The core challenge is what mathematicians call a multimodal optimization problem. A drone’s route can be described as a chain of waypoints in three-dimensional space, and the quality of any candidate route is scored by a cost function that blends four ingredients: total path length, flight altitude, proximity to threats, and smoothness of turns. The altitude term penalizes flying too low or too high, since cameras and sensors work best within a defined band. The threat term models hazards such as buildings, no-fly zones, and bad weather as cylindrical regions, with a safety buffer whose size depends on positioning accuracy and environmental conditions. The smoothness term constrains the yaw and pitch angles between consecutive path segments so that the resulting route is actually flyable within a drone’s mechanical limits.

Traditional swarm intelligence algorithms, from Particle Swarm Optimization to the Grey Wolf Optimizer, have long been applied to this kind of problem because they can search vast solution spaces without needing a precise mathematical model of the terrain. But they share two chronic weaknesses: slow convergence and a tendency to get stuck in local optima, meaning the algorithm settles on a merely decent route while a far better one hides elsewhere in the landscape. The original Red-billed Blue Magpie Optimizer, introduced in 2024, mimics how real magpies hunt in small groups of two to five birds for small prey and in flocks of more than ten for larger quarry, and even cache surplus food for later. It performed well, but the Changsha team found it still converged slowly and could be trapped by deceptive terrain.

The new DRBMO algorithm attacks those weaknesses with four coordinated upgrades. The first targets the very beginning of the search. Standard algorithms scatter their initial candidate solutions randomly, which can leave some regions of the search space crowded and others empty. The researchers instead used a piecewise linear chaotic map, a deterministic nonlinear system whose output, though bounded, wanders ergodically across its entire range. By seeding the population through this chaotic sequence, the initial candidates spread far more evenly across the search space, giving the algorithm a head start in covering terrain and improving its odds of escaping local optima later on.

The second upgrade is a progressive dynamic step size. In the original algorithm, the size of each search agent’s jump depends heavily on random numbers, producing erratic movement and poor final precision. DRBMO replaces this with a step that starts large, at 1.0, and shrinks linearly to 0.1 as iterations proceed. Early on, big steps let the magpie-inspired agents sweep broadly across the solution space; later, small steps allow fine-grained refinement around promising regions. This gradual tightening balances the two fundamental forces of any metaheuristic: exploration, the wide search for where good solutions might live, and exploitation, the focused polishing of the best candidates found so far.

The third innovation imports a weapon from a different algorithmic family. Differential Evolution, a classic technique from the 1990s, creates new candidate solutions by taking one random individual and adding a scaled difference between two others. That difference vector gives the perturbation a direction, unlike the Gaussian noise used in the original magpie algorithm, which jiggles candidates blindly around the population mean. By grafting this differential mutation operator into the predation phase of DRBMO, the researchers gave their search agents the ability to make asymmetric leaps across the solution space, which proved especially valuable on rugged, multi-peaked landscapes where mean-guided updates tend to funnel everyone toward the same mediocre peak.

The fourth change reworks the dynamic weight factor that governs how strongly the current best solution pulls the rest of the population. The team replaced the original formula with a nonlinear expression based on exponential functions, which starts large to encourage wide exploration and then falls rapidly as iterations advance, accelerating convergence in the final stages without letting the swarm collapse prematurely onto a local optimum. Importantly, the researchers also showed that all these additions come at essentially no computational cost: a formal time complexity analysis found that DRBMO retains the same overall complexity, O(T times N times D), as the original algorithm, where T is the number of iterations, N the population size, and D the problem dimension.

The evidence for these claims comes in layers. Qualitative experiments tracking individual search trajectories, exploration-exploitation ratios, and population diversity confirmed that the chaotic initialization produced uniformly spread agents and that the differential operator helped the algorithm escape multi-peak traps. On the CEC 2022 benchmark suite of ten standard test functions, run thirty independent times in both ten and twenty dimensions against six rival algorithms including an improved magpie variant, an improved Particle Swarm Optimization, a Q-learning-guided Grey Wolf Optimizer, the Dung Beetle Optimizer, and a multi-strategy Harris Hawks Optimization, DRBMO found the global optimum on nearly every function and posted lower standard deviations, a sign of robustness rather than lucky runs. A Wilcoxon rank-sum test at the five percent significance level backed the statistical significance of the gaps. An ablation study then dismantled the algorithm piece by piece, showing that each of the four strategies contributed measurable gains on its own.

