Swarm and Evolutionary Computation最新文献_第8页

A robust bi-objective optimization model and heuristic solution for truck-drone collaboration in humanitarian logistics under truck travel time uncertainty 货车行程时间不确定性下人道主义物流卡车-无人机协同的鲁棒双目标优化模型及启发式解

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation

Pub Date : 2026-02-01 Epub Date: 2026-02-09 DOI: 10.1016/j.swevo.2026.102309

Huizhen Zhang , Chaoyue Pan , Mitsuo Gen

This paper studies the humanitarian supply transportation problem in post-disaster scenarios under uncertainty, which employs a truck-drone collaborative delivery to efficiently meet relief demands. We focus on the vehicle routing problem with drones (VRPD), in which each truck carries multiple drones that can independently deliver supplies to several affected areas during a single sortie and return to the truck at designated retrieval points for recharging. Unlike drones, which operate with stable flight times, trucks are subject to uncertain travel times due to post-disaster disruptions. The goal is to minimize both total delivery time and overall travel cost. To solve this complex problem, we propose a bi-objective metaheuristic combining Adaptive Large Neighborhood Search (ALNS) with ϵ-dominance within a multi-objective simulated annealing framework (AMOSA). The performance of the proposed method was evaluated by comparison with NSGA-II, MOEA/D and SPEA2, a state-of-the-art multi-objective optimization algorithm. Experiments were based on real-world data from the Kartal district of Turkey. Results show that the multi-visit mode can effectively reduce drone routes compared to the single-visit mode, especially as truck capacity increases. The importance of considering uncertainty is demonstrated by analyzing the impact of key uncertain parameters on the resulting solutions and by performing out-of-sample testing. Furthermore, we investigate how varying the number of drones and their flight range influence transportation system performance.

本文研究了灾后不确定情景下的人道主义物资运输问题，采用卡车-无人机协同配送的方式高效满足救援需求。我们重点研究了无人机的车辆路线问题（VRPD），其中每辆卡车携带多架无人机，这些无人机可以在一次出击中独立地向几个受影响的地区运送物资，并在指定的检索点返回卡车进行充电。与飞行时间稳定的无人机不同，卡车由于灾后中断，飞行时间不确定。目标是最小化总交付时间和总运输成本。为了解决这一复杂问题，我们提出了一种结合自适应大邻域搜索（ALNS）和ϵ-dominance的双目标元启发式多目标模拟退火框架（AMOSA）。通过与NSGA-II、MOEA/D和SPEA2等多目标优化算法进行比较，评价了该方法的性能。实验基于来自土耳其Kartal地区的真实数据。结果表明，与单次访问模式相比，多次访问模式可以有效减少无人机路线，特别是当卡车容量增加时。通过分析关键不确定参数对最终解的影响和执行样本外测试，证明了考虑不确定性的重要性。此外，我们还研究了不同数量的无人机及其飞行范围对运输系统性能的影响。

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引用次数: 0

A decomposition-based constrained multi-objective optimization algorithm with dynamic resource reallocation guided by niche classification 基于小生境分类的资源动态再分配约束多目标优化算法

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation

Pub Date : 2026-02-01 Epub Date: 2026-02-09 DOI: 10.1016/j.swevo.2026.102321

Rui Yang , Minggang Dong , Wenzhang Liu

Decomposition-based constrained multi-objective evolutionary algorithms (CMOEAs) simplify complex optimization problems by decomposing them into multiple subproblems. These subproblems contribute unevenly to population optimization and demand varying computational resources across generations. However, most existing decomposition-based CMOEAs lack the prior knowledge for predetermining subproblem distributions. This leads to a suboptimal allocation of optimization weights and inflexible resource distribution, ultimately limiting their performance. To address this, we propose a niche classification strategy that identifies the distribution characteristics of local subproblems and categorizes them into distinct niches based on feasibility and dominance. This classification, updated each generation, provides dynamic prior knowledge, enabling adaptive allocation of optimization weights and computational resources tailored to each niche category. To operationalize this, we design a dual-population co-evolution framework based on decomposition, which dynamically redistributes resources among niches. Furthermore, we introduce an novel intergenerational fitness function to better assess the optimization potential of niches within the same category. By analyzing subpopulation changes across consecutive iterations, this function evaluates niche-level performance, thereby decoupling individual performance from fitness evaluation. Comprehensive experiments on 59 benchmark functions and a collaborative path planning task for multi-unmanned surface vehicles demonstrate that the proposed algorithm achieves competitive performance compared with seven state-of-the-art CMOEAs.

