Multipoint Dynamic Aggregation (MPDA) represents a formidable NP-hard challenge in multi-robot systems, characterized by complex dynamic constraints and highly heterogeneous operational environments. To address these challenges, we propose the Bilevel Cooperative Ant Colony Optimization (BiCACO) framework, which features a synergistic dual-loop architecture. The outer optimization loop employs a Dynamic Ensemble Heuristic Process (DEHP) to evolve scenario-specific heuristic configurations through differential evolution guided by fitness feedback. Simultaneously, the inner construction loop leverages multiple ant colonies with temporal synchronization to generate constraint-aware solutions. Furthermore, an inter-generational Pheromone Synergy Mechanism (PSM) is introduced to facilitate elite knowledge inheritance by hybridizing historical-best pheromone patterns with maximum-entropy uniform distributions. This co-evolutionary process maintains an adaptive balance between exploration and exploitation while ensuring a cyclic exchange between heuristic guidance and solution fitness. Extensive evaluations across 50 benchmark instances demonstrate that BiCACO achieves statistical dominance in 66% of scenarios (33 out of 50) and establishes 28 new performance benchmarks. These findings underscore the framework’s capacity to significantly advance the state-of-the-art in multi-robot coordination, providing robust and scalable dispatching solutions for time-critical domains such as disaster response and logistics automation.

The Agile Earth Observation Satellite Scheduling Problem (AEOSSP) entails the selection and scheduling of observation tasks from a candidate list to maximize profits. While Adaptive Large Neighborhood Search (ALNS) is a prominent method for addressing this problem, its reliance on hand-crafted selectors for neighborhood transformation operators often results in inefficient weight updates, thereby limiting its overall performance. To address this issue, we propose a Reinforcement Learning Enhanced ALNS (RL-ALNS) for efficient AEOSSP resolution. RL-ALNS is a lightweight yet effective framework that dynamically prioritizes operators by considering scenario-specific demands and real-time performance during the search process. It integrates a Reinforcement Learning (RL)-enhanced controller to optimize operator selection and incorporates multiple problem-specific position operators (POs) into the ALNS framework to enable efficient task insertion. Experimental results demonstrate the superiority of RL-ALNS over the state-of-the-art Tabu-based ALNS Algorithm (ALNS/TPF). Specifically, RL-ALNS achieves a 2.3% higher profit rate on average and consistently outperforms ALNS/TPF in the early stages of the search process. Further experiments reveal that the RL-enhanced controller significantly improves operator selection efficiency, while the POs ensure stable and high-quality task insertion. These findings highlight the superior performance of RL-ALNS in tackling the AEOSSP, establishing it as a significant advancement over conventional methodologies in satellite scheduling optimization, while also demonstrating its potential for addressing complex, real-world scheduling challenges across diverse domains.

The Uncertain Agile Earth Observation Satellite Scheduling Problem (UAEOSSP) is a novel combinatorial optimization problem and a practical engineering challenge that aligns with the current demands of space technology development. It incorporates uncertainties in profit, resource consumption, and visibility, which may render pre-planned schedules suboptimal or even infeasible. Genetic Programming Hyper-Heuristic (GPHH) shows promise for evolving interpretable scheduling policies; however, their simulation-based evaluation incurs high computational costs. Moreover, the design of the constructive method, denoted as Online Scheduling Algorithm (OSA), directly affects fitness assessment, resulting in evaluation-dependent local optima within the policy space. To address these issues, this paper proposes a Hybrid Evaluation-based Genetic Programming (HE-GP) for effectively solving UAEOSSP. A Hybrid Evaluation (HE) mechanism is integrated into the policy-driven OSA, combining exact and approximate filtering modes: exact mode ensures evaluation accuracy through elaborately designed constraint verification modules, while approximate mode reduces computational overhead via simplified logic. HE-GP dynamically switches between evaluation models based on real-time evolutionary state information. Experiments on 16 simulated instance sets demonstrate that HE-GP significantly outperforms handcrafted heuristics and single-evaluation based GPHH, achieving substantial reductions in computational cost while maintaining excellent scheduling performance across diverse scenarios. Specifically, the average training time of HE-GP was reduced by 17.77\% compared to GP employing exclusively exact evaluation, while the optimal policy generated by HE-GP achieved the highest average ranks across all scenarios.

