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A hyper heuristic algorithm for Efficient Resource Allocation in 5G Mobile Edge Clouds

IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第1期23卷 29-41页

作者： Laboni, Nadia Motalib Safa, Sadia Jahangir Sharmin, Selina Razzaque, Md. Abdur Rahman, Md. Mustafizur Hassan, Mohammad Mehedi Univ Dhaka Dept Comp Sci & Engn Green Networking Res Grp Dhaka 1000 Bangladesh Jagannath Univ Dept Comp Sci & Engn Dhaka 1100 Bangladesh King Saud Univ Coll Comp & Informat Sci Informat Syst Dept Riyadh 11543 Saudi Arabia King Saud Univ Coll Comp & Informat Sci Res Chair Pervas & Mobile Comp Riyadh 11543 Saudi Arabia

Emergence of intelligent devices and mobile edge clouds (MECs) in 5G networks has exponentially increased the number of applications that demand low latency services. However, their resource heterogeneity, limited computing power and storage including congestion in the ultra-dense 5G network, make the real-time services challenging. Existing works are limited either by addressing application delay requirements or computational load balancing. This article develops an efficient resource allocation framework for selecting optimal servers and routing paths in the 5G MEC network by jointly optimizing latency, computational, and network load variances. First, we formulate the above multi-objective problem as a mixed-integer non-linear programming problem. Further, we adopt a hyper-heuristic (AWSH) algorithm by leveraging the combined powers of Ant Colony, Whale, Sine-Cosine, and Henry Gas Solubility Optimization algorithms. The proposed AWSH algorithm works at the higher level, and it explores and exploits one of the three lower-level heuristics in each iteration to efficiently capture the dynamically varying environmental parameters and thereby address the resource allocation problem. Their collaborative effort helps to achieve a global optimum in allocating resources of 5G MEC network. Simulation results prove the superiority of the AWSH algorithm compared to state-of-the-art solutions in terms of service latency, successful offloading ratio, and load balancing.

关键词： Servers Resource management Optimization 5G mobile communication Delays Task analysis Mobile computing 5G Mobile edge cloud resource allocation load balancing multi-objective optimization problem hyper-heuristic algorithm

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hyper-heuristic Task Scheduling algorithm Based on Reinforcement Learning in Cloud Computing

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Intelligent Automation & Soft Computing 2023年第8期37卷 1587-1608页

作者： Lei Yin Chang Sun Ming Gao Yadong Fang Ming Li Fengyu Zhou School of Control Science and Engineering Shandong UniversityJinan250061ShandongChina School of Software Shandong UniversityJinan250101ShandongChina Academy of Intelligent Innovation Shandong UniversityShunhua RoadJinan250101ShandongChina Inspur Cloud Information Technology Co. Ltd.Inspur GroupJinan250101ShandongChina

The solution strategy of the heuristic algorithm is pre-set and has good performance in the conventional cloud resource scheduling ***,for complex and dynamic cloud service scheduling tasks,due to the difference in service attributes,the solution efficiency of a single strategy is low for such *** this paper,we presents a hyper-heuristic algorithm based on reinforcement learning(HHRL)to optimize the completion time of the task ***,In the reward table setting stage of HHRL,we introduce population diversity and integrate maximum time to comprehensively deter-mine the task scheduling and the selection of low-level heuristic ***,a task computational complexity estimation method integrated with linear regression is proposed to influence task scheduling ***,we propose a high-quality candidate solution migration method to ensure the continuity and diversity of the solving *** with HHSA,ACO,GA,F-PSO,etc,HHRL can quickly obtain task complexity,select appropriate heuristic strategies for task scheduling,search for the the best makspan and have stronger disturbance detection ability for population diversity.

关键词： Task scheduling cloud computing hyper-heuristic algorithm makespan optimization

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hyper-heuristic Q-Learning algorithm for Flow-Shop Scheduling Problem with Fuzzy Processing Times 19th

Hyper-heuristic Q-Learning Algorithm for Flow-Shop Schedulin...

