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5th Information Systems International Conference (ISICO) on Role of Information Systems in Industry 4.0

作者： Muklason, Ahmad Irianti, Redian Galih Marom, Ahsanul Inst Teknol Sepuluh Nopember Dept Informat Syst Surabaya Indonesia

Course timetabling problems are challenging, laborious and repetitive work in the universities. However, in many universities this repetitive works was still carried out manually. It is because there are so many constraints that must be considered either from the students and lecturers' requirement or the infrastructure such as room availability. Therefore, to automate the process of timetabling is hard problem. Scientifically, in the literature, course timetabling optimization is one of non-deterministic polynomial problems, usually abbreviated as NP-hard problem. For NP-hard problem, there is not any exact algorithm known could solve the problem within polynomial-time. The state-of-the-art methods for solving the problem are approximation algorithms that are mainly meta-heuristics. This paper presents a new approach, namely, hyper-heuristics as opposed to meta-heuristics, to cope with the need of intensive problem specific parameter tuning in meta-heuristics approach. The algorithms employed within hyper-heuristic approach presented in this paper are tabu search hybridize with variable neighborhood search. Tested over two real-world course timetabling problem datasets, the computational results from the experiments showed that the proposed algorithm could automate the process of timetabling. Furthermore, compared to the timetable produced manually, in term of soft constraint violation penalties, the proposed algorithm could improve by 1855 and 1110 respectively. In addition to new approach, the main contribution of this paper is two real-world course timetabling problems available for public to encourage further research as future works. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Course Timetabling Variable Neighborhood Search algorithm Tabu Search algorithm hyper-heuristic algorithm

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The sorted-waste capacitated location routing problem with queuing time: A cross-entropy and simulated-annealing-based hyper-heuristic algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期201卷 1页

作者： Shang, Chunjian Ma, Liang Liu, Yong Sun, Shuo Univ Shanghai Sci & Technol Sch Management Shanghai Peoples R China

Waste sorting is an imperative and significant issue in China, of which sorted-waste collection and transportation are indispensable parts. Despite its vital yet practical significance, few studies research mathematical models or algorithms of waste collection and transportation from the perspective of waste sorting. To address this issue, we extend a novel transportation model for the waste management system, namely, capacitated location routing problem with queuing time (CLRPQT) and design a cross-entropy and simulated-annealing based hyper-heuristic algorithm (CE-SAHH) for it. The main idea of this paper is three-fold: (1) As a particular property of this problem, source nodes cannot but need to be served by more than one vehicle that causes queuing time between a heterogeneous fleet of vehicles, which is novel in terms of the proposed model;(2) For the methodological contribution, a character encoding scheme, new decoding procedure, and local search strategy are designed embedded in the proposed method;(3) An integration of simulated annealing strategy and the cross-entropy-based hyper-heuristic algorithm is developed to overcome the combinatorial optimization problem with a more complex solution of this study. Finally, the results and analysis of three numeric experiments on benchmark datasets, new instances of CLRPQT, and simulation data in Shanghai, China, verify the effectiveness and universality of the proposed model and method.

关键词： Capacitated location routing problem Sorted-waste collection and transportation hyper-heuristic algorithm Cross-entropy algorithm Simulated-annealing scheme

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Green location routing problem with flexible multi-compartment for source-separated waste: A Q-learning and multi-strategy-based hyper-heuristic algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期121卷

作者： Shang, Chunjian Ma, Liang Liu, Yong Univ Shanghai Sci & Technol Business Sch Shanghai Peoples R China

In this paper, we extend a novel model for source-separated waste collection and transportation, the green location routing problem with multi-compartment (GLRPFMC), for which we design a Q-learning and multi -strategy-based hyper-heuristic algorithm (QLMSHH). The remarkable merits of this paper can be highlighted as the following threefold: (1) The GLRPFMC is novel in that it constructs a variant of the location routing problem with carbon emissions and flexible multi-compartment sizes that occurs in a source-separated waste transportation context. (2) For the methodological contribution, the QLMSHH is presented to design a hyper -heuristic model by intelligently selecting appropriate high-level heuristic components during different stages of the optimization process. (3) The proposed method incorporates the design of solution representations, evolution-acceptance pairs for high-level heuristic construction, the repairing solution scheme, and the local search strategy. Finally, sufficient experiments are conducted on the benchmark, new instances, and simulation data of GLRPFMC and draw some managerial insights. The satisfactory results highlight the efficiency and universality of the proposed model and method.