With persistent staring and wide-area coverage, GEO satellites are particularly suitable for ultra-large area monitoring that demands short revisit time. This study investigates their ultimate coverage capability under such conditions, expressed as the minimum achievable revisit time over a given ultra-large region. We formulate the problem as a combination of the Set Covering Problem (SCP) and the Traveling Salesman Problem (TSP), where regional discretization is applied to address field-of-view (FOV) deformation before unified modeling. To solve this challenge, we present a Dual-Population Evolutionary Algorithm with Adaptive Operator Selection (DPEA-AOS), which maintains two coevolving populations, full coverage and partial coverage, to preserve diversity and mitigate premature convergence. Experimental results demonstrate that DPEA-AOS delivers near optimal solutions under coarse-grid conditions and significantly outperforms state-of-the-art algorithms in fine-grid scenarios. Furthermore, ablation studies reveal that the regionally partitioned initialization strategy, dual-population strategy, and adaptive operator selection mechanism are key factors contributing to the algorithm’s enhanced performance. This study provides theoretical insights into the coverage limits of GEO satellite systems and practical guidance for ultra-large area dynamic monitoring, supporting constellation design and collaborative observation strategies.

The Set Orienteering Problem is a generalization of the popular Orienteering Problem, where customers are grouped into mutually exclusive clusters, and the profit of each cluster is collected by visiting at least one customer in the respective cluster. For this NP-hard problem, we develop a novel heuristic algorithm rooted in the memetic search framework. This algorithm incorporates a pre-processing phase to accelerate the search, an inherit-mutate-repair crossover operator for generating high-quality offspring solutions, and a local search that integrates strategic oscillation to adaptively explore both feasible and infeasible regions of the search space. Other innovative features of our method include a fast tri-individual population, a solution-based tabu strategy based on hashing techniques, and an adaptive mutation operator. Extensive experimental results on two sets of 496 large benchmark instances demonstrate the strong competitiveness of the proposed approach in comparison to state-of-the-art heuristics documented in the literature. Specifically, for the 192 large original benchmark instances, the proposed algorithm outperforms the current best-known solutions for 27 instances, while matching all other established results. For the 304 new larger benchmark instances, the proposed algorithm improves the best-known solutions for 138 instances. Further experiments are carried out to provide deeper insights into the performance of the algorithm by analyzing its primary elements.

Earth Observation Satellites (EOS) are critical for acquiring space-based information, supporting diverse applications from environmental monitoring to urban planning. The increasing demand for satellite imaging services necessitates efficient planning to handle complex and heterogeneous observation tasks. Traditional mission planning approaches, often relying on “single objective, single model, single algorithm” paradigms with meta-tasks and time windows, struggle to integrate these diverse requirements and face scalability issues with increasing task complexity. To address these limitations, this paper proposes a novel Grid Heat-driven Imaging Satellite Complex Task Planning Method. We introduce a Grid Heat-based Requirements Observation Model (GHROM) that unifies point, area, and moving targets into a single, time-variant geospatial heatmap, where grid cell “heat” represents observation priority. For this gridbased representation, we design a Satellite Discrete State Task Planning Model that transforms the problem into finding the highest heat gain path within a Directed Acyclic Graph (DAG) of discrete satellite states, where attitude transition constraints are embedded as graph edges. Based on this model, we develop a Grid Heat-Driven Dynamic Programming Algorithm (GMDPA). Extensive simulation experiments demonstrate the effectiveness and significant advantages of GMDPA, particularly in largescale scenarios, showcasing its superior performance in terms of solution quality and computational efficiency compared to traditional heuristic, metaheuristic, and genetic algorithms.

