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A receding horizon control-based holistic ant colony system approach for multi-runway aircraft arrival sequencing and scheduling

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MEMETIC COMPUTING 2025年第2期17卷 1-18页

作者： Xu, Xin-Xin Jiang, Yi Sang, Hong-Yan Gong, Hui-Li Ding, Xiang-Qian Kwong, Sam Zhan, Zhi-Hui Ocean Univ China Coll Comp Sci & Technol Qingdao 266100 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China Hanyang Univ ERICA Ansan 15588 South Korea Lingnan Univ Dept Comp & Decis Sci Hong Kong Peoples R China Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China

Aircraft arrival sequencing and scheduling (ASS) is a significant research problem that aims to relieve aircraft congestion in airports. Due to the increasing demand for air transportation and the limitation of runway capacity, effective and efficient scheduling approaches for handling the ASS problem are in great need for most modern airports. Ant colony system (ACS) in evolutionary computation is now commonly used to tackle the ASS problem due to its promising performance. However, most existing ACS-based algorithms are designed to tackle single-runway ASS problems or to tackle multi-runway ASS problems in a separative fashion (e.g., first considering the landing sequence and then considering the runway assignment, which will easily result in local optima). This paper the first time proposes a novel holistic ACS (HACS)-based scheduling approach for effectively solving the multi-runway ASS problem by scheduling the sequencing and the runways simultaneously. The proposed approach follows the local memetic feature of ASS that very late arrived aircraft are not likely to be scheduled to land very early, so as to divide the ASS problem into a set of subproblems using a receding horizon control technique and then to optimize each subproblem through the HACS algorithm. The advantage of HACS is that it can figure out the runway assignment of the aircraft in each receding horizon window as well as their landing sequence simultaneously in one stage, which is a global view to obtain the global optimal solution rather than the separative ACS algorithm that is easily trapped to local optima. Instances with different scales and different congestion modes are adopted to comprehensively evaluate the performance of the HACS approach. The experimental results show the superiority of HACS, especially in the large-scale, congested mode, and in scheduling environments with more runways.

关键词： Multi-runway aircraft arrival sequencing and scheduling (MRASS) Receding horizon control (RHC) Ant colony system (ACS) evolutionary computation

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Constrained Multi-Objective Optimization With Constraint Priority

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IEEE TRANSACTIONS ON EMERGING TOPICS IN computationAL INTELLIGENCE 2025年

作者： Zhou, Xinyu Zhu, Yanjun Wang, Yizhi Sun, Ruiqing Zou, Juan Jiangxi Normal Univ Sch Comp & Informat Engn Nanchang 330022 Peoples R China Ganzhou Open Univ Ganzhou 341000 Peoples R China Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China Xiangtan Univ Key Lab Intelligent Comp & Informat Proc Minist Educ China Xiangtan 411105 Peoples R China

Constrained multi-objective problems (CMOPs) are tricky, because it is difficult to handle multiple objectives and constraints simultaneously. Most existing algorithms perform well on CMOPs with a single constraint or multiple simple constraints, but their performance on CMOPs with multiple complex constraints often deteriorates. Generally, for CMOPs, it is much easier to deal with a single constraint than with multiple constraints. Therefore, this paper proposes a strategy for identifying the constraint priority, and then handles the constraints one by one according to the priority. Specifically, for each constraint, an associated population is first established to exclusively consider this constraint, and then the corresponding infeasibility rates of all the constraints are calculated to capture the constraint priority. After that, based on the constraint priority, the roulette selection method is used to determine which constraint should be handled first, which ensures the constraint with high priority has more chances to be handled. Experimental results on five benchmark suites and three real-world problems show that our approach has significant advantages over five state-of-the-art algorithms.

关键词： Optimization Convergence Linear programming Classification algorithms Benchmark testing Sun Fans evolutionary computation Vectors Training evolutionary algorithm constraint priority constrained multi-objective optimization

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A feedback matrix based evolutionary multitasking algorithm for high-dimensional ROC convex hull maximization

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INFORMATION SCIENCES 2025年 690卷

