检索结果-内蒙古大学图书馆

An intelligent hyperheuristic algorithm for the berth allocation and scheduling problem at marine container terminals

引用

TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2025年 198卷

作者： Li, Bokang Afkhami, Payam Khayamim, Razieh Borowska-Stefanska, Marta Wisniewski, Szymon Fathollahi-Fard, Amir M. Ozkul, Seckin Dulebenets, Maxim A. Florida A&M Univ Florida State Univ FAMU FSU Coll Engn Dept Civil & Environm Engn 2035 E Paul Dirac DrSliger BldgSuite 275 Tallahassee FL 32310 USA Univ Lodz Fac Geog Sci PL-90142 Lodz Poland Univ Lodz Inst Built Environm & Spatial Policy Fac Geog Sci Kopcinskiego St 31 PL-90142 Lodz Poland Univ Lodz Res Ctr European Spatial Policy & Local Dev Kopcinskiego St 31 PL-90142 Lodz Poland Macau Univ Sci & Technol Fac Innovat Engn Dept Engn Sci Taipa Macau Peoples R China Univ S Florida Muma Coll Business Sch Mkt & Innovat 4202 E Fowler AveBSN 3403 Tampa FL 33620 USA

Berth allocation and scheduling at marine container terminals holds critical significance for optimizing port operations and ensuring efficient maritime logistics, yet it is exceptionally challenging due to its operational complexity and practical constraints. This study presents a mixed-integer linear programming model for the dynamic discrete berth allocation and scheduling problem with the objective to minimize the total turnaround cost and develop an efficient service schedule for each incoming vessel with a feasible service order and berthing position. A novel Hyperheuristic Hybridized with Exact Optimization (HHEO) is developed to explicitly solve the challenging decision problem studied herein. The HHEO algorithm is designed to dynamically select and apply different genetic operators based on their actual performance. Furthermore, intelligent exact optimization procedures specific to the domain of berth allocation and scheduling are periodically employed within the HHEO framework to improve solution quality and facilitate search for high-quality solutions. Detailed computational experiments are carried out to prove the superiority of the HHEO algorithm against solution methods inspired by exact optimization along with some well-known metaheuristic algorithms, demonstrating its efficiency and applicability for real-life berth planning at marine container terminals. Moreover, the experiments clearly demonstrate that incorporating a hyperheuristic framework along with problemspecific hybridization methods is critical for improving the exploratory and exploitative capabilities of the developed HHEO algorithm. Last but not least, valuable practical insights are uncovered through the proposed approach, offering reliable solutions that can support effective port management under different scenarios of berthing availability, vessel arrival intensity, and delayed vessel departure penalties.

关键词： Maritime transportation Berth allocation and scheduling Optimization evolutionary computation Hyperheuristic algorithms Hybridization

来源：评论

学校读者我要写书评

暂无评论

A Two-Individual evolutionary Algorithm for Cumulative Capacitated Vehicle Routing With Single and Multiple Depots

引用

IEEE TRANSACTIONS ON evolutionary computation 2025年第2期29卷 505-518页

作者： Zou, Yuji Hao, Jin-Kao Wu, Qinghua Univ Angers Dept Comp Sci LERIA F-49045 Angers France Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China

The cumulative capacitated vehicle routing problem (CCVRP) with single or multiple depots (MDCCVRP) is a variant of the popular capacitated vehicle routing problem. Instead of minimizing the total travel time, the objective here is to minimize the sum of all customers' waiting times. This problem has a variety of real-world applications, especially in post-disaster humanitarian relief. To solve the challenging CCVRP and MDCCVRP, we propose a unified two-individual evolutionary algorithm that follows the memetic search framework. The algorithm integrates several key features: a two-individual population mechanism to accelerate convergence while maintaining population diversity, a dedicated edge assembly crossover to generate high-quality offspring, and an adaptive feasible and infeasible local search to achieve a balanced exploration between feasible and infeasible solutions. The algorithm is evaluated on 39 CCVRP instances and 78 MDCCVRP instances commonly used in the literature. computational results show that for the CCVRP, the algorithm outperforms the leading algorithms by achieving improved best results (new upper bound) for 13 instances and matching the best results for 23 other instances. For the MDCCVRP, the algorithm achieves nine improved best results and matches the best results for the remaining instances. The critical components of the algorithm are investigated to understand their contributions.

