Article
Economics
Amina El Yaagoubi, Aicha Ferjani, Yasmina Essaghir, Farrokh Sheikhahmadi, Mohamed Nezar Abourraja, Jaouad Boukachour, Marie-Laure Baron, Claude Duvallet, Ali Khodadad-Saryazdi
Summary: This paper investigates the obstacles limiting the growth of rail freight in Europe, focusing on the development of small multimodal terminals. Optimization of resource use and time efficiency is crucial for handling operations and final truck delivery. Through a case study of the Le Havre-Paris intermodal shuttle, the effectiveness of the optimization-simulation approach is demonstrated, providing comprehensive guidelines for similar terminals.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2022)
Article
Engineering, Marine
Yongsheng Yang, Sha He, Shu Sun
Summary: Reasonable scheduling of loading and unloading equipment for trains can reduce energy consumption in production operations, which is crucial for environmentally-friendly development of terminals. A collaborative scheduling model for Automated Rail Mounted Gantry (ARMG) and Automated Guided Vehicle (AGV) was proposed to minimize equipment energy consumption in a scenario involving vertical railway entry to a port and a shared storage yard. The model employed a two-layer scheduling rule and a self-adaptive chaos genetic algorithm (SCGA) to optimize the placement of ARMG and AGV. Simulation experiments confirmed the effectiveness of the model and algorithm. The study also analyzed the effects of delayed vessel arrival, transshipment container proportion, and the number of automated ARMGs and AGVs on total energy consumption. The results indicate that a 1:4 ratio of ARMG to AGV minimizes energy consumption when all containers are train-ship containers. Furthermore, as ship arrival time increases, reducing the number of AGVs can significantly decrease energy use while maintaining the same number of ARMGs.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Article
Operations Research & Management Science
Ginger Y. Ke
Summary: This study develops a scenario-based robust optimization model for rail-truck intermodal transportation network in the presence of disruptions. By proposing recovery mechanisms and additional constraints, considering different disruption scenarios, the study minimizes expected risk and variability to ensure network reliability. Numerical experiments and sensitivity analyses reveal the relationship between system robustness, reliability, and provide managerial insights for disruption management.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Economics
Ginger Y. Ke, Manish Verma
Summary: This study proposes a recovery framework for rail-truck intermodal terminals from random disruptions through optimization and regression analysis. The results indicate that implementing mitigation strategies can improve network resilience and significant cost savings by reducing the cost of recovery strategies. Additionally, designing a more balanced distribution of freight in intermodal train services can help reduce terminal criticality, suggesting the importance of having backup rental capacities for some terminals.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2021)
Article
Environmental Sciences
Yilin Chen, Bo Gao, Tao Lu, Hui Li, Yiqi Wu, Dejun Zhang, Xiangyun Liao
Summary: This article presents an improved dragonfly algorithm combined with a directed differential operator for feature selection. By adaptively adjusting the step size, designing a new differential operator, and updating the directed differential operator, the proposed method enhances the search capability and convergence speed. Experimental results demonstrate that the proposed algorithm outperforms other representative algorithms in terms of both convergence speed and solution quality.
Article
Chemistry, Analytical
Guancheng Lu, Deqiang He, Jinlai Zhang
Summary: Optimizing the traction curve of urban rail transit trains can achieve energy savings without changing existing infrastructure. A method based on inertia motion and energy optimization is proposed, using the maximum idle distance as the objective and considering constraints such as maximum allowable running speed, passenger comfort, train timetable, maximum allowable acceleration, and kinematics equation. Numerical experiments confirm the effectiveness of the proposed method, showing significant energy-saving properties with approximately 11.2% energy saved.
Article
Computer Science, Artificial Intelligence
Rongjuan Luo, Shoufeng Ji, Tingting Ji
Summary: This paper focuses on a special multi-objective unbalanced transportation problem considering fuel consumption, establishes a mathematical model and proposes a solution method. Numerical results and comparisons on 100 random instances and a real-life case study validate the effectiveness and practical value of the proposed method.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Yi Wang, Tao Li, Xiaojie Liu, Jian Yao
Summary: This study develops an improved adaptive clonal selection algorithm with multiple differential evolution strategies. The algorithm introduces an adaptive mutation strategy pool, an adaptive population resizing method, and detection methods for premature convergence and stagnation. Experimental results demonstrate that the proposed method outperforms state-of-the-art clonal selection algorithms and differential evolution algorithms.