作者： Qiu, Jianfeng Wang, Ning Shu, Shengda Li, Kaixuan Xie, Juan Chen, Chunhui Cheng, Fan Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei Peoples R China Anhui Univ Engn Res Ctr Autonomous Unmanned Syst Technol Minist Educ Hefei Peoples R China Anhui Univ Sch Artificial Intelligence Hefei Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China Anhui Jianzhu Univ Sch Math & Phys Hefei Peoples R China Anhui Prov Geomat Ctr Hefei Peoples R China Key Lab JiangHuai Arable Land Resources Protect & Hefei Peoples R China Anhui Land Satellite Remote Sensing Applicat Tech Hefei Peoples R China

Multi-objective evolutionary algorithms have shown their competitiveness in solving ROC convex hull maximization. However, due to "the curse of dimensionality", few of them focus on high-dimensional ROCCH maximization. Therefore, in this paper, a feedback matrix (FM)-based evolutionary multitasking algorithm, termed as FM-EMTA, is proposed. In FM-EMTA, to tackle "the curse of dimensionality", a feature importance based low-dimensional task construction strategy is designed to transform the high-dimensional ROCCH maximization task into several low-dimensional tasks. Then, each low-dimensional task evolves with a population. To ensure that the low-dimensional task achieves a better ROCCH, an FM-based evolutionary multitasking operator is proposed. Specifically, for each low-dimensional task i, the element FM(i,j) in feedback matrix is defined to measure the degree that the low-dimensional task j could assist task i. Based on it, an FM-based assisted task selection operator and an FM-based knowledge transfer operator are developed to constitute the evolutionary multitasking operator, with which the useful knowledge is transferred among the low-dimensional tasks. After the evolution, the best ROCCHs obtained by the low-dimensional tasks are combined together to achieve the final ROCCH on the original high-dimensional task. Experiments on twelve high-dimensional datasets with different characteristics demonstrate the superiority of the proposed FM-EMTA over the state-of-the-arts in terms of the area under ROCCH, the hypervolume indicator and the running time.

关键词： High-dimensional ROCCH Feedback matrix evolutionary computation evolutionary multitasking

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Symbiotic mechanism-based honey badger algorithm for continuous optimization

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第2期28卷 1-26页

作者： Xu, Yuefeng Zhong, Rui Cao, Yang Zhang, Chao Yu, Jun Niigata Univ Grad Sch Sci & Technol Niigata Japan Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Japan Univ Toyama Fac Engn Toyama Japan Niigata Univ Inst Sci & Technol Niigata Japan

Honey badger algorithm (HBA) is a novel meta-heuristic algorithm inspired by the foraging behaviors of honey badgers, designed to tackle single-objective continuous optimization problems. While the standard HBA demonstrates strong performance across various optimization problems, it still encounters challenges related to insufficient population diversity, particularly in later stages, hindering its ability to escape local optima. To address these problems, we propose an enhanced variant called the symbiotic mechanism-based HBA (SHBA), which incorporates the cooperative symbiotic mechanism between honey badgers and honey-guide birds. Specifically, it integrates information exchange between two distinct populations and employs multiple strategies to increase population diversity while maintaining efficient search performance. We conducted numerical experiments on CEC2017 and CEC2022 and compared their performance with nine competitive algorithms. Furthermore, the scalability and applicability of the proposed SHBA were assessed across various engineering problems. The statistical results show that the proposed SHBA is significantly competitive, with excellent convergence speed and accuracy in benchmark functions and engineering problems.

关键词： Heuristic optimization algorithm evolutionary computation Honey badger algorithm Symbiotic mechanism

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Deep reinforcement learning assisted surrogate model management for expensive constrained multi-objective optimization

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SWARM AND evolutionary computation 2025年 92卷

作者： Shao, Shuai Tian, Ye Zhang, Yajie Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China

Expensive constrained multi-objective optimization problems (ECMOPs) exist in a wide variety of applications from industrial processes to engineering systems. When solving ECMOPs, with only a limited number of function evaluations available, a common approach is to substitute the real function evaluations with more affordable evaluations provided by computationally efficient surrogate models. However, existing surrogate assisted evolutionary algorithms (SAEAs) exhibit poor versatility in handling various ECMOPs, as they only use a constant surrogate modeling scheme or switch the modeling schemes with expert knowledge. To address the dilemma in surrogate modeling, this paper proposes a deep reinforcement learning assisted evolutionary algorithm, which operates on two key issues. First, multiple surrogate models are employed to learn the approximate function of an ECMOP using previously evaluated solutions during the evolutionary process. Second, a deep reinforcement learning method is employed to learn the optimal surrogate model management strategy based on evolutionary experience, selecting the most suitable surrogate modeling scheme for the current generation. Experimental evaluations on a large number of expensive problems demonstrate that the proposed algorithm has a significant effect compared with state-of-the-art competitors.