基于分解的约束多目标进化算法（cmoea）通过将复杂优化问题分解为多个子问题来简化复杂优化问题。这些子问题对种群优化的贡献不均匀，并且需要不同代的计算资源。然而，大多数现有的基于分解的cmoea缺乏预先确定子问题分布的先验知识。这将导致优化权重的次优分配和不灵活的资源分配，最终限制它们的性能。为了解决这个问题，我们提出了一种生态位分类策略，该策略识别局部子问题的分布特征，并根据可行性和优势度将其分类为不同的生态位。这种分类，每一代更新，提供动态先验知识，使优化权重和计算资源的自适应分配适合每个利基类别。为了实现这一目标，我们设计了一个基于分解的双种群协同进化框架，该框架在生态位之间动态地重新分配资源。此外，我们引入了一种新的代际适应度函数来更好地评估同一类别内生态位的优化潜力。通过分析连续迭代中亚种群的变化，该函数评估小生境级性能，从而将个体性能与适应度评估分离。在59个基准函数和多无人水面车辆协同路径规划任务上的综合实验表明，该算法与7种最先进的cmoea相比具有竞争力。

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引用次数: 0

Component-level knowledge transfer based on diffusion model in evolutionary multitasking 进化多任务中基于扩散模型的组件级知识转移

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation

Pub Date : 2026-02-01 Epub Date: 2026-02-02 DOI: 10.1016/j.swevo.2026.102301

Ruilin Wang, Xiang Feng, Huiqun Yu

Evolutionary Multitasking has proven effective in addressing multi-task optimization, with knowledge transfer playing a key role in improving algorithm performance. However, existing studies mainly emphasize the timing and methods of transfer, often constrained by specific task assumptions, while overlooking the potential of components during the process. Additionally, reliance on traditional stochastic evolutionary operators limits search efficiency. To address these limitations, this paper proposes a Diffusion-based Multifactorial Evolutionary Algorithm (D-MFEA), featuring a novel component-level knowledge transfer framework for unconstrained single-objective multi-task problems. This framework integrates a diffusion model as the transfer component, enabling efficient knowledge sharing and collaboration between evolutionary and transfer components. It demonstrates strong generalization, seamlessly adapting to and enhancing various MFEA algorithms. By generating high-quality individuals, the diffusion model facilitates positive transfer, reducing reliance on stochastic evolutionary operators and assumptions about task relationships, thereby significantly improving the efficiency of knowledge transfer. Theoretical analyses ensure the diffusion model’s ability to generate high-quality individuals, while experiments on multiple single-objective multi-task benchmarks and a real-world application demonstrate that D-MFEA achieves faster convergence. Ablation studies confirm the effectiveness and robustness of the framework’s components and analyze the impact of varying noise configurations. Extensive results show that our algorithm outperforms state-of-the-art methods.

进化多任务算法在解决多任务优化问题上已被证明是有效的，其中知识转移在提高算法性能方面起着关键作用。然而，现有的研究主要强调迁移的时间和方法，往往受到特定任务假设的限制，而忽视了迁移过程中组成部分的潜力。此外，对传统随机进化算子的依赖限制了搜索效率。为了解决这些问题，本文提出了一种基于扩散的多因子进化算法（D-MFEA），该算法为无约束单目标多任务问题提供了一种新的组件级知识转移框架。该框架集成了一个扩散模型作为转移组件，实现了进化组件和转移组件之间有效的知识共享和协作。它具有较强的泛化能力，能够无缝地适应和增强各种MFEA算法。扩散模型通过生成高质量的个体，促进正向迁移，减少对随机进化算子和任务关系假设的依赖，从而显著提高知识迁移效率。理论分析保证了扩散模型产生高质量个体的能力，而多个单目标多任务基准实验和实际应用表明，D-MFEA具有更快的收敛速度。消融研究证实了框架组件的有效性和鲁棒性，并分析了不同噪声配置的影响。广泛的结果表明，我们的算法优于最先进的方法。

