Search Results (65)

Learning path optimization is crucial in intelligent educational systems, with the core challenge of efficient multi-objective sequential decision-making under complex prerequisite constraints. To address the poor generalization of existing methods relying on fixed operator scheduling or handcrafted heuristics, this paper proposes a hyper-heuristic framework based on Dueling Deep Q-Network (Dueling DQN-HH), formulating operator selection as a sequential decision-making process for dynamic adaptive scheduling of low-level operators. The framework adopts priority-based encoding to unify learning path representation (decoupling the hyper-heuristic layer from the problem domain) and designs a composite reward mechanism integrating reward shaping, exploration incentives, and computational cost awareness to balance solution quality and efficiency. Additionally, it employs a dueling network architecture with prioritized experience replay to enhance policy learning stability. Experimental results show the proposed method outperforms representative baseline algorithms in solution quality, convergence stability, and computational efficiency. The framework demonstrates superior performance across multiple objectives, particularly in minimizing the total learning time (F_time), as validated on two heterogeneous datasets: MOOCCube (Computer Science) and PsyDataset (Psychology). Further ablation studies and operator evolution analyses verify its adaptive scheduling capability under different objectives and knowledge graph structures, demonstrating strong objective independence and cross-dataset generalization. Full article

In this paper, a new hybrid prediction method is proposed for estimating remaining useful life, emissions, and performance parameters using experimental data obtained from a micro-turbojet engine. Experiments were conducted under various rotational speed conditions, yielding a total of 342 measurement points. Turbine speed, exhaust gas temperature, fuel flow rate, and thrust were considered as input variables in the study. Thermal efficiency, total power, CO₂, and NO₂ were considered as output variables. The experimental findings showed that thermal efficiency varied between 0.49% and 7.1%, total power between 0.266 and 13.94 kW, and CO₂ emissions by volume between 0.317% and 2.183%. The proposed RL-MH-LR-CBR approach combines the advantages of multiple methods. In this method, the interpretable formulation of linear regression serves as the foundation. Additionally, in the adaptive meta-heuristic optimization process, a hyper-heuristic selection mechanism based on the UCB1-based multi-arm bandit approach is used to select the optimal algorithm from among the meta-heuristic methods. Finally, the CatBoost-based residual error learning component aims to capture non-linear patterns that cannot be explained by the linear model. The method was compared with 14 different methods on both the NASA C-MAPSS FD001 dataset and real engine data. The results demonstrate that the proposed framework exhibits more balanced, stable, and higher generalization capabilities compared to classical regression models and powerful AI methods, particularly in non-linear, noisy, and heterogeneous outputs. In the real engine dataset, the proposed method produced R² values of 0.968 for CO₂ and 0.936 for NO₂, while the predictive performance was even stronger for thermal efficiency and total power, with corresponding R² values of 0.998 and 0.995, respectively. Additionally, the method demonstrated a clear advantage in hard-to-model outputs by reducing the error level to 0.061 in NO₂ predictions. These findings demonstrate that the proposed approach is not limited to micro-turbojet-engines. The developed method provides a robust decision support framework that is applicable, scalable, and generalizable to predictive maintenance, emissions monitoring, energy systems, aviation analytics, and other highly dynamic engineering problems. Full article

The construction of partial minimum spanning trees is an NP-hard problem, leading to the development of various heuristic algorithms. Existing heuristics, including Kruskal’s algorithm, frequently employ shortest paths to connect tree components. This study introduces an approximate algorithm for constructing the minimum Steiner tree, which serves as the optimal structure for diffusion multicast. The proposed approach utilizes graph-based structures that provide advantages over conventional shortest-path methods. The algorithm incorporates connections analogous to those in simple Steiner trees when required. These simple trees are represented by hyperedges, and a Hyper Metric Closure can also be applied. Experimental results indicate that this hypergraph-based method enables constructions that more closely approximate the optimal Steiner tree cost compared to traditional pairwise techniques, offering a scalable balance between computational complexity and routing efficiency. Full article