关键词： Surrogate model Expensive optimization evolutionary computation Neural networks Multiple search modes

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Dynamic Multi-Objective Workflow Scheduling Model in Cloud Environment Based on Adaptive Mutation Strategy

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CONCURRENCY AND computation-PRACTICE & EXPERIENCE 2025年第4-5期37卷

作者： Ye, Tao Cui, Zhihua Qinghai Normal Univ Coll Comp Xining Peoples R China Taiyuan Univ Technol Coll Comp Sci & Technol Taiyuan Peoples R China Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan Peoples R China

In the cloud computing environment, workflow scheduling presents a significant challenge due to the unpredictable and dynamic nature of user demands and cloud resources. To address the complexities of workflow scheduling, this paper introduces a dynamic multi-objective workflow scheduling model that comprehensively considers task completion time, load balancing, as well as dynamic changes in power consumption and cost in real-world scenarios. To effectively solve this model and better adapt to dynamic multi-objective optimization problems, we propose a dynamic reference vector guided evolutionary algorithm (DRVEA). The proposed algorithm incorporates an adaptive random mutation strategy, which dynamically adjusts the evolutionary process based on changing optimization goals, thereby enhancing convergence and solution diversity. Experimental results, obtained from both workflow scheduling simulations and standard multi-objective test environments, demonstrate that the proposed algorithm outperforms existing methods, achieving superior results in both solution quality and adaptability.

关键词： adaptive mutation strategy cloud computing DRVEA dynamic many-objective optimization evolutionary computation workflow scheduling model

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Vehicle Painting Robot Path Planning Using Hierarchical Optimization

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IEEE ACCESS 2025年 13卷 56682-56698页

作者： Nagai, Yuya Nakamura, Hiromitsu Shinmachi, Narito Higashizono, Yuta Ono, Satoshi Kagoshima Univ Grad Sch Sci & Engn Dept Informat Sci & Biomed Engn Kagoshima 8900065 Japan TOYOTA AUTO BODY Res & Dev Co Ltd Kirishima 8994461 Japan

In vehicle production factories, the vehicle painting process employs multiple robotic arms to simultaneously apply paint to car bodies advancing along a conveyor line. Designing paint paths for these robotic arms, which involves assigning car body areas to arms and determining paint sequences for each arm, remains a time-consuming manual task for engineers, indicating the demand for automation and design time reduction. The unique constraints of the painting process hinder the direct application of conventional robotic path planning techniques, such as those used in welding. Therefore, this paper formulates the design of paint paths as a hierarchical optimization problem, where the upper-layer subproblem resembles a vehicle routing problem (VRP), and the lower-layer subproblem involves detailed path planning. This approach allows the use of different optimization algorithms at each layer, and permits flexible handling of constraints specific to the vehicle painting process through the design of variable representation, constraints, repair operators, and an initialization process at the upper and lower layers. Experiments with three commercially available vehicle models demonstrated that the proposed method can automatically design paths that satisfy all constraints for vehicle painting with quality comparable to those created manually by engineers.

关键词： Painting Manipulators Paints Path planning Welding Optimization Collision avoidance evolutionary computation Weapons Production facilities Production automation car painting robotic arms path planning hierarchical optimization constraint handling evolutionary computation

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evolutionary Multitasking via Reinforcement Learning

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IEEE TRANSACTIONS ON EMERGING TOPICS IN computationAL INTELLIGENCE 2024年第1期8卷 762-775页

作者： Li, Shuijia Gong, Wenyin Wang, Ling Gu, Qiong China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Hubei Univ Arts & Sci Sch Comp Engn Xiangyang 441053 Peoples R China

Different from traditional evolutionary algorithms (EAs), the multifactorial evolutionary algorithm (MFEA) is proposed to optimize multiple optimization tasks concurrently. Through the knowledge transfer between different tasks, MFEA has been proved to be superior to single-task EAs in the solution quality and convergence speed. Recently, various MFEAs have been developed. Most of them are based on a common model in MFEA, where a fixed knowledge transfer parameter, i.e., random mating probability ($rmp$), is used. In addition, a single evolutionary search operator is employed in the whole evolutionary process. However, in this model, the fixed $rmp$ is difficult to adapt to multiple different tasks. Besides, a single evolutionary search operator may not be suitable for problems with different properties, thus limiting the performance of the algorithm. Based on these considerations, in this article, a reinforcement learning based multifactorial evolutionary algorithm (RLMFEA) is presented. In RLMFEA, it allows different evolutionary search operators to be embedded in MFEA, and each task has a changing $rmp$ that is adaptively adjusted by reinforcement learning. The effectiveness of RLMFEA has been verified on a series of single-objective multitask optimization benchmark functions and a real-world application.