关键词： Green location routing problem Source-separated waste collection and transportation Flexible Multi-compartments hyper-heuristic algorithm Q-learning

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A hierarchical reinforcement learning-aware hyper-heuristic algorithm with fitness landscape analysis

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 90卷

作者： Zhu, Ningning Zhao, Fuqing Yu, Yang Wang, Ling Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou 730050 Peoples R China Jiangsu Univ Technol Sch Elect & Informat Engn Changzhou 213001 Peoples R China Tsinghua Univ Dept Automat Beijing 10084 Peoples R China

The automation of meta-heuristic algorithm configuration holds the utmost significance in evolutionary computation. A hierarchical reinforcement learning-aware hyper-heuristic algorithm with fitness landscape analysis (HRLHH) is proposed to flexibly configure the suitable algorithms under various optimization scenarios. Two kinds of fitness landscape analysis techniques improved based on specific problem characteristics construct the state spaces for hierarchical reinforcement learning. Among them, an adaptive classification based on dynamic ruggedness of information entropy is designed to discern the complexity of problems, which serves as the basis for decision-making actions in upper-layer space. Additionally, an online dispersion metric based on knowledge is further presented to distinguish the precise landscape features in lower-layer space. In light of the characteristics of the state spaces, the hierarchical action spaces composed of meta-heuristics with disparate exploration and exploitation are designed, and various action selection strategies are introduced. Taking into account the real-time environment and algorithm evolution behavior, dynamic reward mechanisms based on evolutionary success rate and population convergence rate are utilized to enhance search efficiency. The experimental results on the IEEE Congress on Evolutionary Computation (CEC) 2017, CEC 2014, and large-scale CEC 2013 test suites demonstrate that the proposed HRLHH exhibits superiority in terms of accuracy, stability, and convergence speed, and possesses strong generalization.

关键词： Hierarchical reinforcement learning hyper-heuristic algorithm Fitness landscape analysis Automated algorithm design Meta-heuristic algorithms

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Clustering-based hyper-heuristic algorithm for multi-region coverage path planning of heterogeneous UAVs

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NEUROCOMPUTING 2024年 610卷

作者： Zhao, Bocheng Huo, Mingying Li, Zheng Yu, Ze Qi, Naiming Harbin Inst Technol Harbin 150001 Peoples R China

In the context of multi-heterogeneous UAV coverage path planning, an effective solution method has been proposed. Firstly, regions are set up as fully connected graphs which are cut into multiple subgraphs by spectral clustering method to assign tasks to multi-heterogeneous UAVs. Additionally, an RL-based hyper-heuristic algorithm is proposed. heuristic space is parameterized by GNN which is trained with the reward provided by the optimization goal to automate design and enhance the heuristic metrics, avoiding the inefficiency and suboptimality of expert design and manual parameter tuning. Compared with existing methods, the proposed algorithm has a better performance in task completion time, execution time and deviation rate, which shows its potential application in the coverage path planning problem of multi-heterogeneous UAVs.