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19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Zhu, Jin-Han Hu, Rong Li, Zuo-Cheng Qian, Bin Zhang, Zi-Qi Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Sch Mech & Elect Engn Kunming 650500 Yunnan Peoples R China

ISBN: (纸本)9789819947546;9789819947553

The flow-shop scheduling problem (FSP) has been extensively studied over the past few decades. Q-learning algorithms (QLAs) have been shown to be effective in evolving efficient heuristics for scheduling problems. In this paper, we propose a hyper-heuristic Q-learning algorithm (HHQLA) for the FSP with fuzzy processing times (FSPF) to minimize the fuzzy makespan. FSPF accounts for the existence of various disturbing factors in the manufacturing industry thatmake the processing time fuzzy. To capture this, we use triangular fuzzy numbers (TFNs) to express the processing time more realistically. Firstly, four low-level heuristics (LLHs) are designed based on the characteristics of FSPF, and the permutations of the LLHs are used as high-level individuals. Secondly, the extraction of valuable information from excellent high-level individuals by using the Q-learning algorithm (QLA). Thirdly, a newsampling strategy is designed to apply the information obtained by QLA to generate new high-level individuals and search for potential regions in the solution space. Finally, the experimental results demonstrate that the proposed HHQLA can effectively solve FSPF.

关键词： Fuzzy processing times hyper-heuristic algorithm Q-learning algorithm

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hyper-heuristic Three-Dimensional Estimation of Distribution algorithm for Distributed Assembly Permutation Flowshop Scheduling Problem 19th

Hyper-heuristic Three-Dimensional Estimation of Distribution...

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19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Li, Xiao Zhang, Zi-Qi Hu, Rong Qian, Bin Li, Kun Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Sch Mech & Elect Engn Kunming 650500 Yunnan Peoples R China

ISBN: (纸本)9789819947546;9789819947553

For the distributed assembly permutation flowshop scheduling problem (DAPFSP) to minimize the maximum completion time, this study suggests a hyperheuristic three-dimensional estimation of distribution algorithm (HH3DEDA) for solving it. The HH3DEDA consists of a high-level strategy (HLS) domain and a low-level problem (LLP) domain. The HLS domain guides the global search direction of the algorithm, while the LLP domain is responsible for searching local information in the problem domain. The HH3DEDA in this paper uses a variety of optimization strategies and metaheuristics that allow for global search and optimization, with the simultaneous setting up of nine variable neighborhood local search operations, and the arrangement of them as HLS domain individuals. Concurrently, the three-dimensional distribution estimation algorithm (3DEDA) is used in the HLS domain to learn the block structure of high-quality individuals in the HLS domain and their location information., and generating new HLS domain individuals by sampling the probability model in 3DEDA, then a series of ordered heuristic operators represented by each new individual generated at the HLS domain is used as a new heuristic algorithm at the LLP domain to perform a more in-deep neighborhood search in the problem domain.

关键词： Distributed Assembly hyper-heuristic algorithm Estimation of Distribution algorithm Variable neighborhood local search

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Collaborative hyper-heuristic Ant Colony algorithm for Solving Multi-objective Fuzzy Low-Carbon Distributed Permutation Flow-Shop and Two-Echelon Vehicle Transportation Integrated Scheduling Problem 20th

Collaborative Hyper-Heuristic Ant Colony Algorithm for Solvi...

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20th International Conference on Intelligent Computing (ICIC)

作者： Ma, Jian-hua Hu, Rong Guo, Ning Qian, Bin Kunming Univ Sci & Technol Sch Informat Engn & Automat Kunming 650500 Yunnan Peoples R China

ISBN: (纸本)9789819755776;9789819755783

This paper proposed a collaborative hyper-heuristic ant colony algorithm (CHACA) for solving a kind of multi-objective fuzzy low-carbon distributed permutation flow-shop and two-echelon vehicle transportation integrated scheduling problem (MDF-TVISP), which adopt triangular fuzzy numbers (TFNs) to represent job processing times and vehicle travel times. The optimization objectives of MDF-TVISP are to minimize the total fuzzy production and transportation costs and fuzzy carbon emissions. CHACA integrated collaborative ant colony (CACA) and hyper-heuristic algorithm (HHA). We employ eight pre-designed heuristic operations in the low-level heuristics (LLHs) to enhance the algorithm's local search capability, while utilizing a novel CACA to improve the performance of high-level strategy (HLS). The interaction between LLHs and HLS greatly improves the search performance of the CHACA. Finally, simulation experiments and algorithm comparisons validate the effectiveness of CHACA.

