Search Results (81)

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

This study introduces a novel train-and-test approach referred to as apprenticeship learning (AL) for generating selection hyper-heuristics to solve the Quadratic Unconstrained Binary Optimisation (QUBO) problem. The primary goal is to automate the design of hyper-heuristics by learning from a state-of-the-art expert and to evaluate whether the apprentice can outperform that expert. The proposed method collects detailed search trace data from the expert and trains the apprentice based on the machine learning models to predict heuristic selection and parameter settings. Multiple data filtering and class balancing techniques are explored to enhance model performance. The empirical results on unseen QUBO instances show that indeed, “student surpasses the teacher”; the hyper-heuristic with the generated heuristic selection not only outperforms the expert but also generalises quite well by solving unseen QUBO instances larger than the ones on which the apprentice was trained. These findings highlight the potential of AL to generalise expert behaviour and improve heuristic search performance. Full article

Hyper-heuristics, or simply heuristics to choose heuristics, represent a powerful approach to tackling complex optimization problems. These methods decide which heuristic to apply throughout the solving process, aiming to improve the solving process. While they have demonstrated significant success across various domains, their suitability for all problem instances, even within a specific domain, is not guaranteed. The literature provides many examples of successful hyper-heuristic models for packing problems. Among those models, we can mention rule-based and fixed-sequence-based hyper-heuristics. These two models have proven useful in various scenarios. This paper investigates a genetic-based approach that produces hybrid hyper-heuristics. Such hybrid hyper-heuristics combine rule-based decisions while firing heuristic sequences. The rationale behind this hybrid approach is that we aimed to combine the strengths of both approaches. Although we expected to improve on the individual performance of the methods, we obtained contradictory results that suggest that, at least in this work, combining the strengths of different hyper-heuristic models may not be a suitable approach. 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

This paper introduces a novel neuromorphic-inspired hyper-heuristic framework (NeuHH) for solving the Capacitated Single-Allocation p-Hub Location Routing Problem (CSAp-HLRP), a challenging combinatorial optimization problem that jointly addresses hub location decisions, capacity constraints, and vehicle routing. The proposed framework employs Spiking Neural Networks (SNNs) as the decision-making core, leveraging their temporal dynamics and spike-timing-dependent plasticity (STDP) to guide the real-time selection and adaptation of low-level heuristics. Unlike conventional learning-based hyper-heuristics, NeuHH provides biologically plausible, event-driven learning with improved scalability and interpretability. Experimental results on benchmark instances demonstrate that NeuHH outperforms classical metaheuristics, Lagrangian relaxation methods, and reinforcement learning-based hyper-heuristics. Specifically, NeuHH achieves superior performance in total cost minimization (up to 13.6% reduction), load balance improvement (achieving a load balance factor of as low as 1.04), and heuristic adaptability (reflected by higher heuristic switching frequency). These results highlight the framework’s potential for real-time and energy-efficient logistics optimization in large-scale dynamic networks. 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

One of the important problems of sustainable logistics is routing vehicles in a sustainable manner, the green vehicle routing problem, or vehicle routing problems which aim to reduce CO₂ emissions. In the literature research, it was seen that these problems were solved with heuristic, metaheuristic, or hyper-heuristic methods and hybrid approaches since they are in the NP-hard class. This work presents a parallel multi-process genetic algorithm that incorporates problem-specific genetic operators to minimize CO₂ emissions in the capacity-constrained vehicle routing problem. Unlike previous research, the algorithm combines parallel computing with tailored genetic operators in order to enhance the diversity of solutions and speed up convergence. Genetic algorithm models were developed to minimize total distance, CO₂ emissions, and both objectives simultaneously. Two genetic algorithm models were developed to minimize total distance and CO₂ emissions. Experimental results using the reference CVRP examples such as A-n32-k5 and B-n44-k7 show that the proposed approach reduces CO₂ emissions by 1.2% more than hybrid artificial bee colony optimization, 1.3% more than ant colony optimization, and 4% more than the traditional genetic algorithm. Experimental results using benchmark CVRP instances demonstrate that the proposed approach outperforms hybrid artificial bee colony optimization, ant colony optimization, and traditional genetic algorithms for most of the test cases. This is done by exploiting multi-core processors, and the parallel architecture has improved computational efficiency; the modules compare and update solutions against the global optimum. Results obtained show that prioritizing CO₂ emissions as the only objective yields better results compared to multi-objective models. This study makes two significant contributions to the literature: (1) it introduces a novel parallel genetic algorithm framework optimized for CO₂ emission reduction, and (2) it provides empirical evidence underscoring the advantages of emission-focused optimization in CVRP. Full article

Artificial intelligence plays an indispensable role in improving productivity and promoting social development, and causal discovery is one of the extremely important research directions in this field. Acyclic directed graphs (DAGs) are the most commonly used tool in causal modeling because of their excellent interpretability and structural properties. However, in the face of insufficient data, the accuracy and efficiency of DAGs learning are greatly reduced, resulting in a false perception of causality. As intuitive expert knowledge, structural constraints control DAG learning by limiting the causal relationship between variables, which is expected to solve the above-mentioned problem. However, it is often impossible to build a DAG by relying on expert knowledge alone. To solve this problem, we propose the use of expert knowledge as a hard constraint and the structural prior gained via data learning as a soft constraint. In this paper, we propose a fitness-rate-rank-based multiarmed bandit (FRRMAB) hyper-heuristic that integrates soft and hard constraints into the DAG learning process. For a linear structural equation model (SEM), soft constraints are obtained via partial correlation analysis. The experimental results on different networks show that the proposed method has higher scalability and accuracy. Full article

In this study, firstly, the balance between the exploration and exploitation capabilities of the weighted mean of vectors (INFO) algorithm was developed using the fitness–distance balance (FDB) method. Then, the FDB-INFO algorithm was developed with a hyper-heuristic method to create the beginning optimal population by using Linear Population Reduction Success History-based Adaptive Differential Evolution (LSHADE) and a novel Hyper-FDB-INFO algorithm was presented. Finally, the developed Hyper-FDB-INFO algorithm was applied to solve the optimal placement and sizing of FACTS devices for the optimal power flow (OPF) problem incorporating wind energy sources. Moreover, determining the placement and sizing of FACTS devices is an additional problem to minimize the total cost of generation and reducing the power losses of the power system. The experimental results showed that the Hyper-FDB-INFO algorithm is a more effective solver than the SHADE-SF, INFO, FDB-INFO and Hyper-INFO algorithms for wind power and FACTS devices integrating the OPF problem. Full article