关键词： Multi-heterogeneous UAVs Coverage path planning Graph Neural Network Spectral clustering hyper-heuristic algorithm

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A hyper-heuristic algorithm via proximal policy optimization for multi-objective truss problems

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 256卷

作者： Yin, Shihong Xiang, Zhengrong Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Jiangsu Peoples R China

This paper proposes a hyper-heuristic evolutionary algorithm via proximal policy optimization, named HHEAPPO, for solving multi-objective truss optimization problems. HHEA-PPO has a two-layer structure: a highlevel strategy and low-level heuristics. The high-level strategy consists of proximal policy optimization, while the low-level heuristics consist of ten predefined heuristic operators. During the iteration process, the high-level strategy selects the most promising low-level heuristic according to the state of the individuals and the population. To maintain the convergence and distribution of the external Pareto archive, a dynamic crowding distance mechanism is employed. HHEA-PPO is applied to eight multi-objective truss optimization problems and compared with thirteen state-of-the-art optimization algorithms in terms of success rate, average computation duration, and average fitness evaluations to evaluate its performance. The results show that HHEA-PPO has higher search efficiency and greater stability, demonstrating its ability to solve large-scale engineering design problems.

关键词： Multi-objective optimization Truss optimization Proximal policy optimization hyper-heuristic algorithm Dynamic crowding distance

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Solving the Multi-robot task allocation with functional tasks based on a hyper-heuristic algorithm

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APPLIED SOFT COMPUTING 2023年 146卷

作者： Yan, Fuhan Di, Kai Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Chongqing Peoples R China Chongqing Univ Posts & Telecommun Key Lab Big Data Intelligent Comp Chongqing Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing Peoples R China

Multi-robot task allocation (MRTA) problem is a classical problem in multi-robot systems. The most common assumption in MRTA is that all the tasks need to be completed with the time cost as small as possible. It is worth noting that some tasks may be optional in some real-world situations, i.e., the robots do not necessarily need to complete all tasks. These optional tasks do not limit the achievement of the goal, but the completion of these tasks will lead to some functional effects (e.g., making other tasks easier to be completed). Therefore, these optional tasks can be called "functional task". Different from functional tasks, if a task must be completed, this task can be called "compulsory task". In this paper, we study the problem where the robots need to minimize the time cost of completing all compulsory tasks and can selectively complete some functional tasks. The existence of the functional tasks greatly increases the solution space of MRTA, because the functional tasks should be firstly suitably selected and then suitably allocated. Existing related algorithms are usually based on the assumption that all tasks must be allocated. Thus, these algorithms cannot suitably deal with functional tasks. We analyze the characteristics of this problem and present a new hyper-heuristic algorithm. The low-level heuristic (LLH) in hyper-heuristic is designed to score the functional tasks by using influence diffusion model. The high-level strategy (HLS) seeks the optimal values of the key parameters in the influence diffusion model based on particle swarm optimization (PSO). Extensive simulated experiments are presented to comprehensively analyze the proposed algorithm. The proposed hyper-heuristic is compared with greedy algorithm and two meta-heuristic algorithms. Based on the simulated data, it is known that the hyper-heuristic algorithm can outperform the benchmark algorithms especially when the number of functional tasks is large.& COPY;2023 Elsevier B.V. All rights

关键词： Multi-robot Task allocation Functional task hyper-heuristic algorithm

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A Novel Simulated Annealing-Based hyper-heuristic algorithm for Stochastic Parallel Disassembly Line Balancing in Smart Remanufacturing

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SENSORS 2023年第3期23卷 1652-1652页

作者： Hu, Youxi Liu, Chao Zhang, Ming Jia, Yu Xu, Yuchun Aston Univ Coll Engn & Phys Sci Birmingham B4 7ET England

Remanufacturing prolongs the life cycle and increases the residual value of various end-of-life (EoL) products. As an inevitable process in remanufacturing, disassembly plays an essential role in retrieving the high-value and useable components of EoL products. To disassemble massive quantities and multi-types of EoL products, disassembly lines are introduced to improve the cost-effectiveness and efficiency of the disassembly processes. In this context, disassembly line balancing problem (DLBP) becomes a critical challenge that determines the overall performance of disassembly lines. Currently, the DLBP is mostly studied in straight disassembly lines using single-objective optimization methods, which cannot represent the actual disassembly environment. Therefore, in this paper, we extend the mathematical model of the basic DLBP to stochastic parallel complete disassembly line balancing problem (DLBP-SP). A novel simulated annealing-based hyper-heuristic algorithm (HH) is proposed for multi-objective optimization of the DLBP-SP, considering the number of workstations, working load index, and profits. The feasibility, superiority, stability, and robustness of the proposed HH algorithm are validated through computational experiments, including a set of comparison experiments and a case study of gearboxes disassembly. To the best of our knowledge, this research is the first to introduce gearboxes as a case study in DLBP which enriches the research on disassembly of industrial equipment.