关键词： evolutionary computation Vehicle routing Statistics Sociology Routing Search problems Memetics Combinatorial optimization cumulative capacitated routing evolutionary algorithm heuristics vehicle routing

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive System to Support Decision Making in Highly Complex Project Portfolio Situations

引用

IEEE ACCESS 2025年 13卷 38115-38132页

作者： Solares, Efrain Fernandez, Eduardo Coello Coello, Carlos A. Segura Lozano, Xochitl Moreno-Cepeda, Reimundo Diaz, Raymundo Univ Autonoma Coahuila Fac Contaduria & Adm Torreon 27000 Mexico Ctr Invest & Estudios Avanzados IPN Departmento Comp Mexico City 07360 Mexico Univ Autonoma Coahuila Fac Ciencias Adm Saltillo 25225 Mexico Univ Autonoma Coahuila Fac Contaduria & Adm Saltillo 25280 Mexico Tecnol Monterrey Monterrey 64849 Mexico

This paper describes a new approach in project portfolio selection (PPS) problems, emphasizing the need to overcome traditional deficiencies with respect to multicriteria decision-making and multiobjective optimization. While existing methods typically allow the solving of partial aspects of the PPS problem, the proposed approach seeks to provide a holistic framework dealing with aspects like interdependence between projects, interaction among criteria, the incorporation of both cardinal and ordinal information, and a hierarchical multiobjective optimization. Unlike approaches that optimize portfolios neglecting superiority of some projects, or those that only assess individual projects without considering overall portfolio performance, the proposal allows for a compromise between both objectives. A case study is given, proving the application of the proposal for developing well-balanced portfolios aligned with strategic organizational goals and stakeholder preferences. The results point to significant improvements in the efficiency and effectiveness of decision-making, especially in complex project environments. This research contributes not only to the advancement of the theoretical framework of PPS but also to practical implications for portfolio management in a wide variety of organizational contexts.

关键词： Portfolios Proposals Optimization MCDM evolutionary computation Vectors Stakeholders Object recognition Costs Benchmark testing Decision making evolutionary algorithms multicriteria analysis multiobjective optimization outranking methods project portfolio selection

来源：评论

学校读者我要写书评

暂无评论

An efficient PSO-based evolutionary model for closed high-utility itemset mining

引用

APPLIED INTELLIGENCE 2025年第4期55卷 1-17页

作者： Carstensen, Simen Lin, Jerry Chun-Wei Univ Bergen Bergen Norway Western Norway Univ Appl Sci Bergen Norway

High-utility itemset mining (HUIM) is a widely adopted data mining technique for discovering valuable patterns in transactional databases. Although HUIM can provide useful knowledge in various types of data, it can be challenging to interpret the results when many patterns are found. To alleviate this, closed high-utility itemset mining (CHUIM) has been suggested, which provides users with a more concise and meaningful set of solutions. However, CHUIM is a computationally demanding task, and current approaches can require prolonged runtimes. This paper aims to solve this problem and proposes a meta-heuristic model based on particle swarm optimization (PSO) to discover CHUIs, called CHUI-PSO. Moreover, the algorithm incorporates several new strategies to reduce the computational cost associated with similar existing techniques. First, we introduce Extended TWU pruning (ETP), which aims to decrease the number of possible candidates to improve the discovery of solutions in large search spaces. Second, we propose two new utility upper bounds, used to estimate itemset utilities and bypass expensive candidate evaluations. Finally, to increase population diversity and prevent redundant computations, we suggest a structure called ExploredSet to maintain and utilize the evaluated candidates. Extensive experimental results show that CHUI-PSO outperforms the current state-of-the-art algorithms regarding execution time, accuracy, and convergence.

关键词： evolutionary computation Closed high-utility itemset Data mining Optimization Particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

Critical-Task-Driven Multi-Objective evolutionary Algorithm for Scheduling Large-Scale Workflows in Cloud Computing

引用

IEEE TRANSACTIONS ON EMERGING TOPICS IN computationAL INTELLIGENCE 2025年

作者： Liu, Xiaolu Yao, Feng Xing, Lining Chen, Huangke Zhao, Wei Zheng, Long Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Xidian Univ Key Lab Collaborat Intelligence Syst Minist Educ Xian 710071 Peoples R China

Simultaneously optimizing energy consumption and makespan is essential for deploying workflows on cloud platforms. Meanwhile, it poses various challenges, such as multiple types of decision variables and large-scale decision variables. Until now, some studies have adopted multi-objective evolutionary optimization techniques to handle these challenges. But, most relevant studies regard the optimization problem as a black box and do not fully explore the domain knowledge to strengthen the search efficiency. To further answer the above challenges, this study tailors a critical-task-driven evolutionary algorithm, namely CTMOEA, to optimize both energy consumption and makespan by evolving two types of decision variables: 1) mappings from tasks to resources;and 2) task runtime. In particular, an adaptive incentive mechanism is designed to distinguish decision variables with higher criticality and allocate more computing resources to evolve them, thus handling large-scale decision variables in a targeted manner. Moreover, a critical task remapping mechanism is designed to adaptively remap critical tasks onto the same resources as their successor tasks to eliminate data transfer time between them, thus pursuing a simultaneous reduction in energy consumption and makespan. At last, based on 16 real-world workflow traces, we validate the superior overall performance of the proposed CTMOEA by significantly outperforming five baselines on 13 test instances. Also, we conduct an ablation analysis to demonstrate the contributions of the three problem-specific mechanisms to overall performance.