关键词： Task analysis Reinforcement learning Optimization Knowledge transfer evolutionary computation Search problems Multitasking Multifactorial evolutionary multitask optimization reinforcement learning knowledge transfer

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Reinforcement Learning-Assisted Memetic Algorithm for Sustainability-Oriented Multiobjective Distributed Flow Shop Group Scheduling

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第4期55卷 2399-2413页

作者： Wang, Yuhang Han, Yuyan Wang, Yuting Wang, Xianpeng Liu, Yiping Gao, Kaizhou Liaocheng Univ Sch Comp Sci Liaocheng 252059 Peoples R China Northeastern Univ Natl Frontiers Sci Ctr Ind Intelligence & Syst Opt Shenyang 110819 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Macau Peoples R China

Amid the global push for sustainable development, rising market demands have necessitated a multiregional, multiobjective, and flexible production model. Against this backdrop, this article investigates the multiobjective distributed flow shop group scheduling problem by formulating a mathematical model and introducing an advanced memetic algorithm integrated with reinforcement learning (RLMA). The RLMA involves a novel cooperative crossover operation in conjunction with the nature of the coupled problems to extensively explore the solution space. Additionally, the Sarsa algorithm enhanced with eligibility traces guides the selection of optimal schemes during the local enhancement phase. To ensure a balance between convergence and diversity, a solution selection strategy based on penalty-based boundary intersection decomposition is utilized. Furthermore, the increasing-efficiency and reducing-consumption strategies integrating a rapid evaluation mechanism are designed by dynamically changing the machine speed to balance economic and sustainability metrics. Comprehensive numerical experiments and comparative analyses demonstrate that the proposed RLMA surpasses existing state-of-the-art algorithms in addressing this complex problem.

关键词： Job shop scheduling Production Optimization Search problems Mathematical models Processor scheduling Memetics evolutionary computation Printed circuits Energy consumption Distributed flow shop environmental sustainability group scheduling multiobjective optimization reinforcement learning

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evolutionary Bayesian Optimization for automated circuit sizing

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KNOWLEDGE-BASED SYSTEMS 2025年 318卷

作者： Visan, Catalin Boldeanu, Mihai Nicolae, Georgian Cucu, Horia Burileanu, Corneliu Buzo, Andi Politehn Bucharest Bucharest Romania Infineon Technol Munich Germany

Automated circuit sizing using Artificial Intelligence is a rapidly increasing area of interest, primarily thanks to its potential to accelerate product time-to-market and enhance employee satisfaction. A host of methods, rooted in different fundamental research philosophies, have been devised for this class of problems. While some of them perform well in terms of convergence speed, robustness has generally been given less attention. In this study we propose a novel automatic circuit sizing framework called evolutionary Bayesian Optimization (EBO). It is a hybrid method combining the strengths of evolutionary computation techniques and Bayesian Optimization. EBO takes full advantage of parallel simulation infrastructure, by inherently using large batches of simulations. Our method is especially designed for multi-objective problems. Thus, it can optimize a large variety of circuits without the need of constructing figure of merit functions. Moreover, the strong emphasis on exploring the high-dimensional space of design variables ensures that EBO is robust and reliable across varying levels of problem complexity. We compare our framework with two state-of-the-art methods having different underlying philosophies and with arguably the most promising multi-objective evolutionary algorithm for this class of problems on four circuits: two proprietary voltage regulators, an open-source voltage regulator, and an open-source operational amplifier. The results show that EBO is superior to the other considered methods with regard to convergence speed and robustness. Generally, it can save between 30% and 70% circuit simulations compared to the next best performing method. Furthermore, EBO is the only method that finds circuit configurations that meet the specifications for all the considered circuits.

关键词： Electronic design automation Circuit sizing Multi-objective optimization evolutionary computation Bayesian Optimization

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