Swarm and Evolutionary Computation最新文献_第4页

Economic optimization scheduling of combined cooling, heat, and power systems utilizing a multi-strategy enhanced PSO algorithm 利用多策略增强型粒子群算法的冷、热、电联合系统经济优化调度

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

Swarm and Evolutionary Computation

Pub Date : 2026-01-01 DOI: 10.1016/j.swevo.2025.102278

Beichen Chen , Haoyuan Lv , Wenpeng Hong , Qingyu Su , Jian Li

Under the dual carbon goals, enhancing the energy efficiency of combined cooling, heating, and power (CCHP) systems while reducing carbon emissions is crucial. As the physical carrier of the energy internet, CCHP systems deliver comprehensive benefits, including energy savings, environmental improvement, and enhanced power supply reliability. This paper establishes a CCHP system model based on the principle of energy gradient utilization. Under time-of-use electricity pricing, the model comprehensively considers electricity consumption, heating, and cooling demands. Subsequently, a hybrid algorithm BC-GWOPSO is proposed, combining an improved Gray Wolf Optimization (GWO) algorithm with Particle Swarm Optimization (PSO). The specific improvement strategy involves using the

β

- distribution strategy to adjust the inertia weight in PSO and applying the cosine law to modify the convergence factor in GWO. For typical summer and winter days, total cost is adopted as the objective function for optimization scheduling, enabling more rational power allocation among units within the CCHP system and minimizing system costs. Finally, the BC-GWOPSO algorithm was experimentally compared with four other optimization algorithms. Friedman and Wilcoxon test results show that BC-GWOPSO algorithm is superior to the other four algorithms. The CCHP system optimization operation results demonstrate that the proposed method effectively reduces total operating costs, environmental costs, and load loss costs. Compared to other algorithms, it exhibits faster convergence speed and better stability, providing an effective scheduling solution for CCHP systems.

在双碳目标下，提高冷热电联产系统的能源效率，同时减少碳排放至关重要。作为能源互联网的物理载体，CCHP系统具有节约能源、改善环境和提高供电可靠性等综合效益。本文建立了基于能量梯度利用原理的热电联产系统模型。在分时电价下，该模型综合考虑了用电量、供热需求和制冷需求。随后，将改进的灰狼优化（GWO）算法与粒子群优化（PSO）算法相结合，提出了BC-GWOPSO混合算法。具体改进策略包括利用β-分布策略调整粒子群中的惯性权值，利用余弦定律修正粒子群中的收敛因子。对于典型的夏天和冬季，采用总成本作为优化调度的目标函数，使热电联产系统内各机组之间的功率分配更加合理，使系统成本最小化。最后，将BC-GWOPSO算法与其他四种优化算法进行了实验比较。Friedman和Wilcoxon测试结果表明，BC-GWOPSO算法优于其他四种算法。热电联产系统优化运行结果表明，该方法有效降低了总运行成本、环境成本和负荷损失成本。与其他算法相比，该算法具有更快的收敛速度和更好的稳定性，为热电联产系统提供了有效的调度解决方案。

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引用次数: 0

Surrogate-assisted evolutionary optimization using on-demand helper task for high-dimensional expensive multi-objective problems 基于按需助手任务的高维昂贵多目标问题代理辅助进化优化