The decisive test, however, was three-dimensional drone path planning in simulated mountainous terrain spanning 100 by 150 by 5 kilometers, with cylindrical threat zones added to create four scenarios of escalating difficulty. Across all four, DRBMO produced the shortest paths and the lowest total planning costs. In the most complex scenario, it beat the improved magpie optimizer, the original magpie optimizer, the improved Grey Wolf Optimizer, the Dung Beetle Optimizer, and improved Particle Swarm Optimization by 9.21, 16.97, 17.52, 18.69, and 19.18 percent respectively on path cost, with path length advantages of up to 16.73 percent. The algorithm converged within roughly one hundred iterations in every scenario, and its planned routes were smooth, kept a sensible altitude, and avoided sharp maneuvers. Planning time was equal to or slightly better than the original algorithm, confirming that the improvements bought performance without computational overhead.

The authors are candid about the method’s limits. DRBMO carries a relatively large number of tunable parameters, and poor settings can degrade performance, making it somewhat sensitive to configuration. Its multi-stage framework also adds computational burden on extremely high-dimensional problems, and it was built for static environments; when the landscape changes mid-flight, the population typically needs reinitialization. Future work, the team says, will pursue adaptive parameter control, lightweight strategies such as dimension-grouped perturbation updates and random subspace reduction, and dynamic response mechanisms with environmental change detection, potentially drawing on adaptive restarts and memory-based transfer learning. For now, the study offers a striking example of a broader trend in artificial intelligence research: taking the cooperative hunting tricks of a bird, sharpening them with chaos theory and evolutionary operators, and turning them into a practical tool that may help autonomous drones find their way through the world’s most cluttered skies.

Subject of Research: A bio-inspired metaheuristic optimization algorithm for UAV three-dimensional path planning in complex environments

Article Title: An investigation of a differential red-billed blue magpie optimization algorithm for UAV three-dimensional path planning

Article References: He, Y., Huang, B., Gao, Q., Huang, Q., & Li, S. (2026). An investigation of a differential red-billed blue magpie optimization algorithm for UAV three-dimensional path planning. Cluster Computing, 29(13), Article 747. https://doi.org/10.1007/s10586-026-06564-1

Image Credits: AI Generated

DOI: 10.1007/s10586-026-06564-1

Keywords: UAV path planning, swarm intelligence, metaheuristic algorithms, red-billed blue magpie optimizer, differential evolution, chaotic mapping, drone navigation, 3D optimization, Cluster Computing, local optima, convergence speed, computational intelligence

Dynamic multi-objective optimization is one of the most demanding corners of computational intelligence. In these problems, an algorithm must simultaneously optimize several conflicting objectives, such as cost versus reliability or speed versus energy consumption, while the objective functions, the decision variables, and even the constraints themselves evolve over time. The set of optimal trade-off solutions, known as the Pareto Set, and its image in objective space, the Pareto Front, migrate as the environment shifts. Evolutionary algorithms, which maintain and refine a population of candidate solutions through selection and variation, are natural candidates for this task because they already search for a diverse set of trade-offs rather than a single point. The difficulty lies in what happens the instant the environment changes: a population carefully tuned to the old problem may be badly positioned for the new one, and re-optimizing from scratch wastes precious computational time.

Most existing prediction-based approaches treat the Pareto Set as a monolithic object. They track the movement of its centroid or of individual points as a whole and extrapolate where it will land next. The authors of the new study argue that this wholesale view overlooks a crucial structural fact: within the decision space, different variables play different roles in shaping convergence and diversity. Some variables primarily determine how close the population gets to the true Pareto Front, while others mainly control how widely the solutions spread across that front. When environmental changes are nonlinear, abrupt, or irregular, a one-size-fits-all prediction can misjudge the movement of both groups at once, weakening the algorithm’s adaptability precisely when adaptability matters most.

EAAP attacks this problem in two stages. The first stage is structural analysis. The algorithm builds a Minimum Spanning Tree over the decision variables, a graph-theoretic construction that connects all variables with the minimum total edge weight and thereby exposes the correlation structure among them. By analyzing this tree, the algorithm separates variables into two categories: convergence variables, whose values strongly influence the location of solutions relative to the Pareto Front, and diversity variables, which govern the spread of the population. This classification, informed by earlier work on decision variable analysis in dynamic multi-objective optimization, allows the algorithm to reason about the moving Pareto Set at the level of variable function rather than treating every dimension identically.