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引用次数: 0

Deep reinforcement learning and heuristic-based dynamic switch migration for Low Earth Orbit satellite networks 基于深度强化学习和启发式的近地轨道卫星网络动态切换迁移

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation

Pub Date : 2026-02-01 Epub Date: 2026-01-31 DOI: 10.1016/j.swevo.2026.102307

Yong Deng , Feng Yao , Jianghan Zhu

The centralized control architecture and programmable features of Software Defined Networking (SDN) present significant opportunities for optimizing Low Earth Orbit (LEO) satellite network performance. Nevertheless, the time-varying topology and non-uniform user distribution characteristics of LEO satellite networks lead to controller load imbalance, which necessitates adaptive controller-switch mapping mechanisms to maintain optimal load distribution between controllers. Most existing migration strategies overlook the overall network performance, resulting in sub-optimal migration quality. Moreover, they fail to address the issue of isolated nodes during migration, which adversely affects network reliability and security. To address these issues, a mathematical optimization model is formulated with the objectives of minimizing latency and achieving controller load balancing, subject to constraints such as controller capacity and intra-domain switch connectivity. To solve this model, we propose a dynamic switch migration algorithm based on deep reinforcement learning and heuristic method (DSM-DH), which comprises two phases: control relationship optimization and connectivity restoration. In the first stage, the deep reinforcement learning (DRL) framework with a multi-neural network architecture is employed, incorporating a dynamic

ϵ

-greedy strategy and a prioritized experience replay mechanism to comprehensively optimize control relationships while satisfying controller capacity constraints. In the second stage, the heuristic approach is used to address the isolated nodes that arise during the migration process. Without violating the controller capacity constraints, isolated switches are prioritized for migration to the controller with the lowest load, so as to minimize the disturbance to the control relationships optimized in the first stage, thereby achieving full connectivity among switches within each domain. Finally, simulation experiments are conducted to compare the DSM-DH algorithm with existing benchmark algorithms across several key performance metrics, including latency and load balancing. The results demonstrate that the DSM-DH algorithm can effectively improve network performance.

软件定义网络（SDN）的集中控制体系结构和可编程特性为优化低地球轨道（LEO）卫星网络性能提供了重要机会。然而，低轨道卫星网络的时变拓扑和非均匀用户分布特性导致控制器负载不平衡，需要自适应控制器-开关映射机制来保持控制器之间的最优负载分配。大多数现有的迁移策略忽略了整体网络性能，导致迁移质量次优。而且无法解决迁移过程中节点隔离的问题，影响网络的可靠性和安全性。为了解决这些问题，在控制器容量和域内交换机连通性等约束条件下，制定了一个数学优化模型，以最小化延迟和实现控制器负载平衡为目标。为了解决这个模型，我们提出了一种基于深度强化学习和启发式方法的动态开关迁移算法（DSM-DH），该算法包括两个阶段：控制关系优化和连通性恢复。第一阶段，采用多神经网络架构的深度强化学习（DRL）框架，结合动态ϵ-greedy策略和优先体验重放机制，在满足控制器容量约束的前提下，对控制关系进行综合优化。在第二阶段，启发式方法用于处理迁移过程中出现的孤立节点。在不违反控制器容量约束的情况下，将隔离的交换机优先迁移到负载最低的控制器上，使对第一阶段优化的控制关系的干扰最小化，从而实现各域内交换机之间的完全连通性。最后，进行了仿真实验，将DSM-DH算法与现有基准算法在几个关键性能指标（包括延迟和负载平衡）上进行了比较。结果表明，DSM-DH算法可以有效地提高网络性能。

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引用次数: 0

A multi-objective evolutionary algorithm with clustering-based archiving and adaptive search mechanism 基于聚类归档和自适应搜索机制的多目标进化算法