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

Swarm and Evolutionary Computation

Pub Date : 2026-01-01 DOI: 10.1016/j.swevo.2025.102273

Qian Li, Yuanchao Liu, Jianchang Liu

Surrogate-assisted evolutionary algorithms (SAEAs) have been recognized as well-suited for solving expensive multi-objective optimization problems (EMOPs). However, most existing SAEAs show promising performance on low-dimensional expensive multi-objective optimization, and rarely pay attention to solving high-dimensional EMOPs. Thus, this work proposes an SAEA using on-demand helper task, termed SAEA-DHT, to efficiently address high-dimensional EMOPs. In SAEA-DHT, an on-demand helper task construction mechanism is proposed to create a low-dimensional helper task tailored to the optimization stage. During the convergence urgent stage, the helper task targets convergence-critical decision variables, whereas in the diversity-demand stage, it shifts the focus to the remaining decision variables. Furthermore, a cross-dimensional knowledge transfer approach is incorporated to efficiently transfer high-quality solutions discovered by the helper task to the original task, thereby accelerating convergence towards the true Pareto front. Finally, an adaptive infill selection scheme is proposed. This scheme dynamically selects infill solutions for exact fitness evaluations based on either convergence-driven or diversity-driven criteria. Extensive experiments are conducted on three well-known benchmarks and time-varying ratio error estimation problems containing up to 200 decision variables. The experimental results demonstrate the advantages of SAEA-DHT over six state-of-the-art SAEAs in solving high-dimensional EMOPs.

代理辅助进化算法（saea）被认为非常适合解决昂贵的多目标优化问题（EMOPs）。然而，现有的多目标优化算法大多在低维昂贵的多目标优化问题上表现良好，很少关注高维emop问题的求解。因此，这项工作提出了一个SAEA使用按需助手任务，称为SAEA- dht，以有效地解决高维emop。在SAEA-DHT中，提出了一种按需辅助任务构建机制，以创建适合优化阶段的低维辅助任务。在收敛紧急阶段，辅助任务针对收敛关键决策变量，而在多样性需求阶段，它将重点转移到剩余决策变量。此外，采用跨维度的知识转移方法，将辅助任务发现的高质量解有效地转移到原始任务中，从而加速向真正的帕累托前沿的收敛。最后，提出了一种自适应填充选择方案。该方案基于收敛驱动或多样性驱动准则动态选择精确适应度评估的填充解。在包含多达200个决策变量的三个著名基准和时变比误差估计问题上进行了广泛的实验。实验结果表明，在求解高维emop时，SAEA-DHT算法优于6种最先进的saea算法。

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引用次数: 0

Hybrid resource allocation framework for large-scale multi-objective optimization 大规模多目标优化的混合资源配置框架

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

Swarm and Evolutionary Computation

Pub Date : 2026-01-01 DOI: 10.1016/j.swevo.2025.102272

Debao Chen , Xiaopin Qiu , Feng Zou , Fangzhen Ge , Yuhui Zheng , Zhenghua Xin

The resource allocation strategy has been well applied in improving the performance of optimization algorithms. However, most resource allocation methods focus on either the objective space or the decision space. Few reports have been published on establishing adaptive resource allocation model that considers the two spaces simultaneously. In this work, a hybrid resource allocation framework is developed to enhance the performance of large-scale multi-objective optimization algorithms and thus fully use the information of the two spaces to build a reasonable resource allocation model. In objective space, shift-based density estimation strategy is used to allocate the individuals to different groups. In decision space, the decision variables are grouped with Pearson correlation coefficient and k-means clustering. This method does not add significant external computational cost to the algorithm, and the new resource allocation model contains more information than single space resource allocation model does. The adaptability of the resource allocation model is enhanced further by designing an adaptive distribution method to determine the reference resources for the population at different evolution phases. The division speed of the population is adaptive modified, and the diversity and evolution time of individuals in the sub-populations are balanced. In addition, a hybrid environmental selection strategy is designed to balance the quality of offspring and computation cost. Three types of experiments on two benchmark functions and a practical experiment are conducted to prove the effectiveness of the new framework. Statistical results indicate that the new framework can enhance the mean inverted generational distance and hypervolume indicator of the algorithms.

资源分配策略在提高优化算法的性能方面得到了很好的应用。然而，大多数资源分配方法要么关注目标空间，要么关注决策空间。建立同时考虑这两个空间的适应性资源分配模型的报道很少。本文提出了一种混合资源分配框架，以提高大规模多目标优化算法的性能，从而充分利用两个空间的信息构建合理的资源分配模型。在客观空间中，采用基于位移的密度估计策略将个体分配到不同的组中。在决策空间中，使用Pearson相关系数和k-means聚类对决策变量进行分组。该方法不会给算法增加大量的外部计算成本，并且新资源分配模型比单一空间资源分配模型包含更多的信息。通过设计一种自适应分配方法来确定种群在不同进化阶段的参考资源，进一步增强了资源分配模型的适应性。对种群的分裂速度进行适应性修正，使亚种群中个体的多样性和进化时间达到平衡。此外，设计了一种混合环境选择策略，以平衡子代质量和计算成本。在两个基准函数上进行了三种类型的实验和一个实际实验，以证明新框架的有效性。统计结果表明，新框架可以提高算法的平均倒代距离和超容量指标。