关键词： Distributed Permutation Flow-Shop Two-echelon Integrated Scheduling Triangular Fuzzy Numbers hyper-heuristic algorithm

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Mathematical modeling and optimization of multi-period fourth-party logistics network design problems with customer satisfaction-sensitive demand

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 278卷

作者： Zhang, Yuxin Huang, Min Fu, Yaping Jiang, Songchen Wang, Xingwei Fang, Shu-Cherng Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Northeastern Univ State Key Lab Synthet Automation Proc Ind Shenyang 110819 Liaoning Peoples R China Qingdao Univ Sch Business Qingdao 266071 Shandong Peoples R China Northeastern Univ Coll Comp Sci & Engn Shenyang 110169 Liaoning Peoples R China North Carolina State Univ Edward P Fitts Dept Ind & Syst Engn Raleigh NC 27695 USA

In the current customer-driven logistics environment, customer satisfaction has become a critical factor influencing demand. When services differ from expectations, customers often exhibit bounded rational behavior. However, existing research on fourth-party logistics (4PL) network design commonly ignores the impact of customer satisfaction and psychological behaviors on demand, creating a significant gap between current models and customer-centric demands. To address this gap, this work proposes a multi-period 4PL network design problem with demand sensitive to customer satisfaction considering bounded rational behavior. First, a novel mixed integer non-linear programming model is developed to maximize profit under investment budget and service level constraints. Second, due to the NP-hardness and non-convexity, an integration-driven Q-learning based hyper-heuristic algorithm framework is proposed. To prevent reduced diversity and premature convergence resulting from over-exploitation of the global optimum, this algorithm efficiently selects suitable low-level heuristics by integrating both population and individual states with a corresponding adaptive reward function. Finally, the proposed algorithm is compared with eight commonly used algorithms and the exact solver CPLEX using different scale instances. The effectiveness and efficiency are demonstrated by numerical results. Furthermore, managerial insights are provided for investors. Company profit depends not only on investment, costs, and income, but also on customer satisfaction. Increasing the investment budget is more beneficial when the cost-income ratio is around the required service level. Customers with higher required service levels will bring greater profits when the budget is insufficient. Ignoring the impact of customer satisfaction on demand may result in the failure to achieve expected profits.

关键词： Multi-period 4PL network design Customer satisfaction-sensitive demand Bounded rationality Integration-driven Q-learning hyper-heuristic algorithm

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Optimising work-sharing in disassembly line balancing problems: a multi-cycle strategy

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2025年

作者： Li, Min Zhou, Binghai Tongji Univ Inst Ind Engn Sch Mech Engn Caoan Rd 4800Mech Bldg A444 Shanghai 201804 Peoples R China

To improve workload balance and reduce cycle time in disassembly line balancing problems, a multi-cycle work-sharing strategy that allows tasks to be reassigned across sub-cycles is proposed for the first time. While this strategy enhances disassembly efficiency, it also increases total resource demands, as different tasks may require distinct resources for effective execution. Therefore, to effectively address this challenge, a bi-objective integer linear programming model is established with the goal of minimising both overall cycle time and total resource count. The epsilon constraint method is then employed to obtain solutions for small-scale problems. For large-scale problems, a Double Deep Q-Network-based hyper-heuristic (DDQN-HH) algorithm is developed. This approach incorporates specialised encoding and decoding strategies, two state functions, and eight heuristic action rules to enhance its effectiveness. And comparative experiments using multi-objective evaluation metrics demonstrate the DDQN-HH algorithm's superiority over three other leading algorithms: the Deep Q-Network-based hyper heuristic (DQN-HH) algorithm, the Random, Greedy-based hyper heuristic (RG-HH) algorithm, and the Multi-objective Equilibrium Optimiser (MOEO) algorithm. Furthermore, the DDQN-HH's practical significance in addressing real-world engineering problems is validated through managerial applications.