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation

Pub Date : 2026-02-01 Epub Date: 2026-01-08 DOI: 10.1016/j.swevo.2025.102277

Yiting Zeng, Peng Shao, Shaoping Zhang

To address the challenges of unstable archiving mechanisms and the difficulty of balancing diversity and convergence in multi-objective evolutionary algorithms, this paper proposes a multi-objective evolutionary algorithm with clustering-based archiving and an adaptive search mechanism based on Harris Hawks optimization (MOCAS/HHO). Building on the framework of the Harris Hawks Optimization (HHO), MOCAS/HHO employs k-medoids clustering to update the archive, where representative solutions at the cluster centers are preserved to improve solution diversity. Subsequently, MOCAS/HHO identifies ‘valuable solutions’ from the archive to guide the population toward the correct search direction. Based on the proportion and saturation of the ‘valuable solutions’, a regulatory factor is introduced to perturb the escape energy E, enabling the algorithm to adaptively adjust its search direction. Moreover, leaders are randomly selected from the valuable solutions to enhance stability and the global search capability of MOCAS/HHO. For the performance evaluation, MOCAS/HHO is compared with 9 algorithms on 25 benchmark functions, using IGD and HV metrics and statistical analysis. MOCAS/HHO outperforms MOHHO on approximately 88 % of the selected 2–3 objective functions, while achieving superior performance on all chosen 4-objective high-dimensional functions. For the Car side impact design problem, MOCAS/HHO improves IGD by 24.3 % over MOEDO; for the Liquid-rocket single element injector design, it improves IGD by 65.95 % over MOGWO; and for Conceptual marine design, it ranks second in IGD to MOEA/D. Overall, these results indicate that MOCAS/HHO achieves a good balance between convergence and diversity across both benchmark test functions and practical engineering applications.

针对多目标进化算法中归档机制不稳定以及难以平衡多样性和收敛性的问题，提出了一种基于聚类归档和基于Harris Hawks优化的自适应搜索机制的多目标进化算法（MOCAS/HHO）。在Harris Hawks Optimization （HHO）框架的基础上，MOCAS/HHO采用k- medioids聚类更新存档，其中保留了集群中心的代表性解决方案，以提高解决方案的多样性。随后，MOCAS/HHO从档案中识别出“有价值的解决方案”，以引导人们朝着正确的搜索方向前进。根据“有价解”的比例和饱和度，引入调节因子扰动逃逸能E，使算法能够自适应调整搜索方向。此外，从有价值的解决方案中随机选择领导者，以增强MOCAS/HHO的稳定性和全局搜索能力。为了进行性能评价，利用IGD和HV指标和统计分析，在25个基准函数上比较了MOCAS/HHO与9种算法的性能。在所选的2-3个目标函数中，MOCAS/HHO在约88%的目标函数上优于MOHHO，而在所有所选的4-目标高维函数上均取得了优异的性能。对于汽车侧面碰撞设计问题，moas /HHO比MOEDO提高了24.3%的IGD；对于液体火箭单元件喷射器设计，IGD比MOGWO提高65.95%；船舶概念设计IGD排名第二，仅次于MOEA/D。总体而言，这些结果表明，在基准测试功能和实际工程应用中，MOCAS/HHO在收敛性和多样性之间取得了良好的平衡。

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引用次数: 0

Low-cost safe path planning and exit scheduling of multi-UAV aerial refueling based on swarm intelligence 基于群体智能的多无人机空中加油低成本安全路径规划与出口调度

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation

Pub Date : 2026-02-01 Epub Date: 2026-01-19 DOI: 10.1016/j.swevo.2026.102293

Bin Hang , Pengjun Guo

Aerial refueling technology is a crucial means of extending unmanned aerial vehicles (UAVs) mission duration and expanding operational range, garnering extensive attention. However, planning safe and cost-effective refueling routes for multiple UAVs in complex three-dimensional airspace, and achieving efficient and orderly egress after mission completion, still face technical challenges such as inadequate path safety and low egress scheduling efficiency. To address these challenges, this paper proposes a multi-agent hierarchical collaborative optimization framework that simulates group competition and cooperation to achieve task allocation and path coordination. By integrating factors such as path length, threat sources, air turbulence, altitude-dependent energy consumption, and turning loss, a multi-dimensional cost function is constructed, forming a comprehensive trajectory optimization model for UAV aerial refueling missions. Based on flight landing scheduling (FLS) theory, a dynamic time window allocation and conflict resolution mechanism is introduced, establishing a two-stage optimization architecture of ”path planning-safe egress.” Simulation results indicate that, compared to several mainstream meta-heuristic algorithms, the proposed method achieves superior path quality and higher scheduling efficiency under complex conditions, reliably accomplishing low-cost, coordinated multi-UAV refueling and safe egress operations.