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引用次数: 0

A parallel evolutionary approach for multi-objective bus transit network design in multi-modal transit systems integrating with timetable synchronization 考虑时刻表同步的多模式公交系统多目标公交线网设计并行演化方法

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

Swarm and Evolutionary Computation

Pub Date : 2026-01-01 DOI: 10.1016/j.swevo.2025.102264

Yindong Shen , Zhuang Qian , Wei Wang

To address the gap that bus transit network design (BTND) in multi-modal contexts (e.g., bus, rail, BRT, shared bikes) overlooks timetable integration, this study first formulates the Multi-objective BTND with Timetable Synchronization (MBTND-TS) problem and its corresponding mathematical model. Core objectives include achieving multi-modal coordination, minimizing passenger travel/transfer time and operating costs, and maximizing timetable synchronization. To solve this MBTND-TS problem, a Parallel Evolutionary Approach (PEA-MBTND-TS) is proposed. It partitions the main population into parallel subpopulations and designs a coordination mechanism based on shared elite routes, with hierarchical crossover/mutation operators to effectively guide parallel evolutionary search. Meanwhile, a timetable construction with maximum synchronization count is devised. Finally, a non-dominated ranking method based on weight vectors enhances solution convergence and population diversity. Experiments conducted on real-world scenarios demonstrate that PEA-MBTND-TS outperforms NSGA-II, NSGA-III and MOEA/D in solution diversity and convergence. Additionally, integrating multi-modal transit into BTND and synchronizing with timetables significantly reduces public transit costs for passengers, operators and city governments.

针对公交、轨道交通、快速公交、共享单车等多模式环境下公交网络设计忽视时间表整合的不足，本文首先提出了具有时间表同步的多目标公交网络设计问题（MBTND-TS）及其相应的数学模型。核心目标包括实现多模式协调，最大限度地减少乘客旅行/转移时间和运营成本，并最大限度地实现时间表同步。为了解决MBTND-TS问题，提出了一种并行进化方法（PEA-MBTND-TS）。将主种群划分为并行亚种群，设计了基于共享精英路径的协调机制，并采用分层交叉/变异算子有效地指导并行进化搜索。同时，设计了具有最大同步次数的时间表结构。最后，提出了一种基于权向量的非支配排序方法，提高了算法的收敛性和种群多样性。实际场景实验表明，PEA-MBTND-TS在解决方案多样性和收敛性方面优于NSGA-II、NSGA-III和MOEA/D。此外，将多式联运纳入BTND并与时间表同步，大大降低了乘客、运营商和市政府的公共交通成本。

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引用次数: 0

A novel embedded dual-layer multi-objective evolutionary algorithm for multimodal emergency logistics delivery under demand uncertainty and supply shortage 需求不确定和供应短缺条件下多式联运应急物流配送的嵌入式双层多目标进化算法