关键词： Cloud computing Energy consumption Optimization Costs Scheduling algorithms Data centers Runtime Data transfer Data models computational modeling workflow scheduling evolutionary computation multi-objective optimization large-scale energy efficient

来源：评论

学校读者我要写书评

暂无评论

A cluster chaotic optimization for solving power loss and voltage profiles problems on electrical distribution networks

引用

KNOWLEDGE-BASED SYSTEMS 2025年 311卷

作者： Diaz, Primitivo Haro, Eduardo H. Avalos, Omar Perez, Nayeli Univ Guadalajara CUCEI Dept Elect Av Revoluc 1500 Guadalajara Jal Mexico

The growing demand for electricity poses significant challenges in maintaining a reliable and efficient power supply. Optimal Capacitor Placement (OCP) in electrical engineering addresses this issue by strategically positioning capacitor banks within constrained Radial Distribution Networks (RDNs). Traditional optimization methods often struggle with this problem;alternative approaches, such as metaheuristic algorithms, present promising solutions. Despite advances in optimization techniques, challenges in achieving optimal solutions continue. To address these challenges, recent hybrid computational methods, such as the cluster chaotic optimization (CCO) algorithm, have emerged to enhance stability and robustness in finding optimal solutions. The effectiveness of the CCO algorithm lies in its combination of evolutionary computation (EC) and Machine Learning (ML) approaches. These approaches improve the search strategy by leveraging information extracted from the solution landscape, resulting in high performance in discovering optimal solutions. In this context, this work aims to utilize the strengths of the CCO algorithm to solve real-world challenges and evaluate its potential in addressing the OCP. The CCO algorithm was tested on three benchmark RDNs to assess its efficacy. Results were compared with those obtained from classical and recently developed methods and analyzed using non- parametric tests. The findings indicate that the CCO algorithm is competitive and robust in solving the OCP, outperforming similar strategies, and demonstrates its effectiveness in optimizing complex real-world problems in electrical engineering.

关键词： Optimal Capacitor Placement Cluster chaotic optimization Radial Distribution Networks evolutionary computation Metaheuristic algorithms Energy systems computational Intelligence

来源：评论

学校读者我要写书评

暂无评论

A Multi-Objective evolutionary Algorithm Based on Bilayered Decomposition for Constrained Multi-Objective Optimization

引用

IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2025年第2期20卷 244-262页

作者： Yasuda, Yusuke Kumagai, Wataru Tamura, Kenichi Yasuda, Keiichiro Tokyo Metropolitan Univ Dept Elect Engn & Comp Sci 6-6 Asahigaoka Hino Tokyo 1910065 Japan Yokogawa Elect Corp Innovat Ctr 2-9-32 Naka Cho Musashino Tokyo 1800006 Japan

This paper proposes a multi-objective evolutionary algorithm based on bilayered decomposition (MOEA/BLD) for solving constrained multi-objective optimization problems. MOEA/D is an effective method for solving unconstrained multi-objective optimization problems. It decomposes the objective space using weight vectors and simultaneously searches for solutions for the subproblems. However, real-world applications impose many constraints, and these constraints must be handled appropriately when searching for good feasible solutions. The proposed MOEA/BLD treats such constraints as an additional objective function. Furthermore, in addition to the conventional weight vector, an augmented weight vector is introduced that decomposes the objective space and constraint violation space hierarchically. In the first stage, the objective space is decomposed by conventional weight vectors. In the next stage, the bi-objective space consisting of the scalarizing function and constraint violation is decomposed by augmented weight vectors. The augmented weights are adjusted so that they decrease linearly in the search process as the search gradually moves from infeasible regions to feasible regions. The proposed algorithm is compared to several state-of-the-art constrained MOEA/Ds using multi- and many-objective problems. The results show that the proposed method outperforms existing methods, in terms of search performance, under various conditions. (c) 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

关键词： multi-objective optimization metaheuristics evolutionary computation MOEA/D constraint handling decomposition

来源：评论

学校读者我要写书评

暂无评论

Integrating machine learning with dynamic multi-objective optimization for real-time decision-making

引用

INFORMATION SCIENCES 2025年 690卷

作者： Sarkar, Puja Khanapuri, Vivekanand B. Tiwari, Manoj Kumar Indian Inst Management Mumbai Mumbai India Indian Inst Technol Kharagpur Dept Ind & Syst Engn Kharagpur 721302 West Bengal India