空中加油技术是延长无人机任务时间、扩大作战范围的重要手段，受到了广泛关注。然而，在复杂的三维空域中规划安全、经济的多架无人机加油路线，并在任务完成后实现高效有序的出口，仍然面临路径安全性不足、出口调度效率低等技术挑战。为了解决这些问题，本文提出了一个模拟群体竞争与合作的多智能体分层协同优化框架，以实现任务分配和路径协调。通过整合路径长度、威胁源、空气湍流、高度相关能耗、转向损失等因素，构建多维代价函数，形成无人机空中加油任务综合轨迹优化模型。基于飞机着陆调度理论，引入了一种动态时间窗分配与冲突解决机制，建立了“路径规划-安全出口”两阶段优化体系结构。仿真结果表明，与几种主流的元启发式算法相比，该方法在复杂条件下具有更好的路径质量和更高的调度效率，可靠地完成了低成本、协同的多无人机加油和安全撤离操作。

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引用次数: 0

Synergistic Particle Swarm Optimized Bio-inspired Artificial Neural Network for Fractional Analysis of tumor-immune competitive system with multiple time delays 多时滞肿瘤免疫竞争系统分数分析的协同粒子群优化仿生神经网络

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation

Pub Date : 2026-02-01 Epub Date: 2026-01-20 DOI: 10.1016/j.swevo.2026.102284

Muhammad Wajahat Anjum , Noreen Sher Akbar , Muhammad Bilal Habib , Taseer Muhammad

This study introduces a novel application of machine learning based intelligent computing for developing bio-inspired neural networks and gradient backpropagation neural networks for tumor-immune interaction model. These models aim to solve nonlinear fractional cancer mathematical system described by four differential equations representing the population dynamics of cancer cells, macrophages, CD8+ T cells, and dendritic cells. Synthetic datasets were generated using the Adams-Bashforth predictor-corrector numerical method, with variations in the time delay and fractional order across each compartment. Both neural network models were trained on these datasets, divided into training and testing sets with an 80:20 ratios. Their performance was evaluated using metrics such as the R-squared score, mean squared error, and visual analyses including absolute error plots, error histograms, and loss curves. A total of six different optimizers were taken with three based on gradient based learning and three based on bio-inspired learning. The models were evaluated based on minimizing the mean squared error. The Bayesian Regularized Gradient based Neural Networks and Particle Swarm Optimized Bio-inspired Artificial Neural Network were found out to be the best performing models in the group of gradient-based and bio-inspired models respectively. However, the Particle Swarm Optimized Bio-inspired Artificial Neural Network demonstrated the highest efficiency, outperforming other gradient and bio-inspired algorithms according to statistical and graphical assessments.

本研究介绍了一种基于机器学习的智能计算的新应用，用于开发生物启发神经网络和梯度反向传播神经网络，用于肿瘤-免疫相互作用模型。这些模型旨在解决由癌细胞、巨噬细胞、CD8+ T细胞和树突状细胞的群体动力学的四个微分方程所描述的非线性分数癌症数学系统。使用Adams-Bashforth预测校正数值方法生成合成数据集，其中每个隔间的时间延迟和分数顺序有所不同。两种神经网络模型都在这些数据集上进行训练，以80:20的比例分为训练集和测试集。使用r平方分数、均方误差和视觉分析（包括绝对误差图、误差直方图和损失曲线）等指标对其性能进行评估。总共采用了6种不同的优化方法，其中3种基于梯度学习，3种基于仿生学习。基于均方误差最小化对模型进行评估。基于贝叶斯正则化梯度的神经网络和基于粒子群优化的仿生人工神经网络分别是基于梯度和仿生模型组中表现最好的模型。然而，根据统计和图形评估，粒子群优化的仿生人工神经网络显示出最高的效率，优于其他梯度和仿生算法。

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引用次数: 0

Real-time optimization of energy consumption and load balancing in server clusters based on model decomposition and a new differential evolution variant 基于模型分解和一种新的差分进化变体的服务器集群能耗与负载平衡实时优化