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

Swarm and Evolutionary Computation

Pub Date : 2026-01-01 DOI: 10.1016/j.swevo.2025.102279

Hongtao Lei, Chunxian Guo , Jianmai Shi, Cheng Zhu, Weiming Zhang

Natural disasters are becoming increasingly frequent and severe worldwide nowadays, highlighting the urgency of building an efficient emergency logistics system. This paper focuses on the issue of multimodal emergency relief transportation under the conditions of material shortage and uncertain demand. We propose a novel two-stage robust optimization framework that simultaneously minimizes transportation time and cost while incorporating a priority ranking mechanism for disaster-affected areas based on urgency. The model is built following a unique "pre-planning and post-adjustment" mechanism: it establishes a robust multimodal emergency transportation solution based on worst-case scenarios in the first stage, then in the second stage it adjusts resource allocation as disaster information is updated. To address this problem, we propose an embedded dual-layer solution methodology, employing a Reinforcement Learning-based Customized NSGA-II (RL-CNSGA-II) algorithm for outer-layer route optimization and combining with a genetic algorithm for inner-layer scenario-specific resource allocation. Comprehensive experiments demonstrate that the model applied in this paper, can effectively address scenarios involving varying demand fluctuations and resource shortages. The proposed algorithm performs route planning and resource allocation efficiently with different dataset scales and parameter settings, and outperforms the compared SPEA-II and NSGA-II algorithms.

当今世界自然灾害日益频繁和严重，建立高效的应急物流体系迫在眉睫。本文主要研究物资短缺和需求不确定条件下的多式联运应急救援运输问题。我们提出了一种新的两阶段鲁棒优化框架，该框架可以最大限度地减少运输时间和成本，同时结合基于紧急程度的受灾地区优先级排序机制。该模型采用独特的“事前规划后调整”机制：第一阶段基于最坏情况建立稳健的多式联运应急运输解决方案，第二阶段根据灾害信息更新调整资源配置。为了解决这一问题，我们提出了一种嵌入式双层解决方法，采用基于强化学习的定制NSGA-II （RL-CNSGA-II）算法进行外层路由优化，并结合遗传算法进行内层特定场景的资源分配。综合实验表明，本文所采用的模型能够有效地解决需求波动和资源短缺的情况。该算法在不同的数据集规模和参数设置下都能有效地进行路由规划和资源分配，优于所比较的SPEA-II和NSGA-II算法。

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引用次数: 0

A dual-task-assisted self-organizing map evolutionary algorithm for constraining multimodal multi-objective optimization 约束多模态多目标优化的双任务辅助自组织映射进化算法

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

Swarm and Evolutionary Computation

Pub Date : 2026-01-01 DOI: 10.1016/j.swevo.2025.102276

Qianlin Ye , Zongda Wu , Keli Hu , Huawen Liu , Guoqing Li , Wanliang Wang

This paper proposes a novel dual-task-assisted self-organizing multi-objective evolutionary algorithm (DSCMMOEA) for constrained multimodal multi-objective optimization problems (CMMOPs). The algorithm establishes a collaborative dual-task framework. The main task employs a self-organizing map (SOM) network to learn the topological structures of the Pareto front in both decision and objective spaces. This enables efficient exploration of discrete feasible regions. The auxiliary task, disregarding constraints, focuses on mining potentially high-quality infeasible solutions to guide the population across infeasible regions. A dynamic offspring sharing mechanism facilitates knowledge transfer between these tasks, enhancing global search capability. Furthermore, this paper designs a dynamic environment selection mechanism that adaptively adjusts weight vectors to balance diversity and convergence. An adaptive pruning strategy is also introduced to coordinate diversity in both decision and objective spaces by sequentially eliminating solutions using dual-space crowding distances. Experimental results on the CMMOP and CMOP benchmark suites, along with real-world engineering applications, confirm that DSCMMOEA provides an effective and innovative solution for complex CMMOPs.

针对约束多模态多目标优化问题，提出了一种新的双任务辅助自组织多目标进化算法（DSCMMOEA）。该算法建立了一个协作的双任务框架。主要任务采用自组织映射（SOM）网络学习决策空间和目标空间中Pareto前沿的拓扑结构。这使得离散可行区域的有效探索成为可能。辅助任务不考虑约束条件，专注于挖掘潜在的高质量不可行解决方案，以引导人口跨越不可行区域。动态的子代共享机制促进了任务间的知识传递，增强了全局搜索能力。在此基础上，设计了一种动态环境选择机制，自适应调整权重向量，平衡多样性和收敛性。引入了一种自适应剪枝策略，通过利用双空间拥挤距离顺序消除解来协调决策空间和目标空间中的多样性。在CMMOP和CMOP基准套件上的实验结果以及实际工程应用证实，DSCMMOEA为复杂的CMMOP提供了有效和创新的解决方案。