Once the variables are classified, the algorithm deploys a multi-stage prediction model for the convergence variables. In the first stage, a sliding-window Kalman Filter processes the historical centroid information of the population. The Kalman Filter, a celebrated recursive estimator originally developed for aerospace tracking, fuses noisy observations with a motion model to produce a refined estimate of the current state and a preliminary prediction of the movement direction. The sliding window means that only the most recent history is used, which keeps the filter responsive to changes in the dynamics of the environment rather than being dragged by stale data. This denoising step matters because the centroids computed from an evolving population carry substantial noise, and feeding raw centroids directly into a predictor can propagate errors forward.

The second stage of the prediction pipeline refines the Kalman estimate using a memory-guided Particle Swarm Optimization model. Particle Swarm Optimization simulates a swarm of agents that adjust their positions based on personal and collective experience, and here the swarm is harnessed not to solve the optimization problem directly but to polish the predicted positions of the convergence variables. By exploiting the archive of historical elite solutions, the model can correct the preliminary Kalman prediction, blending the filter’s short-term dynamical estimate with longer-term knowledge of where high-quality solutions have historically resided. The combination is notable because Kalman filters and swarm-based refinement have each appeared in previous dynamic optimization algorithms, but their staged integration, with the filter providing a denoised directional estimate that the swarm model then refines, is a distinctive feature of EAAP.

Diversity variables receive a fundamentally different treatment. Because these variables control the spread of the population rather than its convergence, predicting their movement is less meaningful than regenerating it. EAAP therefore applies Latin hypercube sampling, a stratified statistical technique that ensures a sample is spread uniformly across the entire parameter space, to reconstruct a well-distributed population in the new environment. Latin hypercube sampling divides each dimension into equal intervals and draws samples so that every interval is represented exactly once, avoiding the clumping that plagues naive random sampling. By reseeding the diversity variables this way, the algorithm guarantees that the regenerated population covers the new Pareto Set broadly, giving the evolutionary search a strong foundation for the next optimization cycle.

To evaluate the approach, the team ran comparative experiments on 22 benchmark functions drawn from the standard test suites used in the dynamic multi-objective optimization community, including the widely used problem definitions associated with the CEC 2018 competition on dynamic multiobjective optimization. These benchmarks simulate a range of environmental change types, from smooth periodic shifts to abrupt and irregular transitions, and they allow researchers to measure how quickly and accurately an algorithm re-converges to the new Pareto Front after each change. The reported results indicate that EAAP demonstrates strong effectiveness and robustness across this suite, suggesting that the combination of variable classification, staged prediction, and stratified resampling holds up under diverse change scenarios rather than being tuned to a single kind of dynamics.

The significance of this work extends beyond benchmark competitions. The literature the authors build on spans a striking range of applications where dynamic multi-objective optimization is already in play: energy-efficient scheduling of flexible job shops in the aerospace industry, raw ore allocation in mineral processing, power and spectrum allocation in cognitive radio networks, and multi-autonomous-underwater-vehicle path planning. In each of these settings, the environment genuinely changes, whether through fluctuating demand, varying ore grades, shifting interference conditions, or moving obstacles, and an algorithm that can anticipate the new optimum rather than rediscover it from scratch translates directly into computational savings and faster response times. A classification-aware predictor could, in principle, be grafted onto many existing evolutionary frameworks, since the MST-based analysis and the two-stage prediction operate as modular components around a standard evolutionary core.

The study also situates itself within a broader trend of importing increasingly sophisticated predictive machinery into evolutionary computation. Earlier efforts have employed forward-looking prediction, memory strategies, incremental support vector machines, Bayesian vector autoregression, transfer learning, reinforcement learning, and echo state networks to anticipate environmental change. Each brings trade-offs between model complexity, data requirements, and responsiveness. EAAP’s contribution is to show that a carefully staged pipeline, in which a classical linear estimator handles noise reduction and direction estimation before a swarm-based memory model performs refinement, can be competitive while remaining conceptually transparent. The work was supported by the National Natural Science Foundation of China under grants 62466054 and 62066041, and the authors, Haoyu Wu, Yongjie Ma, Leiyuan Ma, Liang Wang, Yu Wang, and Xinrong Li, are affiliated with the College of Physics and Electronic Engineering at Northwest Normal University. As dynamic environments become the norm rather than the exception in industrial optimization, algorithms that understand the internal anatomy of the problems they solve, variable by variable, are likely to define the next generation of adaptive search.