关键词： disassembly line balancing problem hyper-heuristic algorithm multi-objective optimisation disassembly remanufacturing

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Automated Course Timetabling Optimization Using Tabu-Variable Neighborhood Search Based hyper-heuristic algorithm

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Procedia Computer Science 2019年 161卷 656-664页

作者： Ahmad Muklason Redian Galih Irianti Ahsanul Marom Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya

Course timetabling problems are challenging, laborious and repetitive work in the universities. However, in many universities this repetitive works was still carried out manually. It is because there are so many constraints that must be considered either from the students and lecturers’ requirement or the infrastructure such as room availability. Therefore, to automate the process of timetabling is hard problem. Scientifically, in the literature, course timetabling optimization is one of non-deterministic polynomial problems, usually abbreviated as NP-hard problem. For NP-hard problem, there is not any exact algorithm known could solve the problem within polynomial-time. The state-of-the-art methods for solving the problem are approximation algorithms that are mainly meta-heuristics. This paper presents a new approach, namely, hyper-heuristics as opposed to meta-heuristics, to cope with the need of intensive problem specific parameter tuning in meta-heuristics approach. The algorithms employed within hyper-heuristic approach presented in this paper are tabu search hybridize with variable neighborhood search. Tested over two real-world course timetabling problem datasets, the computational results from the experiments showed that the proposed algorithm could automate the process of timetabling. Furthermore, compared to the timetable produced manually, in term of soft constraint violation penalties, the proposed algorithm could improve by 1855 and 1110 respectively. In addition to new approach, the main contribution of this paper is two real-world course timetabling problems available for public to encourage further research as future works.

关键词： Course Timetabling Variable Neighborhood Search algorithm Tabu Search algorithm hyper-heuristic algorithm

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An Improved hyper-heuristic Clustering algorithm for Wireless Sensor Networks

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MOBILE NETWORKS & APPLICATIONS 2017年第5期22卷 943-958页

作者： Tsai, Chun-Wei Chang, Wei-Lun Hu, Kai-Cheng Chiang, Ming-Chao Natl Chung Hsing Univ Dept Comp Sci & Engn Taichung 40227 Taiwan Natl Sun Yat Sen Univ Dept Comp Sci & Engn Kaohsiung 80424 Taiwan

Clustering is one of the most famous open problems of wireless sensor network (WSN) that has been studied for years because all the sensors in a WSN have only a limited amount of energy. As such, the so-called low-energy adaptive clustering hierarchy (LEACH) was presented to prolong the lifetime of a WSN. Although the original idea of LEACH is to keep each sensor in a WSN from being chosen as a cluster head (CH) too frequently so that the loading of the sensors will be balanced, thus avoiding particular sensors from running out of their energy quickly and particular regions from failing to work, it is far from perfect because LEACH may select an unsuitable set of sensors as the cluster heads. In this paper, a high-performance hyper-heuristic algorithm will be presented to enhance the clustering results of WSN called hyper-heuristic clustering algorithm (HHCA). The proposed algorithm is designed to reduce the energy consumption of a WSN, by using a high-performance metaheuristic algorithm to find a better solution to balance the residual energy of all the sensors so that the number of alive sensor nodes will be maximized. To evaluate the performance of the proposed algorithm, it is compared with LEACH, LEACH with genetic algorithm, and hyper-heuristic algorithm alone in this study. Experimental results show that HHCA is able to provide a better result than all the other clustering algorithms compared in this paper, in terms of the energy consumed.

关键词： Wireless sensor networks Clustering hyper-heuristic algorithm

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