To address the time-consuming and complex multiple agile Earth observation satellite scheduling problem (multi-AEOSSP), we propose a novel application of the LLMassisted evolutionary computation (LLM-EC) framework to design task dispatching heuristics with three key innovations: 1) Customized prompt strategies for initialization, crossover, and mutation tailored for efficient order-dispatching heuristics. 2) The evolved heuristics decompose multi-AEOSSP into parallel single-satellite subproblems, significantly reducing solution complexity. 3) Experiments conducted on realistic scenarios demonstrate that LLM-EC achieves a higher solution quality than expert-designed heuristics, with improvements of up to 6.8%.

Advancements in remote sensing payload technology have led to an exponential increase in the volume of data generated. Traditional methods of data transmission, which rely on satellite overpasses for data relay back to Earth, are increasingly inefficient and limit the effectiveness of mission data retrieval. The evolution of inter-satellite links now allows for relayed data transfer between satellites, presenting a promising solution. However, challenges remain due to the compact size of satellites, the dynamic changes in network topology, and resource limitations. These factors necessitate the segmentation of large volumes of data into smaller packets for effective transmission. Addressing these issues and ensuring rapid and complete delivery of remote sensing data to ground stations is critical. This paper introduces a uniform fragmented strategy that divides remote sensing data into several equally sized sub-packets. Building upon this strategy, we have developed a Fuzzy Logic-based Q-routing algorithm designed specifically for large-volume data transmission from low-Earth orbit (LEO) satellites. This algorithm uses fuzzy logic evaluation to consider multiple states such as satellite energy levels, buffer capacity, and data integrity, optimizing decisions on the next hop for each data packet. Extensive simulation experiments were conducted to validate our approach, demonstrating that our proposed Q-routing algorithm outperforms the traditional Shortest Path algorithm across small, medium, and large-scale scenarios. The results confirm the efficacy of our proposed method.

The virtual machine placement (VMP) problem is a critical task in the field of cloud computing. The assignment of virtual machines to physical machines affects the quality of cloud services and running cost. Given a set of physical machines with certain capacities and a set of virtual machines with requirements, VMP aims to allocate each virtual machine to a capacity constrained physical machine in such a way that the total number of the physical machines used is minimized while their usage does not exceed the capacity. In this study, a cardinality constrained iterated local search algorithm is proposed to solve the VMP problem by transforming VMP into a sequence of cardinality-constrained problems, where each problem involves a fixed number k of physical machines. The algorithm uses the tabu search procedure for solution improvement, which exploits two new neighborhoods based on dedicated evaluation functions for neighboring solution selection. In addition, it uses a simple perturbation strategy to prevent the algorithm from search stagnation. Numerical results show that the proposed algorithm is highly competitive in both solution quality and computational efficiency, compared to several state-of-the-art algorithms on 18 subsets of 1800 widely used benchmark instances. Specifically, the algorithm reports the best results in terms of the average objective values on 17 out of 18 instance subsets with a short run time of 5 s. Importantly, using the lower bounds, it proves for the first time the optimality of solutions for 1390 instances. We study the impact of the key components of the algorithm on its performance.

In the face of a sudden public health emergency caused by a new infectious disease, it is necessary to establish a multi-level emergency medical facility (including primary and superior facilities) to address the surge in medical needs. In this context, traditional hospitals are responsible for patient screening, primary emergency medical facilities are responsible for treating mild cases, and superior emergency medical facilities are responsible for treating severe cases. Against the backdrop of uncertainties such as patient self-referral and the autonomous progression of the disease, we address an important problem of integrated emergency medical facility location and patient dispatching under uncertainty and propose a multi-stage stochastic programming model to formulate the problem. For a deterministic model under a given set of scenarios, a Decomposition-based Dual-level Heuristic (DDH) algorithm is proposed to efficiently solve the problem, where the upper level employs tabu search to optimize the location scheme, and the lower level utilizes a patient allocation heuristic to provide an optimized patient dispatching solution. Numerical experiments are conducted using Wuhan, China, the epicenter of the COVID-19 outbreak, as an example. The results show that the DDH algorithm achieves high quality solutions close to those obtained by state-of-the-art solver CPLEX but with significantly reduced computational overload. The DDH algorithm is also compared with the progressive hedging algorithm and genetic algorithm, showing its superior performance in terms of solution quality and computational efficiency. Through extensive data analysis, valuable conclusions and managerial insights are obtained, providing useful references for emergency response in similar public health emergencies in the future.