INFORMATION SCIENCES
(2022)
Article
Economics
Bruno P. Bruck, Jean-Francois Cordeau, Emma Frejinger
Summary: This paper addresses a practical problem faced by a major North American railway, where terminal operators must make several decisions daily to improve efficiency. The study introduces an approach based on mixed integer linear programming to generate high-quality solutions and assist the company in generating more effective plans.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2021)
Article
Environmental Sciences
Zhu Yao, Mi Gan, Xiaoke Li, Xiaobo Liu
Summary: China is developing an air-HSR fast freight mode to meet the growing demand for express freight and reduce carbon emissions. A novel freight network design model is developed, integrating node centrality, carbon emissions, and time efficiency. The planned air-HSR network could provide nationwide express service in 24 hours and reduce carbon emissions by 2.08 million tons, a 57.35% reduction compared to the original airline network.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Zhiqiang Zeng, Min Zhang, Tao Chen, Zhiyong Hong
Summary: Most research on improving differential evolution algorithms has focused on mutation operator and parameter control. In this paper, a new selection operator is proposed to improve the algorithm’s performance, especially when individuals are in a state of stagnation. Experimental results have shown that the proposed selection operator significantly enhances the algorithm's performance and helps it escape local optimal values.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Ersin Korkmaz
Summary: In this study, Bezier search differential evolution (BeSD) and black widow optimization (BWO) algorithms-based estimation models have been developed to estimate the transportation energy consumption in Turkey. The effects of demand distribution in modes of transportation on energy consumption are examined using various parameters. The BeSD algorithm outperforms the BWO algorithm and is found to be the most appropriate method for transportation energy demand (TED) estimation.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Jose Marcio Fachin, Gilberto Reynoso-Meza, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: A new population-based stochastic optimization algorithm HSADE is proposed in this paper, addressing unconstrained global optimization problems by exploring and combining best features of DE algorithms. HSADE achieved optimal performance in benchmark functions testing and automotive sector applications, significantly improving calibration efficiency.
Article
Engineering, Civil
Kaijun Leng, Shanghong Li
Summary: The research focuses on optimizing the path of logistics distribution vehicles to enhance the efficiency of intelligent logistics systems. A new Concentration-Immune Algorithm Particle Swarm Optimization (C-IAPSO) is proposed, which shows superior performance in vehicle path optimization.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Xinghan Chen, Tianshuai Zuo, Maoxiang Lang, Shiqi Li, Siyu Li
Summary: This study investigates the integrated optimization of container transfer station selection and train timetables in a road-rail intermodal transport network. The aim is to improve rail capacity utilization and reduce transportation costs. Using a grid-based adaptive artificial bee colony algorithm, effective solutions are achieved for different case scales.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Ye Tian, Weijian Zhu, Xingyi Zhang, Yaochu Jin
Summary: PlatEMO is an open-source platform that solves complex optimization problems with a variety of metaheuristics. It has been widely used in the computational intelligence community and can tackle various difficulties regardless of the problem's field.
Editorial Material
Computer Science, Artificial Intelligence
Xingyi Zhang, Ran Cheng, Liang Feng, Yaochu Jin
Summary: Optimization and learning are two main paradigms of artificial intelligence, frequently enhanced by each other in addressing complex real-world problems. Evolutionary multi-objective optimization algorithms are widely used but face challenges in solving complex problems. Machine learning techniques have been applied to enhance these algorithms.
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE
(2023)
Editorial Material
Computer Science, Artificial Intelligence
Xingyi Zhang, Ran Cheng, Yaochu Jin, Bernhard Sendhoff
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Yapei Wu, Xingguang Peng, Handing Wang, Yaochu Jin, Demin Xu
Summary: Many real-world optimization tasks suffer from noise, but current research on noise-tolerant algorithms is limited to low-dimensional problems. This article proposes a landscape-aware grouping method for cooperative coevolutionary algorithms to solve high-dimensional problems under noisy environments. Experimental results show that the proposed method is able to effectively identify interactive decision variables in the presence of noise.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Information Systems
Yajie Zhang, Ye Tian, Hao Jiang, Xingyi Zhang, Yaochu Jin
Summary: In recent years, solving constrained multiobjective optimization problems by introducing simple helper problems has become popular. This study provides a comprehensive overview of existing constrained multiobjective evolutionary algorithms and proposes a novel helper-problem-assisted CMOEA, which has shown competitive performance in experiments.