关键词： Disassembly line balancing problems multi-cycle work-sharing epsilon constraint Double Deep Q-Network hyper-heuristic algorithm

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Multi-objective test case prioritization based on an improved MOEA/D algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 266卷

作者： Chen, Xin Luo, Dengfa Yu, Dongjin Fang, Zhaohao Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou 310018 Peoples R China

Multi-objective test case prioritization (MOTCP) problems are a critical task in software testing. It aims to prioritize test cases with multiple test standards and targets. However, there are few studies focusing on resolving MOTCP problems and the performance of existing methods is not promising. In this paper, we propose anew algorithm named PMOEA/D-VW which is based on the original MOEA/D (multi-objective evolutionary algorithms based on decomposition) framework. We improve MOEA/D by making improvements in the crossover operator, the weight vector generation and updating strategy, and the neighborhood changing strategy. We conduct extensive experiments on six open source Java projects to validate the effectiveness of PMOEA/D-VW. Experimental results show that: (1) PMOEA/D-VW achieves 98.67% in terms of APFDc (the average of the percentage of faults detected) on average and improves the state-of-the-art (SOTA) MOPSO by 6.77%. (2) The Pareto optimal (PS) solutions achieved by PMOEA/D-VW have better distribution than those of the baseline methods. (3) The new crossover operator IPBX can contribute to improve the performance of PMOEA/D-VW.

关键词： Multi-objective optimization problem MOEA/D hyper-heuristic algorithm Crossover operator Test case prioritization

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Integrated scheduling of material delivery and processing

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 201卷

作者： Zheng, Jinlong Zhao, Yixin Li, Jianfeng She, Wencong Li, Yinya Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China Beijing Aeronaut Technol Res Ctr Beijing 100076 Peoples R China

Material delivery and product processing are two important components of intelligent manufacturing. Existing research mostly focuses on either job shop scheduling or vehicle scheduling. However, integrating vehicle scheduling in material delivery with job shop scheduling can effectively improve the production efficiency of enterprises. This paper investigates the flexible flow shop scheduling problem (FFSP) considering material delivery and process scheduling (FFSPDP). First, a mixed integer programming model is developed to minimize the sum of maximum completion times at each stage. Second, a hyper-heuristic algorithm based on deep reinforcement learning is proposed. Twelve low-level heuristic (LLH) operators are designed to perturb and optimize solutions, and a deep neural network is used to learn the relationship between the states of the solution and the LLH. This paper designs a new state space and reward function to improve the stability of deep neural network training. Then, two improved NEH2 algorithms are designed to initialize the population, which are used to improve the quality of the initial solution and improve the convergence speed of the algorithm. Finally, extensive comparative experiments validate the effectiveness and performance of the proposed method. The results show that the developed mathematical model and the algorithm proposed can effectively solve the investigated problem in this paper.

关键词： Flexible flow shop scheduling problem hyper-heuristic algorithm Deep reinforcement learning Integrated scheduling

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hyper-heuristic for integrated due-window scheduling and vehicle routing problem for perishable products considering production quality

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ENGINEERING OPTIMIZATION 2021年第11期53卷 1902-1921页

作者： Jafari Nozar, F. Behnamian, J. Bu Ali Sina Univ Dept Ind Engn Fac Engn Hamadan Hamadan Iran

In today's competitive environment, industrial units are seeking to reduce costs and increase customer numbers;if their business involves perishable products, achieving these goals is even more important. By integrating scheduling and vehicle routing problems for perishable products, this study tries to minimize costs and maximize customers' purchase probability. In the scheduling stage, a flexible flowshop scheduling problem considering production quality is studied. After completion of the last job, the distribution stage begins and each product must be delivered in its time-window. This problem is formulated as mixed-integer linear programming and solved by the GAMS solver. Owing to the NP hardness of this problem, a hyper-heuristic is proposed to solve large-size instances. In the proposed algorithm, the acceptance of the solution is based on the Monte Carlo criterion. Finally, the numerical results and analysis demonstrate the proposed algorithm's superiority on some criteria compared to the non-dominated sorting genetic algorithm-II (NSGA-II).

关键词： Scheduling vehicle routing problem perishable products multi-objective optimization hyper-heuristic algorithm production quality

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