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引用次数: 0

Robust optimization framework for the electric vehicle location-routing problem with split deliveries and incompatible loading 分货不相容电动汽车定位路径问题的鲁棒优化框架

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

Swarm and Evolutionary Computation

Pub Date : 2026-01-01 DOI: 10.1016/j.swevo.2025.102270

Zhihong Huang, Yixiao Lou, Yi Wang, Fang Guo, Mengzhao Dong

Electric freight vehicles (EFV) are key innovations in efforts to reduce pollutant emissions. The extended location-routing problem with incompatible loading constraints and split delivery by order (ELRPILC-SDO) was evaluated for EFVs in the present study. Routing scenarios that allow divisible shipments and classical location-routing problems were targeted. First, an integer programming model was formulated to maximize the operational efficiency of the EFV by minimizing operating costs. Subsequently, a robust optimization layer that partitions delivery cost uncertainty into multiple ranges and controls deviations was added. Afterward, heuristics that combine iterative greedy algorithms with a mechanism for dynamically evaluating extensive solution spaces were developed. The performance of these methodologies was validated through comprehensive numerical experiments. Finally, the impact of delivery costs and splitting delivery orders on the overall service strategy was analyzed; the algorithm’s ability to solve the ELRPILC-SDO in a real-world distribution network in Zhongyuan District, Zhengzhou, China, was validated; and the influence of EFVs on the economy and emission reductions in a practical logistics distribution network was discussed. The experimental results indicate that (1) the developed methodologies require 0.046% of the runtime of a standard exact algorithm while they improve the solution quality by 1.40%, and (2) allowing order splitting reduces the total costs by up to 6.14% and the routing costs by up to 9.71%. These findings highlight that the demand split-by-order strategy offers flexible delivery options. Using EFVs as transportation vehicles helps reduce pollutant emissions, particularly as the battery range increases.

电动货运车辆（EFV）是减少污染物排放的关键创新。本文研究了具有不兼容装载约束和订单分割配送的扩展位置-路由问题（ELRPILC-SDO）。目标是允许可分割的货物和经典的位置路由问题的路由场景。首先，建立了一个整数规划模型，通过最小化运行成本来最大化EFV的运行效率。在此基础上，增加了将交付成本不确定性划分为多个范围并控制偏差的鲁棒优化层。然后，将迭代贪婪算法与动态评估广泛解空间的机制相结合，提出了启发式算法。通过综合数值实验验证了这些方法的性能。最后，分析了配送成本和配送订单分割对整体服务策略的影响；在中国郑州中原区实际配电网中验证了该算法求解ELRPILC-SDO的能力；并在实际的物流配送网络中讨论了efv对经济和减排的影响。实验结果表明：(1)所开发方法的运行时间仅为标准精确算法的0.046%，而求解质量却提高了1.40%;(2)允许顺序分割可使总成本降低6.14%，路由成本降低9.71%。这些发现突出表明，按订单划分的需求策略提供了灵活的交付选择。使用电动汽车作为交通工具有助于减少污染物排放，特别是随着电池续航里程的增加。

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引用次数: 0

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

Swarm and Evolutionary Computation

Pub Date : 2026-01-01 DOI: 10.1016/j.swevo.2025.102269

Jiawei Chen , Sheng Qi , Feiran Wang , Lining Xing , Yingwu Chen

Due to the high-dimensional decision variables and the sparse nature of solutions in sparse large-scale multi-objective optimization problems (SLMOPs), traditional multi-objective evolutionary algorithms (MOEAs) encounter substantial challenges. Researchers have proposed various sparse evolutionary algorithms (SEAs) to address these challenges. However, most existing SEAs focus on accurately identifying non-zero variable positions while neglecting changes in objective function values during optimization. This paper introduces a Jaccard similarity coefficient-based evolutionary algorithm (JSCEA) designed to search for sparse distributions that optimize objective values rapidly and efficiently. Leveraging the Jaccard similarity coefficient (JSC) to identify critical sparse patterns among promising solutions and propagate them to subsequent generations enhances the algorithm’s computing efficiency, particularly under limited computational resources. Experimental results on three real-world problems and eight benchmark tests demonstrate that JSCEA performs competitively on problem sizes of up to 10,000 variables.