INFORMATION SCIENCES
(2023)
Article
Automation & Control Systems
Zhening Liu, Handing Wang, Yaochu Jin
Summary: Offline data-driven multiobjective optimization problems are common in practice. To address the issue of error accumulation when using surrogate models for optimization, a new surrogate-assisted indicator-based evolutionary algorithm is proposed. This algorithm can select the appropriate type of surrogate models based on the error, and it performs well in practical problems.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Hui Bai, Ran Cheng, Danial Yazdani, Kay Chen Tan, Yaochu Jin
Summary: This paper proposes a bilevel variable grouping (BLVG)-based framework to address the issue of variable grouping in large-scale dynamic optimization when cooperating with multipopulation strategies. The framework outperforms several state-of-the-art frameworks for large-scale dynamic optimization, as shown by empirical studies.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Zhenshou Song, Handing Wang, Yaochu Jin
Summary: Expensive constrained optimization problems can be solved by evolutionary algorithms in conjunction with computationally cheap surrogates. However, existing methods neglect the differences between different surrogate models, resulting in unsatisfactory performance.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yuping Yan, Mohammed B. M. Kamel, Marcell Zoltay, Marcell Gal, Roland Hollos, Yaochu Jin, Ligeti Peter, Akos Tenyi
Summary: This paper presents a practical privacy-preserving scheme combining cryptographic techniques and communication networking solutions to address the security vulnerabilities and communication inefficiencies in federated learning. The proposed approach utilizes Kafka for message distribution, the Diffie-Hellman scheme for secure server aggregation, and gradient differential privacy for interference attack prevention.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Ye Tian, Xiaopeng Li, Haiping Ma, Xingyi Zhang, Kay Chen Tan, Yaochu Jin
Summary: This paper proposes a novel operator selection method based on reinforcement learning, which uses deep neural networks to learn a policy that determines the best operator for each parent, addressing the exploration-exploitation dilemma in operator selection.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Hongliang Guo, Qihang Peng, Zhiguang Cao, Yaochu Jin
Summary: This article proposes a unified algorithm named DRL-Searcher for the multirobot efficient search (MuRES) problem in a nonadversarial moving target scenario. DRL-Searcher utilizes distributional reinforcement learning (DRL) to evaluate and improve the search policy's return distribution for both minimizing capture time and maximizing capture probability objectives. The algorithm is further adapted for target search without real-time location information, and a recency reward is introduced for implicit coordination among multiple robots. Comparative simulations and real-world experiments demonstrate the superior performance of DRL-Searcher.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Foivos Ntelemis, Yaochu Jin, Spencer A. Thomas
Summary: Self-supervised learning (SSL) has become popular for generating invariant representations without human annotations. However, the desired representations are achieved by using prior online transformations on the input data, making each SSL framework customized for specific data types and requiring modifications for other types.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Geochemistry & Geophysics
Tao Lei, Xinzhe Geng, Hailong Ning, Zhiyong Lv, Maoguo Gong, Yaochu Jin, Asoke K. K. Nandis
Summary: In this article, an efficient ultralightweight spatial-spectral feature cooperation network (USSFC-Net) is proposed for remote sensing image change detection. The USSFC-Net addresses the high computational costs, high memory usage, and lack of cooperation between spatial and spectral features in existing methods. It achieves better performance in change detection with lower computational costs and fewer parameters compared to other CNNs-based methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Mathematics, Interdisciplinary Applications
Xilu Wang, Yaochu Jin
Summary: This study incorporates transfer learning capabilities into the optimizer by using particle filters, and proposes a particle-filter-based multi-objective optimization algorithm. By simulating a sequence of target distributions to balance multiple objectives, the Pareto optimal solutions can be approximated. Experimental results demonstrate that the proposed algorithm achieves competitive performance compared to state-of-the-art multi-objective evolutionary algorithms on most test instances.
MATHEMATICAL AND COMPUTATIONAL APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Ye Tian, Xingyi Zhang, Cheng He, Kay Chen Tan, Yaochu Jin
Summary: A large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems. While most variation operators in existing metaheuristics are empirically designed, new operators are automatically designed in this work, which are expected to be search space independent and thus exhibit robust performance on different problems.
CHINESE JOURNAL OF ELECTRONICS
(2023)
Article
Computer Science, Artificial Intelligence
Guiliang Gong, Jiuqiang Tang, Dan Huang, Qiang Luo, Kaikai Zhu, Ningtao Peng
Summary: This paper proposes a flexible job shop scheduling problem with discrete operation sequence flexibility and designs an improved memetic algorithm to solve it. Experimental results show that the algorithm outperforms other algorithms in terms of performance. The proposed model and algorithm can help production managers obtain optimal scheduling schemes considering operations with or without sequence constraints.
SWARM AND EVOLUTIONARY COMPUTATION
(2024)
Article
Computer Science, Artificial Intelligence
Daniel Molina-Perez, Efren Mezura-Montes, Edgar Alfredo Portilla-Flores, Eduardo Vega-Alvarado, Barbara Calva-Yanez
Summary: This paper presents a new proposal based on two fundamental strategies to improve the performance of the differential evolution algorithm when solving MINLP problems. The proposal considers a set of good fitness-infeasible solutions to explore promising regions and introduces a composite trial vector generation method to enhance combinatorial exploration and convergence capacity.
SWARM AND EVOLUTIONARY COMPUTATION
(2024)
