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学者姓名：朱光宇
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Abstract ：
孔加工是机械制造的基本工序之一.针对数控机床的刀具路径优化问题,提出一种新颖的孔加工刀具路径优化模型—-带可决策孔的孔加工多刀具路径优化问题(MTdDPO).在该模型中,工件上的孔分为两类:固定孔和可决策孔.MTdDPO的目标是通过判断可决策孔的路径归属和路径内各孔的加工顺序来实现加工路径长度的最小化.为实现MTdDPO的优化,提出基于强化学习的分段遗传算法(RLSGA).在RLSGA中,种群被视为智能体,智能体的状态是种群的多样性系数,3种不同的分段交叉算子是智能体的动作,智能体的奖励与种群的适应度值和多样性系数的变化有关.针对MTdDPO,新建5个基准测试问题,并在测试问题上将RLSGA与其他4个算法进行对比.结果表明,RLSGA的表现明显优于其他算法,RLSGA能够有效地解决MTdDPO问题.
Keyword ：
孔加工路径优化 孔加工路径优化 强化学习 强化学习 组合优化 组合优化 遗传算法 遗传算法
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| GB/T 7714 | 朱光宇 , 张德颂 . 基于强化学习的遗传算法求解一种新的钻削路径优化问题 [J]. | 控制与决策 , 2024 , 39 (2) : 697-704 . |
| MLA | 朱光宇 等. "基于强化学习的遗传算法求解一种新的钻削路径优化问题" . | 控制与决策 39 . 2 (2024) : 697-704 . |
| APA | 朱光宇 , 张德颂 . 基于强化学习的遗传算法求解一种新的钻削路径优化问题 . | 控制与决策 , 2024 , 39 (2) , 697-704 . |
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Hole-making is one of the basic processes of mechanical production. For the problem of toolpath optimization of computer numerical control (CNC) machine tools, a novel toolpath model for hole-making called as multi-tool drilling path optimization problems with decidable holes (MTdDPO) is proposed. In the MTdDPO, holes on workpieces are divided into two categories: fixed holes and decidable holes. The goal of the MTdDPO is to minimize the length of the machining path by judging the path ownership of decidable holes and the machining sequence of all holes in each path. To realize the optimization of the MTdDPO, a segmented genetic algorithm based on reinforcement learning (RLSGA) is proposed. The population of the RLSGA is regarded as the agent, the states of the agent are the intervals of the diversity coefficient of the population, three different segmental crossover operators are the actions of the agent, and the reward of the agent is related to the changes in fitness value and diversity coefficients of the population. Based on the MTdDPO, 5 benchmark test problems are designed, and the RLSGA is compared with other 4 algorithms on these test problems. Results show that the performance of the RLSGA is significantly better than other algorithms, which means the RLSGA can effectively solve the MTdDPO problems. © 2024 Northeast University. All rights reserved.
Keyword ：
Benchmarking Benchmarking Combinatorial optimization Combinatorial optimization Computer control systems Computer control systems Genetic algorithms Genetic algorithms Infill drilling Infill drilling Learning algorithms Learning algorithms Machine tools Machine tools Reinforcement learning Reinforcement learning
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| GB/T 7714 | Zhu, Guang-Yu , Zhang, De-Song . Genetic algorithm based on reinforcement learning for a novel drilling path optimization problem [J]. | Control and Decision , 2024 , 39 (2) : 697-704 . |
| MLA | Zhu, Guang-Yu 等. "Genetic algorithm based on reinforcement learning for a novel drilling path optimization problem" . | Control and Decision 39 . 2 (2024) : 697-704 . |
| APA | Zhu, Guang-Yu , Zhang, De-Song . Genetic algorithm based on reinforcement learning for a novel drilling path optimization problem . | Control and Decision , 2024 , 39 (2) , 697-704 . |
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The optimal foraging algorithm (OFA) is a swarm-based algorithm motivated by animal behavioral ecology theory. When solving complex optimization problems characterized by multiple peaks, OFA is easy to get trapped in local minima and encounters slow convergence. Therefore, this paper presents an improved optimal foraging algorithm with social behavior based on quasi-opposition (QOS-OFA) to address these problems. First, quasi-opposition-based learning (QOBL) is introduced to improve the overall quality of the population in the initialization phase. Second, an efficient cosine-based scale factor is designed to accelerate the exploration of the search space. Third, a new search strategy with social behavior is designed to enhance local exploitation. The cosine-based scale factor is used as a regulator to achieve a balance between global exploration and local exploitation. The proposed QOS-OFA is compared with seven meta-heuristic algorithms on a CEC benchmark test suite and three real-world optimization problems. The experimental results show that QOS-OFA is better than other competitors on most of the test problems.