Subject of Research: A dynamic multi-objective evolutionary algorithm using decision variable classification and multi-stage prediction

Article Title: A dynamic multi-objective optimization evolutionary algorithm based on decision variable classification and multi-stage prediction model

Article References: Wu, H., Ma, Y., Ma, L., Wang, L., Wang, Y., & Li, X. (2026). A dynamic multi-objective optimization evolutionary algorithm based on decision variable classification and multi-stage prediction model. Cluster Computing, 29(14), Article 818. https://doi.org/10.1007/s10586-026-06634-4

Image Credits: AI Generated

DOI: 10.1007/s10586-026-06634-4

Keywords: dynamic multi-objective optimization, evolutionary algorithm, decision variable classification, Kalman filter, particle swarm optimization, Latin hypercube sampling, Pareto front, minimum spanning tree, prediction model, computational intelligence, Cluster Computing, dynamic

The research team, led by Manisha Galphade and colleagues, started from a deceptively simple observation: the statistical character of wind speed data changes with the seasons. The mean, variance, distribution shape, autocorrelation, and extreme values of wind records all shift as atmospheric conditions move from one regime to another. A single general-purpose model, trained on a full year of data, tends to overfit the turbulent high-wind seasons and underfit the calmer ones. Classical statistical tools such as ARIMA and seasonal ARIMA assume linearity and stationarity, assumptions that wind power data routinely violates. Machine learning methods like random forests and support vector machines capture nonlinear relationships but struggle with long-term temporal dependencies. Even the deep learning tools that have transformed other fields face difficulties when the underlying signal changes its personality every few months.

Transformers, the architecture behind the current artificial intelligence boom, have been proposed as a solution. Models such as the Informer, the Temporal Fusion Transformer, and the Autoformer bring powerful attention mechanisms to time-series forecasting, and they have shown impressive results on long sequences. But the authors point out a practical catch: wind farms rarely produce the enormous, clean datasets that transformers crave. Sensor coverage is limited, measurements are noisy, and self-attention scales quadratically with sequence length, driving up training time and hardware demands. With many hyperparameters to tune and a tendency to overfit small, messy datasets, transformers can be an expensive and fragile choice for operational wind forecasting, particularly at installations that lack cloud-scale computing.

The team’s answer is a serial hybrid that chains two recurrent architectures together. A long short-term memory network, or LSTM, first processes the raw wind power sequence. LSTMs are built around memory cells governed by three gates—an input gate, a forget gate, and an output gate—that control what information is stored, discarded, and passed forward, allowing the network to learn long-range dependencies without suffering from vanishing gradients. The output of this LSTM is then fed into a bidirectional LSTM, or BiLSTM, which reads the refined representations in both forward and backward directions. Because the BiLSTM operates on already-filtered, higher-level features rather than noisy raw signals, the combination performs a kind of progressive feature abstraction: the LSTM extracts stable sequential patterns, and the BiLSTM layers contextual understanding on top of them.

Before any training begins, the raw data undergoes careful preprocessing. Missing values are filled using imputation methods, with a random forest regression imputer chosen for one dataset because it captures nonlinear relationships without assuming a particular data distribution. Outliers are detected by standardizing each data point and flagging values that deviate too far from the mean, and the cleaned features are then rescaled to the range between zero and one using min-max normalization. These steps matter more than they might appear: wind farm records are riddled with sensor dropouts, negative power readings caused by measurement artifacts, and spikes from unusual atmospheric events, and a forecasting model fed uncleaned data will happily learn the noise.

The researchers tested their approach on two real wind farms with very different characters. The first is the Yalova wind farm in western Turkey, a 54,000-kilowatt installation of 36 turbines whose supervisory control and data acquisition system recorded wind speed, direction, generated power, and theoretical power at ten-minute intervals throughout 2018, yielding more than 46,000 records. The second is the Sotavento wind farm in Galicia, Spain, with 24 onshore turbines and a capacity of 17,560 kilowatts, providing hourly meteorological and generation data for 2014. In both cases the data was split by season, with thirty-day windows drawn from winter, spring, summer, and autumn, and the models were evaluated using root mean squared error, mean absolute error, and the coefficient of determination.