The assignment of hospital rooms to patients is a critical operational decision that has a direct impact on patient safety, infection control, and staff workload. This study introduces HRPM–IRC, an epidemiology-aware hyper-heuristic framework developed to optimize room–patient matching by minimizing the risk of nosocomial infections, reducing travel and specialty mismatch costs, and promoting equitable nurse workload distribution. A mixed-integer linear programming model is formulated to capture infection transmission probabilities, isolation and cohorting requirements, and multi-ward capacity constraints. On top of this model, a bio-inspired hyper-heuristic adaptively selects and refines low-level heuristics, including cohort-first greedy allocation, risk-gradient swaps, and pathogen-aware local MILP refinement, on the basis of contextual epidemiological indicators and reinforcement learning. The framework was validated using a real-world dataset obtained from a tertiary hospital in Lebanon, comprising 142 anonymized patient admissions, 35 rooms, and six nursing teams. Results demonstrate that HRPM–IRC consistently reduces modeled infection risk and workload imbalance by up to forty percent compared to conventional assignment heuristics while maintaining near-real-time decision-making capabilities suitable for dynamic hospital operations. These findings underscore the effectiveness of epidemiology-aware hyper-heuristics in enhancing hospital resilience, improving infection prevention, and supporting fair resource utilization in data-limited healthcare environments typical of Lebanon and other middle-income countries. Full article

In this paper, a Multiple-Objective Memetic Algorithm (MOMA) is proposed to address the Energy-Efficient Distributed Assembly Permutation Flow-Shop Scheduling Problem (EEDAPFSP) by explicitly exploiting the structural and objective symmetries inherent in the scheduling process, with the dual objectives of minimizing the maximum completion time (makespan) and total energy consumption (TEC). The EEDAPFSP is a complex NP-hard optimization problem in modern sustainable manufacturing that balances production efficiency and environmental sustainability. During the global search phase, a symmetry-preserving dual-search framework is constructed, in which diverse and potential regions in the solution space are explored by symmetrically generating time-dominant product sub-sequences (TDPSs) and energy-dominant product sub-sequences (EDPSs) in the individuals of each iteration, enabling complementary exploration from time- and energy-oriented perspectives. This is accomplished through the incorporation of a variable-weight metric technique and a first product fixed strategy into an estimation distributed algorithm-based hyper-heuristic (EDAHH), so as to maintain a balanced and symmetric probabilistic modeling of decision patterns with respect to the makespan and energy consumption. In the local search phase, two problem-specific designed neighborhood structures are proposed to refine the job sequences corresponding to the TDPS and EDPS in the superior sub-population, effectively reducing both the makespan and TEC. A box-level $\epsilon$ dominance technique based on the crowding distance is proposed for Pareto archive updating. Additionally, an energy-saving strategy is embedded throughout the algorithm, incorporating three mechanisms—job processing delay, machine shutdown and restart control, and speed regulation—to further optimize TEC during both the global and local search phases. Finally, extensive computational experiments are carried out, and the results demonstrate that the MOMA achieves significantly better performance in terms of the inverted generational distance ($IGD$) and the quality metric $\rho$ compared with state-of-the-art algorithms. The resulting Pareto front of non-dominated solutions provides a comprehensive set of trade-offs between energy consumption and the makespan, offering decision makers flexible and efficient scheduling options. Full article