Advances in satellite miniaturisation have led to a steep rise in the number of Earth-observation platforms, turning the downlink of the resulting high-volume remote-sensing data into a critical bottleneck. Low-Earth-Orbit (LEO) communication constellations offer a high-throughput relay for these data, yet also introduce intricate scheduling requirements. We term the associated task the Remote Sensing Data Transmission in Communication Constellations (DTIC) problem, which comprises two sequential stages: inter-satellite routing, and satellite-to-ground delivery. This problem can be cast as a Hybrid Flow Shop Scheduling Problem (HFSP). Unlike the classical HFSP, every processor (e.g., ground antenna) in DTIC can simultaneously accommodate multiple jobs (data packets), subject to two-dimensional spatial constraints. This gives rise to a new variant that we call the Hybrid Flow Shop Problem with Two-Dimensional Processor Space (HFSP-2D). After an in-depth investigation of the characteristics of this HFSP-2D, we propose a constructive heuristic, denoted NEHedd-2D, and a Two-Stage Memetic Algorithm (TSMA) that integrates an Inter-Processor Job-Swapping (IPJS) operator and an Intra-Processor Job-Swapping (IAJS) operator. Computational experiments indicate that when TSMA is initialized with the solution produced by NEHedd-2D, the algorithm attains the optimal solutions for small-sized instances and consistently outperforms all benchmark algorithms across problems of every size.

The Satellite Range Scheduling Problem (SRSP) involves task types such as Telemetry, Tracking, and Command (TTC) and Data Transmission (DT), which are essential for ensuring satellite operation and mission completion. With the rapid expansion of Low Earth Orbit (LEO) constellations, the demand for TTC and DT, driven by massive mission requirement data, has increased significantly. However, existing methods lack scalability in handling large-scale and complex data and fail to address the integrated scheduling of TTC and DT tasks, rendering them inadequate for meeting the scheduling requirements of an integrated TTC and DT ground station network. To address this issue, this study develops a mixed-integer programming model that captures the scheduling relationship between TTC and DT tasks under shared resources and proposes a big-data-oriented intelligent scheduling method—Advantage Actor-Critic with Large Neighborhood Search (A2C-LNS). The method employs multi-policy networks to learn and optimize the destruction ratio, task selection, and repair strategies, driven by large-scale task and resource data. Experiments conducted on multiple large-scale simulation and data-driven scenarios demonstrate that A2C-LNS achieves superior performance in terms of mission profit and operational efficiency.