The results reveal a striking seasonal fingerprint. At Yalova, spring and summer proved the easiest to forecast, with the hybrid model achieving coefficients of determination as high as 0.99 and its best summer performance at a lookback window of six time steps. Autumn, a transitional season mixing summer and winter behavior, produced moderate errors, while winter was hardest of all: volatility, sudden spikes and drops, and non-stationary behavior pushed the error metrics up and the explanatory power down to roughly 0.80. At Sotavento the same pattern emerged, with the optimal lookback window shifting from five steps in spring to three in summer and just one in autumn. The authors identify this systematic analysis of lookback window optimization as a central contribution, showing that no single window length serves all seasons and that adaptive, season-specific temporal context can substantially improve accuracy.

The hybrid model did not just beat its individual components. Against standalone LSTM and BiLSTM baselines, it reduced mean absolute error by 1.38 percent in spring, 12.1 percent in summer, about 4.67 percent in autumn, and 5.94 percent in winter. It also outperformed temporal convolutional networks, attention-based LSTMs, and transformer models across both datasets, and a Diebold-Mariano statistical test confirmed that most of these improvements were significant at the five percent level, with the gaps against TCN and transformer models highly significant. An ablation study reinforced the design choices: removing either component, reversing the layer order, changing the unit counts, dropping regularization, or altering the learning rate all degraded performance, sometimes dramatically, confirming that the specific architecture and its hyperparameters are genuinely well balanced rather than accidentally lucky.

Perhaps the most persuasive numbers concern efficiency. The proposed model contains just 10,913 parameters, occupies 171 kilobytes, and trained in about 43 seconds—faster than every competing model tested, including the much larger TCN with its 89,473 parameters. Its normalized accuracy of 96.34 percent topped the field, edging out the LSTM at 95.11 percent and the transformer at 92.03 percent. The authors attribute this to a favorable bias-variance trade-off: transformers and attention models carry representational capacity that moderate-sized wind datasets cannot exploit, so their extra parameters mostly buy overfitting risk, while the LSTM’s gating mechanism acts as an inherent noise filter that attention mechanisms sometimes lack. With linear computational complexity in sequence length rather than quadratic, the architecture is well suited to real-time forecasting systems and edge deployments where memory and compute are scarce.

The implications reach well beyond two Spanish and Turkish wind farms. Accurate seasonal forecasting underpins grid planning, reserve operation, and maintenance scheduling, and as wind penetration grows, the cost of forecast error grows with it. The study is candid about its limits: winter remains difficult for every model tested, and the authors suggest that incorporating external meteorological variables, attention mechanisms, and ensemble methods could push performance further. But the core message is a useful corrective to the prevailing enthusiasm for ever-bigger architectures. For seasonal wind power prediction on realistic, medium-sized datasets, a thoughtfully composed pair of recurrent networks—one reading time forward, one reading it both ways—can beat the giants while running on hardware that fits in a wind farm’s back pocket.

Subject of Research: Seasonal wind power forecasting using a hybrid LSTM-BiLSTM deep learning model tested on two wind farms

Article Title: Seasonal wind power forecasting using data-driven and computational intelligence techniques

Article References: Galphade, M., Dande, A., More, N., Nikam, V., & Hatkar, V. (2026). Seasonal wind power forecasting using data-driven and computational intelligence techniques. Results in Engineering, 32, Article 113122. https://doi.org/10.1016/j.rineng.2026.113122

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113122

Keywords: wind power forecasting, LSTM, BiLSTM, deep learning, seasonal variability, renewable energy, time series forecasting, transformer models, grid stability, Yalova wind farm, Sotavento wind farm, computational intelligence

The research, authored by Jun Yin of Beijing Cnoce Technologies Co., Ltd., addresses a persistent weakness in interactive intelligent systems. When requirements are complex, vague, or embedded in specialized background knowledge, conventional systems struggle to accurately interpret human needs or intentions. Rather than treating each user request as a one-shot prediction problem, the proposed algorithm treats interpretation as an iterative cycle: the system computes a response, collects real-time feedback about how well that response matched the demand, and continuously refines its behavior according to the demand response structure. The goal is to improve both the intelligent response capability of the system and the experience of the user on the other side of the interaction.