由于稀疏大规模多目标优化问题（SLMOPs）决策变量的高维性和解的稀疏性，传统的多目标进化算法（moea）面临着巨大的挑战。研究人员提出了各种稀疏进化算法（SEAs）来解决这些挑战。然而，现有的大多数SEAs侧重于准确识别非零变量位置，而忽略了优化过程中目标函数值的变化。本文介绍了一种基于Jaccard相似系数的进化算法（JSCEA），旨在快速有效地搜索稀疏分布以优化目标值。利用Jaccard相似性系数（JSC）在有希望的解决方案中识别关键稀疏模式并将其传播到后续代，可以提高算法的计算效率，特别是在计算资源有限的情况下。在三个实际问题和八个基准测试上的实验结果表明，JSCEA在多达10,000个变量的问题规模上具有竞争力。

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引用次数: 0

Enhanced LSHADE-SPACMA with fitness-directed selection pressure mutation and elastic archive for UAV trajectory planning and PV parameter identification 基于适应度定向选择压力突变和弹性存档的改进LSHADE-SPACMA无人机轨迹规划和PV参数识别

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

Swarm and Evolutionary Computation

Pub Date : 2025-12-27 DOI: 10.1016/j.swevo.2025.102257

Wenhao Mao , Shengwei Fu , Guozhang Zhang , Haisong Huang , Guangming Gong , Yulu Liu , Honghai Fu , Yinwei Li , Yongpeng Zhao , Langlang Zhang , Jiawei Wang

This paper presents a novel adaptive algorithm, ECLSHADE-SPACMA, designed to significantly improve the performance of its predecessor, LSHADE-SPACMA. The proposed algorithm incorporates four key innovations: First, it introduces a dynamic fitness-directed selection pressure mutation strategy to enhance the guidance of evolutionary direction. Second, it employs a nonlinear population reduction strategy based on an exponential function, enabling a smooth transition from global exploration to local convergence. Third, it proposes an archive pruning method based on elastic geometric weighting that effectively preserves high-quality solutions while maintaining population diversity. Finally, it establishes an adaptive probability-based local search mechanism guided by success rate feedback, thereby improving optimization efficiency in later stages. Experimental results show that the ECLSHADE-SPACMA algorithm not only significantly outperforms LSHADE-SPACMA on complex test problems but also surpasses many other high-performance optimizers. Especially when the problem dimension increases, its optimization performance advantage becomes more prominent. Further practical application evaluations demonstrate that ECLSHADE-SPACMA effectively overcomes the optimization challenges in unmanned aerial vehicle (UAV) trajectory planning within complex mountainous environments and achieves robust, efficient, and accurate performance in the parameter identification tasks of solar photovoltaic models.

本文提出了一种新的自适应算法ECLSHADE-SPACMA，其设计显著提高了其前任LSHADE-SPACMA的性能。该算法包含四个关键创新：首先，引入了动态适应度导向的选择压力突变策略，增强了进化方向的方向性；其次，采用基于指数函数的非线性种群减少策略，实现了从全局探索到局部收敛的平稳过渡；第三，提出了一种基于弹性几何加权的档案剪枝方法，在保持种群多样性的同时有效地保留了高质量的解。最后，建立了以成功率反馈为导向的基于概率的自适应局部搜索机制，提高了后期的优化效率。实验结果表明，ECLSHADE-SPACMA算法不仅在复杂的测试问题上明显优于LSHADE-SPACMA算法，而且优于其他许多高性能优化器。特别是当问题维度增加时，其优化性能优势更加突出。进一步的实际应用评估表明，ECLSHADE-SPACMA有效克服了复杂山地环境下无人机轨迹规划的优化挑战，在太阳能光伏模型参数识别任务中实现了鲁棒、高效、准确的性能。

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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 : 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