Wireless capsule endoscopy is a non-invasive and painless approach for diagnosing gastrointestinal diseases. An automated medical decision support system can significantly enhance clinician efficiency and reduce the incidence of misdiagnosis when analyzing lesions within the 50,000 to 100,000 image frames generated for each individual. However, only a small number of previous studies have focused on fine-grained recognition characteristics, and very few have simultaneously addressed both fine-grained recognition and class imbalance issues in wireless capsule endoscopy. To address these issues, we propose a novel medical decision support network that includes a prototype attention enhancement module and a dynamic curriculum learning approach. The prototype attention enhancement module improves lesion-sensitive compact representation learning by leveraging cosine similarity between the representation and a prototype memory with multi-class centers. The dynamic curriculum learning adopts triplet loss and weighted cross-entropy, facilitated by a progressive factor that controls between fine-grained representation learning and class-sensitive learning. Through extensive comparative experiments on three public datasets, the proposed network demonstrated competitive performance, outperforming previous methods with an F1-score of 96.6% on the 10-class Kvasir-Capsule dataset, an accuracy of 98.3% on the CAD-CAP dataset, and an accuracy of 93.7% on the mixed KID dataset. Two additional gastrointestinal histopathology datasets confirm the generalization of the proposed network. The code will be available at https://github.com/Xingcun-Li/MDSN-WCE Note to Practitioners-The primary aim of this study is to address diagnostic challenges in automated wireless capsule endoscopy (WCE). WCE provides a non-invasive diagnostic method for gastrointestinal diseases, but manual analysis of the resulting large volume of image data is time-consuming and error-prone. To this end, we propose a novel medical decision support network, which is also applicable to similar medical image analysis issues characterized by class imbalance and fine-grained recognition. Our approach integrates a prototype attention enhancement (PAE) module and dynamic curriculum learning (DCL), enabling the model to accurately identify lesions within WCE images and exhibit favorable performance in gastrointestinal histopathology image recognition beyond the WCE domain. Additionally, our approach can be applied to various common deep learning models without incurring significant additional computational costs, thereby aiding endoscopists in significantly reducing the time required for diagnosing WCE video frames. The code will be uploaded to GitHub for direct use in the automated diagnosis of WCE video frames. While our method shows promise, its main focus is on rapidly identifying a large number of frames within WCE videos. However, further work is needed to extract key frames and improve the interpretability of outputs from WCE videos, which is also a future research direction.

This paper investigates a novel problem, namely the Uncertain Agile Earth Observation Satellite Scheduling Problem (UAEOSSP). Unlike the static AEOSSP, it takes into account a range of uncertain factors (e.g., task profit, resource consumption, and task visibility) in order to reflect the reality that the actual information is inherently unknown beforehand. An effective Genetic Programming Hyper-Heuristic (GPHH) is designed to automate the generation of scheduling policies. The evolved scheduling policies can be utilized to adjust plans in real time and perform exceptionally well. Experimental results demonstrate that evolved scheduling policies significantly outperform both well-designed Look-Ahead Heuristics (LAHs) and Manually Designed Heuristics (MDHs). Specifically, the policies generated by GPHH achieve an average improvement of 5.03

For a simple undirected weighted graph G=(V,E,w,c), the weighted total domination problem is to find a total dominating set S with the minimum weight cost. A total dominating set S is a vertex subset satisfying that for each vertex in V there is at least one neighboring vertex in S. We propose a knowledge-based iterated local search algorithm for this problem that combines a reduction procedure to reduce the input graph, a learning-based initialization to generate high-quality initial solutions and a solution-based iterated local search to conduct intensive solution examination. Experiments on 342 benchmark instances show that the algorithm outperforms state-of-the-art algorithms. In particular, it reports 93 new upper bounds and 249 same results (including 165 known optimal results). The impact of each component of the algorithm is examined.

The capacitated electric vehicle routing problem (CEVRP) extends the traditional vehicle routing problem by simultaneously considering the service order of the customers and the recharging schedules of the vehicles. Due to its NP-hard nature, we decompose the original problem into two sub-problems: a capacitated vehicle routing problem (CVRP) and a fixed route vehicle charging problem (FRVCP). A highly effective threshold acceptance based multi-layer search (TAMLS) algorithm is proposed to quickly obtain high-quality solutions. TAMLS consists of three layers. An iterated thresholding search procedure and a thresholding selection procedure are employed to produce diversified CVRP solutions in the first layer and to screen out high quality ones in the second layer, respectively. In the third layer, a removal heuristic coupling with an enumeration method is adopted to solve FRVRP, which produces optimized charging schedules. Extensive computational results show that TAMLS outperforms the state-of-the-art algorithms in terms of both solution quality and computation time. In particular, it is able to obtain new best results for 11 out of 17 benchmark instances, and reach the best known results on the remaining 6 instances. Additional experimental analyses are performed to better understand the contributions of key algorithmic components.