At the foundation of the method is an effective data collection mechanism. The system gathers real-time response information as interactions unfold, building up the raw material needed to evaluate and improve its own performance. In a known finite domain, the algorithm derives a corresponding feature statistical decision, which anchors the interpretation process in measurable properties of the data rather than in guesswork. This statistical grounding matters because human-computer cooperation networks are messy: nodes in such networks are not equally spaced or equally loaded, and the demands arriving at each node differ in both volume and character.

One of the paper’s central technical contributions lies in how it handles that unevenness. The algorithm extracts the load characteristics of non-equidistant nodes in the human-machine cooperation network using the knowledge of a large language model. In practical terms, the language model’s broad knowledge base is used to characterize how computational and communicative load is distributed across nodes that sit at irregular positions in the network topology. This allows the system to understand where demand concentrates, where it is sparse, and how those patterns shift as users interact with the system over time. Load characterization of this kind is essential for any cooperative system that must allocate responses fairly and quickly across many simultaneous users.

The second pillar of the method is attention. The algorithm constructs a knowledge framework for the large language model according to a two-level attention matrix, and then analyzes an iterative demand response strategy for human-machine cooperation in two stages. The two-level attention structure allows the model to weigh different pieces of information at different granularities, deciding which features of a request deserve emphasis and which contextual signals should modulate that emphasis. By organizing the response strategy into two stages, the system separates coarse interpretation from fine-grained refinement, letting each iteration pass progressively sharper approximations of user intent back into the loop.

The experimental results reported in the study offer concrete evidence that the design works as intended. The algorithm is able to remove abnormal data when the threshold is set to 10, and across three datasets the proportion of abnormal data reaches its lowest state under the proposed method. Anomaly removal at a fixed threshold is more than a housekeeping detail; noisy or malformed interaction data can poison the feedback loop that iterative systems depend on, so a reliable filtering stage protects the integrity of every subsequent optimization step. The fact that the algorithm achieves the lowest abnormal-data proportion across all three datasets suggests that the filtering mechanism is robust rather than tuned to a single data distribution.

Performance gains extend beyond data cleaning. After applying the algorithm, both throughput and utilization rate improved significantly, and the response speed became faster. Throughput measures how much useful work the system completes per unit of time, while utilization rate reflects how effectively the system’s resources are kept busy on productive tasks. Improvements in both metrics, combined with faster responses, indicate that the iterative loop does not merely reinterpret requests more accurately but also runs efficiently enough to lighten the operational burden on the underlying infrastructure. For users, faster response speed is the most visible benefit; for system operators, higher throughput and utilization translate directly into capacity savings.

The study situates itself within a growing body of work on human-machine cooperation. Prior research has explored cooperation between human operators and machines in scheduling problems, man-algorithm cooperation in the intelligent design of clothing products across multiple links, and collision-free path planning for industrial robot manipulators that must account for safe human-robot interaction. More recent efforts have examined how autonomous vehicles can assimilate human feedback in reinforcement learning models and how human-centric digital twins can support human-machine collaboration. What distinguishes the new algorithm is its explicit integration of large language model knowledge into the cooperation loop itself, rather than using language models only as peripheral interfaces.

That integration reflects a broader trend. Large language models have already been applied to domain-specific text mining, as demonstrated by materials-science language models built for information extraction, and to collaborative requirements elicitation, where human-machine iterative approaches use language models to help elicit and model software requirements. Natural language processing systems have been deployed to extract structured events from clinical texts and to classify sensitive records in mental healthcare. The new work extends this lineage into the domain of demand response, where the

Subject of Research: An iterative demand response algorithm that integrates large language model knowledge to improve human-computer cooperation in interpreting complex user requirements.

Article Title: Iterative Demand Response Algorithm for Human-Computer Cooperation Integrating Knowledge of Large Language Model

Article References: Iterative Demand Response Algorithm for Human-Computer Cooperation Integrating Knowledge of Large Language Model. (n.d.). https://doi.org/10.1007/s10489-026-07428-2

Image Credits: AI Generated

DOI: 10.1007/s10489-026-07428-2

Keywords: demand response algorithm, human-computer cooperation, large language model, iterative optimization, attention matrix, load characteristics, anomaly detection, throughput, utilization rate, user intent, Applied Intelligence, computational intelligence