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation

Pub Date : 2026-01-01 Epub Date: 2025-12-23 DOI: 10.1016/j.swevo.2025.102230

Zhi Xiong , Ziyu Wang , Jianlong Xu , Fei Wang

Adjusting server deployment in a server cluster in real time with a changing workload to balance energy saving and load balancing is an urgent problem. Addressing the limitations of existing research in decision variable reduction and constraint handling, this work proposes an online real-time optimization strategy for energy consumption and load balancing of server clusters based on model decomposition and a new differential evolution (DE) variant. The optimization content includes the on/off state, CPU frequency, and workload allocation of each server. The decision variables are reasonably defined to derive the cluster energy consumption and load balancing models, and the cluster optimization is described as a bi-objective optimization model. Then, based on the model characteristics, the model is decomposed into two layers of optimization to reduce the difficulty of solving the problem, where the outer layer is a bi-objective nonlinear optimization problem and the inner layer is a single-objective mixed-integer linear programming problem. Finally, the inner-layer optimization is solved using the Gurobi optimizer, and the outer-layer optimization is solved using the non-dominated sorting genetic algorithm II and DE algorithm. To address the constraints existing in the outer-layer optimization, a new DE variant, DE/rand/1/while-if-either-or, is proposed. This variant can increase the feasible probability of mutant individuals and reduce the interference with the evolution mechanism, improving the population quality. Tests in various scenarios verify the real-time optimization capability of the proposed strategy and demonstrate the feasibility and effectiveness of the model decomposition and the new DE variant.

在服务器集群中，随着工作负载的变化，实时调整服务器部署，实现节能和负载均衡是一个迫切需要解决的问题。针对现有研究在决策变量约简和约束处理方面的局限性，本文提出了一种基于模型分解和一种新的差分进化（DE）变体的服务器集群能耗和负载平衡在线实时优化策略。优化内容包括每台服务器的开/关状态、CPU频率和工作负载分配。合理定义决策变量，推导出集群能耗和负载均衡模型，并将集群优化描述为双目标优化模型。然后，根据模型特点，将模型分解为两层优化，降低问题的求解难度，其中外层为双目标非线性优化问题，内层为单目标混合整数线性规划问题。最后，采用Gurobi优化器求解内层优化，采用非支配排序遗传算法II和DE算法求解外层优化。为了解决存在于外层优化中的约束，提出了一个新的DE变体DE/rand/1/while-if-非此即彼。这种变异可以增加突变个体的生存概率，减少对进化机制的干扰，提高种群质量。各种场景的测试验证了所提出策略的实时优化能力，并证明了模型分解和新的DE变体的可行性和有效性。

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引用次数: 0

Optimizing multi-period multi-mode multi-time window home health care scheduling with an improved tabu search algorithm 基于改进禁忌搜索算法的多周期多模式多时间窗口家庭医疗调度优化

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation

Pub Date : 2026-01-01 Epub Date: 2025-12-26 DOI: 10.1016/j.swevo.2025.102263

Yufeng Zhou, Zimei Pan, Yimeng Zhao, Zhiguo Li

With the rapid growth of online healthcare and increasing demand for personalized medical services, the traditional offline outpatient care model is increasingly unable to meet the diverse needs of various patient groups. To better align with patient preferences and improve care quality, this paper presents a multi-period home health care routing and scheduling problem (HHCRSP), in which patients can select among three service modes: outpatient, door-to-door, and online. The study addresses key challenges in real-world home healthcare delivery, including caregiver-patient matching, time window flexibility, and continuity of care. The objective is to optimize caregiver assignments and scheduling decisions across different service modes while minimizing total costs. We formulate the problem as a mixed-integer nonlinear programming model that captures multiple patient time windows and collaboration between online and offline services. To solve this complex problem efficiently, we propose an improved tabu search (ITS) algorithm. The ITS incorporates a dynamic tabu length mechanism, a novel swap-and-change operator for optimizing patients’ service dates, and a forward start interval algorithm for handling multiple time windows. Numerical experiments demonstrate that ITS outperforms the basic tabu search (TS), competitive simulated annealing (CSA), variable neighborhood search (VNS), and random general variable neighborhood search (RGVNS), achieving average improvements of 21.24 %, 12.28 %, 7.81 %, and 1.76 %, respectively, in solution quality. Sensitivity analyses further reveal that the setting of objective function cost parameters, caregiver-patient skill level deviations, and the number of caregiver workdays significantly impact scheduling performance. The research findings provide valuable decision-making support for healthcare staff scheduling.