The Capacitated Vehicle Routing Problem (CVRP) has a wide range of applications in logistics and transportation. Current metaheuristics typically rely on manually added constraints. A hyper-heuristic framework can reduce the dependency on domain-specific knowledge. Therefore, this research proposes a Clustered Simulated Annealing algorithm (CSA). When generating the initial solution of the distribution path, the CSA adopts the Clustered Clarke–Wright Savings algorithm (CCW), the core of which is to use the K-means algorithm to cluster according to the Euclidean distances between the distribution points. The CCW can reduce the search range of the optimization problem by clustering and generating the initial solution quickly, enabling the CSA to perform better in data processing and real-time updates. The CSA then optimizes the initial solution using the Improved Simulated Annealing Hyper-Heuristic algorithm (ISAHH), divided into upper and lower layers. The Improved Simulated Annealing High-Level Heuristic strategy (ISAHLH) is used to select the Low-Level Heuristic operators (LLHs). At the same time, LLHs are used to generate new distribution paths. This research designs an Improved Tabu Low-Level Heuristic operator (ITabuLLH), which can search for several different paths simultaneously in a single iteration, thus improving the convergence speed of the algorithm. ISAHLH and ITabuLLH both use the Unequal Probability Selection mode (UEPS) to speed up the search process. The CSA is tested on the Uchoa benchmark set, and the results verify that the optimal value improvement of the CSA solution is higher than 20% when compared to eleven other algorithms. Full article

Urban logistics face complexity due to traffic congestion, fleet heterogeneity, warehouse constraints, and driver workload balancing, especially in the Heterogeneous Multi-Trip Vehicle Routing Problem with Time Windows and Time-Varying Networks (HMTVRPTW-TVN). We develop a mixed-integer linear programming (MILP) model with dual-peak time discretization and exact linearization for heterogeneous fleet coordination. Given the NP-hard nature, we propose a Hyper-Heuristic based on Cumulative Reward Q-Learning (HHCRQL), integrating reinforcement learning with heuristic operators in a Markov Decision Process (MDP). The algorithm dynamically selects operators using a four-dimensional state space and a cumulative reward function combining timestep and fitness. Experiments show that, for small instances, HHCRQL achieves solutions within 3% of Gurobi’s optimum when customer nodes exceed 15, outperforming Large Neighborhood Search (LNS) and LNS with Simulated Annealing (LNSSA) with stable, shorter runtime. For large-scale instances, HHCRQL reduces gaps by up to 9.17% versus Iterated Local Search (ILS), 6.74% versus LNS, and 5.95% versus LNSSA, while maintaining relatively stable runtime. Real-world validation using Shanghai logistics data reduces waiting times by 35.36% and total transportation times by 24.68%, confirming HHCRQL’s effectiveness, robustness, and scalability. Full article

Bayesian networks (BNs) are effective and universal tools for addressing uncertain knowledge. BN learning includes structure learning and parameter learning, and structure learning is its core. The topology of a BN can be determined by expert domain knowledge or obtained through data analysis. However, when many variables exist in a BN, relying only on expert knowledge is difficult and infeasible. Therefore, the current research focus is to build a BN via data analysis. However, current data learning methods have certain limitations. In this work, we consider a combination of expert knowledge and data learning methods. In our algorithm, the hard constraints are derived from highly reliable expert knowledge, and some conditional independent information is mined by feature selection as a soft constraint. These structural constraints are reasonably integrated into an exponential Monte Carlo with counter (EMCQ) hyper-heuristic algorithm. A comprehensive experimental study demonstrates that our proposed method exhibits more robustness and accuracy compared to alternative algorithms. Full article

We examine how site-based path planning algorithms for enclosed spaces can be enhanced with situational detail. Addressing this question has led to value propositions in facility design, where there is often a call to match, map, and merge infrastructure considerations and configurations with potential implications for individual, group, and crowd flow through enclosed spaces. Responding to this question also invokes computational propositions, as facility design software is often computationally conservative with few resources devoted to simulation. We show that situational factors—the peculiarities and momentarily fleeting shifts in an individualized context that embody people in their movement through spaces—can be embedded into traditional, computationally lean path planning heuristics in ways that are actionable in widely used facility design software. We achieve this with algorithmic expansion of well-known planning algorithms using node-based architectures that permit the inclusion detail if, when, and where needed in a hyper-localized situational context that nests within site considerations. We demonstrate a proof of concept for use in the popular Unity 3D modeling platform, showing that situationally sensitive path planning can be achieved during the simulation run time of prototypical design scenarios for enclosed spaces with moving individuals, groups, and crowds. Full article