Real-time decision-making in dynamic multi-objective optimization problems (DMOPs) is challenging due to constantly changing objectives and constraints. This paper integrates machine learning with Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve DMOPs and make real-time decisions. Learning-based methods have gained popularity for predicting solutions in new environments and capturing changing patterns in optimal solutions. However, existing approaches often struggle with training difficulty and reduced prediction accuracy due to irrelevant or redundant variables. Therefore, we introduce a new interdependent prediction (IDP) technique to identify correlations between variables and prediction targets and select significant variables for a predictive model. In this way, a better initial population is predicted. The IDP strategy is integrated within the dynamic NSGA-II, introducing a new algorithm called IDP-DNSGA-II. This integration facilitates rapid convergence, finding optimal or near-optimal solutions. The proposed method is evaluated against standard benchmarks, demonstrating superior performance in convergence speed and solution diversity with the changes in the problem environment. The IDP-DNSGA-II is validated through real-world optimization challenges in sustainable automobile production distribution in order-to-delivery systems to enhance environmental sustainability and operational efficiency. This study identifies the minimum frequency of change required in real- world problems to adequately track the optimal decision in real-time.

关键词： Machine learning Correlation Dynamic multi-objective optimization evolutionary computation

来源：评论

学校读者我要写书评

暂无评论

Collaborative optimization of manufacturing service allocation via multi-task transfer learning evolutionary approach

引用

JOURNAL OF INTELLIGENT MANUFACTURING 2025年第3期36卷 1761-1779页

作者： Zhou, Jiajun Gao, Liang Lu, Chao Yao, Xifan China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China South China Univ Technol Sch Mech & Automot Engn Guangzhou 510640 Peoples R China

Industrial internet platforms are regarded as an emerging fashion for the flexible integration of production resources located in multiple sites to complete complicated tasks submitted by users. Manufacturing Service Composition (MSC) is an essential technique that supports the optimal construction of value-added services. evolutionary computation approaches are frequently employed to resolve MSC issues and have achieved great accomplishments. These evolutionary solvers, however, require massive computational resources for objective evaluation and face cold-start problem. Recent advances in transfer learning field provide a new means for extracting knowledge across tasks to enhance the problem-solving efficiency. In light of above, this article is intended to devise a Multi-task Transfer evolutionary Search (MTES) approach for MSC considering the occurrence of multiple user requests, with which each corresponds to a MSC task, optimization experiences from constructing distinct MSC tasks can be learned to promote the search of arrival tasks at hand jointly. The MTES can adapt the helper task selection and the intensity of knowledge transfer from a synergistic perspective, such that it can be suitable for many-task scenario. Numerical studies on different scales of MSC tasks demonstrate that, compared to the prevalent peers, our proposed MTES needs far less computational resources to achieve the convergence performance and the solution quality is higher, which verifies the applicability and effectiveness of the proposed approach for dealing with MSC problem.

关键词： Industrial internet platform Manufacturing service collaboration evolutionary computation Knowledge transfer Multi-task learning

来源：评论

学校读者我要写书评

暂无评论

Architecture Knowledge Distillation for evolutionary Generative Adversarial Network

引用

INTERNATIONAL JOURNAL OF NEURAL SYSTEMS 2025年第4期35卷 2550013页

作者： Xue, Yu Lin, Yan Neri, Ferrante Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Peoples R China Univ Surrey Sch Comp Sci & Elect Engn NICE Res Grp Guildford GU2 7XS England

Generative Adversarial Networks (GANs) are effective for image generation, but their unstable training limits broader applications. Additionally, neural architecture search (NAS) for GANs with one-shot models often leads to insufficient subnet training, where subnets inherit weights from a supernet without proper optimization, further degrading performance. To address both issues, we propose Architecture Knowledge Distillation for evolutionary GAN (AKD-EGAN). AKD-EGAN operates in two stages. First, architecture knowledge distillation (AKD) is used during supernet training to efficiently optimize subnetworks and accelerate learning. Second, a multi-objective evolutionary algorithm (MOEA) searches for optimal subnet architectures, ensuring efficiency by considering multiple performance metrics. This approach, combined with a strategy for architecture inheritance, enhances GAN stability and image quality. Experiments show that AKD-EGAN surpasses state-of-the-art methods, achieving a Fr & eacute;chet Inception Distance (FID) of 7.91 and an Inception Score (IS) of 8.97 on CIFAR-10, along with competitive results on STL-10 (FID: 20.32, IS: 10.06). Code and models will be available at https://***/njit-ly/AKD-EGAN.

关键词： Neural architecture search generative adversarial network evolutionary computation architecture knowledge distillation generative model

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：