gini coefficient of the CCD shows an upward trend, with the degree of imbalance increasing year by year; the main sources of the overall differences follow this order: intra-regional disparity (G_w) > inter-regional disparity (G_b) > transvariation density (G_t). (3) The CCD between GF and GTI exhibits a positive spatial correlation, and the agglomeration degree is constantly increasing; the High-High Cluster areas are mainly concentrated in northern China. (4) Economic development level, financial development level, population scale, and urbanization level drive the coupling coordination between GF and GTI. This study provides new theoretical and empirical evidence for the complex coupling relationship and driving factors of GF and GTI and offers a key scientific basis for the Chinese government to formulate differentiated regional policies, thereby promoting the effective implementation of the green and low-carbon development strategy. Full article

As the digitalization and intelligent transformation of power systems accelerates, the Power Internet of Things (PIoT) plays a pivotal role in ensuring efficient energy transmission and real-time regulation. However, this openness and interconnectivity also expose the system to diverse cyber threats, where attackers can disrupt stable power communication and dispatch operations through means such as data tampering, denial-of-service attacks, and control intrusion. To characterize the dynamic adversarial process between attackers and defenders in the PIoT, this paper constructs a zero-sum differential game model for cyber attack–defense strategy selection. To achieve equilibrium in the formulated differential game, optimal control theory is employed to solve the optimization problems of the game participants, thereby deriving the optimal strategies for both attackers and defenders. Finally, simulation results illustrate the evolution of network resource competition between attackers and defenders in the PIoT. The results also demonstrate that our proposed model can effectively and accurately describe the evolution of the system security state and the impact of strategic interactions between attackers and defenders. Full article

Wildfires represent one of the most destructive natural disturbances, yet they play a fundamental ecological role in the regeneration and evolution of forest ecosystems. In Mediterranean regions, fire acts as a selective factor shaping plant adaptive strategies and the structure of vegetation mosaics. This study analyzes post-fire regeneration dynamics in Pinus radiata and P. pinaster plantations located in Roccaforte del Greco (Metropolitan City of Reggio Calabria, southern Italy), severely affected by the 2021 wildfires. Phytosociological surveys were conducted along permanent transects using the Braun-Blanquet method and analyzed through diversity indices (Shannon, Evenness), Non-Metric Multidimensional Scaling (NMDS), Indicator Species Analysis (IndVal), and hierarchical clustering. The results reveal a clear floristic differentiation among management conditions, with higher species diversity and variability, and a predominance of pioneer therophytes and hemicryptophytes in burned areas. The in situ retention of burned logs enhances structural and microenvironmental heterogeneity, facilitating the establishment of native species and supporting post-fire functional recovery. Overall, this preliminary study, focusing on early successional dynamics, suggests that the in situ retention of burned logs may positively contribute to ecosystem resilience and biodiversity in post-fire Mediterranean pine forests, while also highlighting the need for long-term monitoring to confirm the persistence of these effects. Full article

Investigating plant responses to climate change is critical for understanding phylogeography and devising conservation strategies. This study focuses on the Lycoris aurea (L’Hér.) Herb. complex in East Asia, a system characterized by high cytotype diversity (2n = 12–16), to test whether ecological niche differentiation drives its spatio-temporal evolution. We integrated dynamic niche modeling to reconstruct distribution dynamics from the Last Interglacial (LIG) to the future (2100). Results indicate that mainland China populations have expanded northward since the LIG, establishing their current patterns, while island populations (Taiwan, Ryukyu) remained relatively stable due to geographic constraints. Under future warming scenarios, the complex is projected to further expand northward. We identified key migration corridors, with high inter-cytotype connectivity in the Sichuan-Hubei region and intra-cytotype migration in the Yunnan Plateau and Nanling region. Although the two dominant cytotypes currently exhibit niche equivalency, they show distinct climatic sensitivities—Cytotype II is driven by precipitation and Cytotype IV by temperature—and are projected to diverge spatially in the future. These findings elucidate the evolutionary history of L. aurea and provide a reference for the conservation and utilization of Lycoris germplasm. Full article

To address the path-planning challenge for unmanned aerial vehicles (UAVs) in complex environments, this study presents an improved pelican optimization algorithm enhanced with multiple strategies (MIPOA). The proposed method introduces four main improvements: (1) using chaotic mapping to spread the initial search points more evenly, thereby increasing population variety; (2) incorporating a random Lévy-flight strategy to improve the exploration of the search space; (3) integrating a differential evolution approach based on Cauchy mutation to strengthen individual diversity and overall optimization ability; and (4) adopting an adaptive disturbance factor to speed up convergence and fine-tune solutions. To evaluate MIPOA, comparative tests were carried out against classical and modern intelligent algorithms using the CEC2017 and CEC2022 benchmark sets, along with a custom UAV environmental model. Results show that MIPOA converges faster and achieves more accurate solutions than the original pelican optimization algorithm (POA). On CEC2017 in 30-, 50-, and 100-dimensional cases, MIPOA attained the best average ranks of 1.57, 2.37, and 2.90, respectively, and achieved the top results on 26, 21, and 19 test functions, outperforming both POA and other advanced algorithms. For CEC2022 (20 dimensions), MIPOA obtained the highest Friedman average rank of 1.42, demonstrating its effectiveness in complex UAV path-planning tasks. The method enables the generation of faster, shorter, safer, and collision-free flight paths for UAVs, underscoring the robustness and wide applicability of MIPOA in real-world UAV path-planning scenarios. Full article