Disaster logistics presents a highly complex decision-making challenge under conditions of uncertainty, where the timely and efficient allocation of scarce resources is essential to minimize human suffering. In this context, we propose a novel Quantum-Inspired Hyperheuristic Framework (QHHF) designed to solve Dynamic Multi-Objective Combinatorial Optimization Problems (DMOCOPs) arising in disaster relief operations. The proposed framework integrates Quantum-Inspired Evolutionary Algorithms (QIEAs), which facilitate diverse and explorative solution generation, with a Reinforcement Learning (RL)-based hyperheuristic capable of dynamically selecting the most suitable low-level heuristic in response to evolving disaster conditions. A dynamic multi-objective mathematical model is formulated to simultaneously minimize total travel cost and risk exposure, while maximizing priority-weighted demand satisfaction. The model captures real-world complexity through time-dependent variables, stochastic demand variations, and fluctuating transportation risks. Extensive simulations using real-world disaster scenarios demonstrate the effectiveness of the proposed approach in generating high-quality solutions within stringent response time constraints. Comparative evaluations reveal that QHHF consistently outperforms traditional heuristics and metaheuristics in terms of adaptability, scalability, and solution quality across multiple objective trade-offs. Notably, our method achieves a 9.6% reduction in total travel cost, a 6.5% decrease in cumulative risk exposure, and a 4.7% increase in priority-weighted demand satisfaction when benchmarked against existing techniques. This work contributes both to the advancement of hyperheuristic theory and to the development of practical, AI-enabled decision-support tools for emergency logistics management. Full article

In complex environments, three-dimensional path planning for agricultural UAVs involves the comprehensive consideration of multiple factors, including obstacle avoidance, path optimization, and computational efficiency, which significantly complicates the achievement of safe and efficient flight. As environmental complexity increases, the search space expands exponentially, thereby making the problem more challenging to solve and categorizing it as an NP-hard problem. To obtain an optimal or near-optimal path within this vast search space, it is essential to balance the path length, safety, and computational cost. This paper proposes a novel UAV path planning method based on the Hybrid Multi-Strategy Dung Beetle Optimization Algorithm (HMSDBO), which effectively reduces path length and improves path smoothness. First, a new Latin hypercube sampling strategy is introduced to significantly enhance the population diversity and improve the global search capabilities. Furthermore, an innovative golden sine strategy is proposed to greatly enhance the algorithm’s robustness. Lastly, a new hybrid adaptive weighting strategy is employed to improve the algorithm’s stability and reliability. To validate the effectiveness of HMSDBO, this study compares its performance with that of the Adaptive Chaotic Gray Wolf Optimization Algorithm (ACGWO), Primitive Dung Beetle Optimization Algorithm (DBO), Whale Optimization Algorithm (WOA), Crayfish Optimization Algorithm (COA), and Hyper-Heuristic Whale Optimization Algorithm (HHWOA) in complex agricultural UAV environments. Experimental results show that the path lengths calculated by HMSDBO are reduced by 21.3%, 7.88%, 19.95%, 8.09%, and 4.2%, respectively, compared to the aforementioned algorithms. This reduction significantly enhances both the optimization effectiveness and the smoothness of three-dimensional path planning for agricultural UAVs. Full article

Hyper-heuristics emerged as a broader metaheuristic framework to address the limitations of traditional optimization heuristics. By abstracting the design of low-level heuristics, hyper-heuristics offer a flexible and adaptable approach to solving complex problems. This study conducts a bibliometric analysis of the hyper-heuristic-algorithms-related literature indexed in the Scopus database to map its evolution, identify key research trends, and pinpoint influential authors and journals. The study encompasses document growth over time, predominant author keywords, high-impact journals, and prolific authors ranked by publication count and citation impact. A detailed examination of author keywords unveils the core research themes within the hyper-heuristic domain. The findings of this study provide valuable insights into the current literature in hyper-heuristic research and offer guidance for novice and experienced researchers. Full article