The integration problem of production and transportation (IPPT) is one of the most important decision issues in real-life manufacturing and flow industries. To illustrate its potential for improving difficult production and transportation processes, we focus on a challenging case of IPPT, namely the distributed no-wait flow-shop and transportation integrated scheduling problem (DNWFSTISP) considering third party logistics (3PL), which occurs in many practical industries. DNWFSTISP consists of integrating the well-known distributed no-wait flow-shop scheduling and dedicated vehicle routing in the sense of 3PL while minimizing the total cost. First, we investigate the integration approach of the production and transportation of DNWFSTISP and present three problem-specific loading heuristics, which account for both a make-to-order strategy and 3PL providers to improve customer satisfaction. Second, we propose an active learning based hyper-heuristic algorithm (ALHHA) to solve the problem. ALHHA is unique in that it relies on an active learning based high-level exploration to discover promising search regions and a neighborhood search based low-level exploitation to intensively examine given search regions. Third, we assess the composing ingredients of the proposed ALHHA on 40 instances with up to 200 jobs and 7 factories to shed light on their impacts on the performance of the algorithm. Moreover, we make the 40 instances and solved upper bounds publicly available, which would be valuable for future research on DNWFSTISP.

With the current miniaturization and cost reduction trend, many small imaging satellites are deployed in low Earth orbit (LEO). Efficient scheduling of transmission links is crucial for handling the large volume of imaging data in the time-varying inter-satellite broadband communication networks. This ensures optimal utilization of inter-satellite link resources and gives rise to a novel problem known as the large-volume LEO satellite imaging data networked transmission scheduling problem (LLSIDNTSP). This problem requires the integrity of the imaging data received on the ground station. A Specific Time-Evolving Graph (STEG) model is introduced to formulate this problem, while a highly effective Sequential Two-Phased Heuristic Algorithm (STPHA) is proposed to solve it. STPHA determines an optimized contact plan (CP) with a contact-fast-construction algorithm (CFCA) in the first phase. With this CP as input, it produces an optimal transmission schedule with Linear Programming in the second phase. The CFCA incorporates a novel heuristic called Mission Residual Volume Factor(MRVF), which guides the selection of a proper inter-satellite and satellite-to-ground link at each construction step. Extensive simulation results demonstrate the effectiveness of the proposed STPHA. In particular, it can achieve the optimal solution for small-sized scenarios with one-hundredth of the computing time used by the exact solver (CPLEX). Also, it is consistently better than other heuristic algorithms adapted from the literature for large-sized scenarios. Additional experimental analysis is conducted to showcase the effectiveness of the innovative components of STPHA.

This article presents the large-scale Integrated Scheduling Problem of TTC and DDT with Idle Degree Requirements (IS-TTC&DDT-IDR), which involves efficiently allocating antenna resources and scheduling tasks for tracking, telemetry, and command (TTC) as well as digital data transmission (DDT) in satellite ground stations. The problem aims to optimize task completion while managing idle resource capacity. To tackle this challenge, a Multi-Stages Local Search (MSLS) algorithm is proposed. The MSLS algorithm is designed based on the problem’s unique characteristics and is structured in three stages: the first stage uses a Forcibly Insertion Procedure (FIP) to generate a high-quality initial solution for DDT tasks, the second stage also uses the Forced Insertion Procedure (FIP) to optimize the TTC task, and the third stage enhances idle capacity through an Exchanging Procedure (EP). To design the experiments, this paper firstly extends task scale in quasi-real scenarios to ten-thousands level within a multi-satellite system, while current studies conduct their experiments in maximum 1600 tasks. Extensive empirical results based on such scenarios demonstrate that the MSLS algorithm outperforms reference algorithms on optimization value, stability, and convergence.