The Walrus Optimization (WO) algorithm, a metaheuristic inspired by walrus behavior, is known for its competitive convergence speed and effectiveness in solving high-dimensional and practical engineering optimization problems. However, it suffers from a tendency to converge to local optima and exhibits instability during the iterative process. To overcome these limitations, this study proposes an improved WO (IMWO) algorithm based on the integration of Differential Evolution/best/1 (DE/best/1) mutation, Logistics–Sine–Cosine (LSC) Mapping, and the Beta Opposition-Based Learning (Beta-OBL) strategy. These strategies work synergistically to enhance the algorithm’s global exploration capability, improve its search stability, and accelerate convergence with higher precision. The performance of the IMWO algorithm was comprehensively evaluated using the CEC2017 and CEC2022 benchmark test suites, where it was compared against the original WO algorithm and six other state-of-the-art metaheuristics. Experimental data revealed that the IMWO algorithm achieved average fitness rankings of 1.66 and 1.33 in the two test suites, ranking first among all compared algorithms. The WSN coverage optimization problem aims to maximize the monitored area while reducing perception blind spots under limited node resources and energy constraints, which is a typical complex optimization problem with multiple constraints. In a practical application addressing the coverage optimization problem in Wireless Sensor Networks (WSNs), the IMWO algorithm attained average coverage rates of 95.86% and 96.48% in two sets of coverage experiments, outperforming both the original WO and other compared algorithms. These results confirm the practical utility and robustness of the IMWO algorithm in solving complex real-world engineering problems. Full article

Urban competitiveness encapsulates a city’s comprehensive capacity for development. Utilizing panel data for 282 prefecture-level cities from 2012 to 2020, this study constructs an evaluation index system of urban competitiveness and applies kernel density estimation, standard deviational ellipse, trend-surface analysis, and Dagum Gini coefficient decomposition to examine its spatiotemporal evolution and regional disparities. The results indicate that: (1) urban competitiveness exhibits a V-shaped recovery, with intensified polarization after 2016, widening innovation advantages in the East, and persistent decline in the Northeast; (2) the spatial configuration follows a “dual-gradient, polycentric” structure, characterized by an inverted-U pattern along the east–west axis and an expanding gradient gap along the north–south axis; (3) club convergence and hierarchical entrenchment coexist, as polarization deepens in the East while the Northeast tends toward internal balance; and (4) the competitiveness center shifts southeastward, accompanied by a pronounced fragmentation trend in the Northeast. Based on these findings, the paper proposes differentiated spatial governance, the development of multi-tier innovation networks, and the promotion of green and sustainable development as integrated strategies to systematically enhance urban competitiveness. Full article

Diurnally asymmetric warming under global climate change is reshaping terrestrial ecosystems, with important implications for vegetation productivity, biodiversity, and carbon sequestration. However, the mechanisms underlying the delayed and differentiated vegetation responses to daytime and nighttime warming, particularly under interacting precipitation regimes, remain insufficiently understood, limiting accurate assessments of ecosystem resilience under future climate scenarios. Clarifying how vegetation responds dynamically to asymmetric temperature changes and precipitation, including their lagged effects, is therefore essential. Here, we analyzed the spatiotemporal evolution of growing-season Normalized Difference Vegetation Index (NDVI) across the Yellow River Basin from 2001 to 2022 using Theil–Sen median trend estimation and the Mann–Kendall test. We further quantified the lagged responses of NDVI to daytime maximum temperature (Tmax), nighttime minimum temperature (Tmin), and precipitation, and identified their dominant controls using partial correlation analysis and an XGBoost–SHAP framework. Results show that (1) growing-season climate in the YRB experienced pronounced diurnal warming asymmetry: Tmax, Tmin, and precipitation all increased, but Tmin rose substantially faster than Tmax. (2) NDVI exhibited an overall increasing trend, with declines confined to only 2.72% of the basin, mainly in Inner Mongolia, Ningxia, and Qinghai. (3) NDVI responded to Tmax, Tmin, and precipitation with distinct lag times, averaging 43, 16, and 42 days, respectively. (4) Lag times were strongly modulated by topography, soil properties, and hydro-climatic background. Specifically, Tmax lag time shortened with increasing elevation, soil silt content, and slope, while showing a decrease-then-increase pattern with potential evapotranspiration. Tmin lag time lengthened with elevation, soil sand content, and soil pH, but shortened with higher potential evapotranspiration. Precipitation lag time increased with soil silt content and net primary productivity, decreased with soil pH, and varied nonlinearly with elevation (decrease then increase). By explicitly linking diurnal warming asymmetry to vegetation response lags and their environmental controls, this study advances process-based understanding of climate–vegetation interactions in arid and semi-arid regions. The findings provide a transferable framework for improving ecosystem vulnerability assessments and informing adaptive vegetation management and conservation strategies under ongoing asymmetric warming. Full article