随着在线医疗的快速发展和对个性化医疗服务需求的不断增加，传统的线下门诊模式越来越不能满足不同患者群体的多样化需求。为了更好地满足患者的需求，提高护理质量，本文提出了一种多时期家庭健康护理路径与调度问题（HHCRSP），患者可在门诊、上门和在线三种服务模式中进行选择。该研究解决了现实生活中家庭医疗服务的关键挑战，包括护理者与患者的匹配、时间窗口的灵活性和护理的连续性。目标是优化不同服务模式的护理人员分配和调度决策，同时最大限度地降低总成本。我们将该问题表述为一个混合整数非线性规划模型，该模型捕获了多个患者时间窗口以及在线和离线服务之间的协作。为了有效地解决这一复杂问题，我们提出了一种改进的禁忌搜索（ITS）算法。该系统采用了一种动态禁忌长度机制、一种用于优化患者服务日期的新型交换和更改算子，以及一种用于处理多个时间窗口的前向开始间隔算法。数值实验表明，ITS优于基本禁忌搜索（TS）、竞争模拟退火（CSA）、可变邻域搜索（VNS）和随机通用变量邻域搜索（RGVNS），求解质量平均分别提高21.24 %、12.28 %、7.81 %和1.76 %。敏感度分析进一步显示，目标函数成本参数的设定、护理者-患者技能水平偏差和护理人员工作日数显著影响调度绩效。研究结果为医护人员调度提供了有价值的决策支持。

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引用次数: 0

Multi-objective bilevel programming model for optimizing network interdiction deployment 网络拦截部署优化的多目标双层规划模型

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation

Pub Date : 2026-01-01 Epub Date: 2025-12-24 DOI: 10.1016/j.swevo.2025.102265

Wei-Chang Yeh , Chyh-Ming Lai , Tsung-Hua Wu

This study introduces a multi-objective bilevel programming model to address the ground force interdiction deployment problem, which is a hierarchical optimization framework where the defender strategically allocates resources to disrupt the attacker’s operational routes under resource constraints. At the upper level, the defender seeks to minimize interdiction costs while maximizing disruption to the attacker’s most reliable and shortest invasion paths. At the lower level, the attacker responds by minimizing the length of its invasion path and maximizing its reliability. To solve this problem, a novel nested multi-objective evolutionary algorithm, termed iNSSSO, is proposed. The algorithm integrates nondominated sorting simplified swarm optimization to optimize the defender's interdiction strategy at the upper level and Bi-Objective A* to solve the attacker’s bi-objective pathfinding problem at the lower level. To further improve solution quality and diversity, the algorithm incorporates dynamic reliability thresholding and min-cut search mechanisms. Experimental validation on 36 test instances demonstrates that iNSSSO consistently outperforms state-of-the-art algorithms, including MOPSDA, NSGA-II, SPEA2, NSGA-III, MOEA/D, and NSSSO, in terms of solution quality, diversity, and convergence. Furthermore, a practical analysis identifies critical network bottlenecks and frequently interdicted edges, offering valuable insights for resource allocation and defensive strategy planning in network interdiction scenarios.

针对地面部队拦截部署问题，提出了一种多目标双层规划模型，该模型是防御方在资源约束下战略性地分配资源以破坏攻击方作战路线的分层优化框架。在上层，防御者寻求最大限度地减少拦截成本，同时最大限度地破坏攻击者最可靠和最短的入侵路径。在较低的级别上，攻击者通过最小化其入侵路径的长度和最大化其可靠性来响应。为了解决这一问题，提出了一种新的嵌套多目标进化算法iNSSSO。该算法集成了非支配排序简化群算法，在上层优化防御者的拦截策略，在下层解决攻击者的双目标寻路问题。为了进一步提高解的质量和多样性，算法引入了动态可靠性阈值和最小切搜索机制。36个测试实例的实验验证表明，iNSSSO在解决质量、多样性和收敛性方面始终优于最先进的算法，包括MOPSDA、NSGA-II、SPEA2、NSGA-III、MOEA/D和NSSSO。此外，实际分析确定了关键的网络瓶颈和经常被拦截的边缘，为网络拦截场景中的资源分配和防御策略规划提供了有价值的见解。

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引用次数: 0