This study addresses the optimization of the Vehicle Routing Problem (VRP) with prioritized customers by introducing and comparing two advanced solution approaches: a metaheuristic-based hyperheuristic framework and a Variational Autoencoder (VAE)-based hyperheuristic. The VRP with prioritized customers introduces additional complexity by requiring efficient routing while ensuring high-priority customers receive service within strict constraints. To tackle this challenge, the proposed metaheuristic-based hyperheuristic dynamically selects and adapts low-level heuristics using Simulated Annealing (SA) and Ant Colony Optimization (ACO), enhancing search efficiency and solution quality. In contrast, the VAE-based approach leverages deep learning to model historical routing patterns and autonomously generate new heuristics tailored to problem-specific characteristics. Through extensive computational experiments on benchmark VRP instances, our results reveal that both approaches significantly enhance routing efficiency, with the VAE-based method demonstrating superior generalization across varying problem structures. Specifically, the VAE-based approach reduces total travel costs by an average of $8\%$ and improves customer priority satisfaction by $95\%$ compared to traditional hyperheuristic methods. Moreover, a comparative analysis with recent state-of-the-art algorithms highlights the competitive performance of our approaches in balancing computational efficiency and solution quality. These findings underscore the potential of integrating metaheuristics with machine learning in complex routing problems and provide valuable insights for real-world logistics and transportation planning. Future research will explore the generalization of these methodologies to dynamic and large-scale routing scenarios. Full article

Traditional spacecraft task planning has relied on ground control centers issuing commands through ground-to-space communication systems; however, as the number of deep space exploration missions grows, the problem of ground-to-space communication delays has become significant, affecting the effectiveness of real-time command and control and increasing the risk of missed opportunities for scientific discovery. Adaptive Space Scientific Exploration requires that spacecraft have the ability to make autonomous decisions to complete known and unknown scientific exploration missions without ground control. Based on this requirement, this paper proposes an actor–critic-based hyper-heuristic autonomous mission planning algorithm, which is used for mission planning and execution at different levels to support spacecraft Adaptive Space Scientific Exploration in deep space environments. At the bottom level of the hyper-heuristic algorithm, this paper uses the particle swarm optimization algorithm, grey wolf optimization algorithm, differential evolution algorithm, and positive cosine optimization algorithm as the basic operators. At the high level, a reinforcement learning strategy based on the actor–critic model is used, combined with the network architecture, to construct a framework for the selection of advanced heuristic algorithms. The related experimental results show that the algorithm can meet the requirements of Adaptive Space Scientific Exploration, and exhibits a quality solution with higher comprehensive evaluation in the test. This study also designs an example application of the algorithm to a space engineering mission based on a collaborative sky and earth control system to demonstrate the usability of the algorithm. This study provides an autonomous mission planning method for spacecraft in the complex and ever-changing deep space environment, which supports the further construction of spacecraft autonomous capabilities and is of great significance for improving the exploration efficiency of deep space exploration missions. Full article

The U-shaped automated container terminal (U-ACT) meets the requirements of sea-rail intermodal transportation with its unique layout. However, this layout also presents challenges, such as complex container transshipment planning and challenging equipment scheduling, which limit further improvements in overall efficiency. This paper focuses on the integrated scheduling of horizontal transportation and container-handling equipment for container transshipment at U-ACT. To minimize operation time and energy consumption while addressing path conflicts among container trucks, we designed a two-layer scheduling model to generate an optimal scheduling scheme for each automated device. Given the complexity of the problem, we developed a reinforcement learning-driven hyper-heuristic algorithm (RLHA) capable of efficiently searching for near-optimal solutions. Small-scale experiments demonstrate that our RLHA outperforms other algorithms, improving optimization results by 5.14% to 28.87% when the number of container operation tasks reaches 100. Finally, large-scale experiments were conducted to analyze key factors impacting sea-rail intermodal transport operations at U-ACT, providing a foundation for practical optimization. Full article