Keyword ：
Global exploration Global exploration Local exploitation Local exploitation Optimal foraging algorithm Optimal foraging algorithm Quasi-opposition-based learning Quasi-opposition-based learning Social behavior Social behavior
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| GB/T 7714 | Ding, Chen , Zhu, Guangyu . Improved optimal foraging algorithm for global optimization [J]. | COMPUTING , 2024 , 106 (7) : 2293-2319 . |
| MLA | Ding, Chen 等. "Improved optimal foraging algorithm for global optimization" . | COMPUTING 106 . 7 (2024) : 2293-2319 . |
| APA | Ding, Chen , Zhu, Guangyu . Improved optimal foraging algorithm for global optimization . | COMPUTING , 2024 , 106 (7) , 2293-2319 . |
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As one of the most fundamental operations in mechanical production, hole-making plays a crucial role. However, existing hole-making sequence optimization models are not suitable for workshops with variable production parameters. To address this issue, a new model, named multi-objective multi-tool hole-making sequence optimization with precedence constraints (MO-MTpcHSO), is proposed in this paper. The model has two objectives: spindle travel distance and tool switching time. To solve MO-MTpcHSO, a customized Q-learning based genetic algorithm (QLGA) is proposed. The adaptive encoding method allows chromosomes to express feasible solutions, the population is considered as the agent, and the states are intervals of the diversity coefficient. Different insertion methods in the crossover operator are set as actions, and the reward is related to the diversity and values of objective functions of the population. The effectiveness of QLGA is validated by comparing it with other algorithms in practical workpieces. Moreover, the reasonability of actions and the necessity of the Q-learning framework in QLGA are validated.
Keyword ：
genetic algorithm genetic algorithm hole-making sequence optimization hole-making sequence optimization multi-objective problem multi-objective problem Q-learning Q-learning
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| GB/T 7714 | Zhang, Desong , Chen, Yanjie , Zhu, Guangyu . Multi-Objective Hole-Making Sequence Optimization by Genetic Algorithm Based on Q-Learning [J]. | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE , 2024 . |
| MLA | Zhang, Desong 等. "Multi-Objective Hole-Making Sequence Optimization by Genetic Algorithm Based on Q-Learning" . | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE (2024) . |
| APA | Zhang, Desong , Chen, Yanjie , Zhu, Guangyu . Multi-Objective Hole-Making Sequence Optimization by Genetic Algorithm Based on Q-Learning . | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE , 2024 . |
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增材制造中多种类零件分批排样存在打印时间成本高和工作台空间利用率低等问题,且需考虑零件高度的影响.分批排样问题包括零件在工作台上放置策略和成型批次分配两个子问题.放置策略涉及成型方向选择、零件碰撞检测和定位策略.基于建立的成型方向准则,利用多边形表示零件投影轮廓,提出基于临界多边形的改进移动碰撞法以确定3种不同形态多边形零件的免碰撞排放范围,提出新的左下定位策略放置零件,新定位策略融合了改进的建设性方法和契合度;提出单机台面积占用最大化策略实现零件成型批次分配.基于上述研究,提出基于最优觅食算法的分批排样算法,算法采用双重编码表达零件放置顺序和旋转角度,以最小化完工时间为目标实现优化分批排样.以案例库零件为对象,与3种对比算法比较,表明所提方法的方案能有效提高增材制造的空间利用率和缩短完工时间.
Keyword ：
三维排样 三维排样 分批排样 分批排样 增材制造 增材制造 最优觅食算法 最优觅食算法
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| GB/T 7714 | 朱光宇 , 蒋起爽 , 林晓斌 . 基于最优觅食算法的增材制造中多种类零件分批排样研究 [J]. | 计算机集成制造系统 , 2024 , 30 (7) : 2340-2349 . |
| MLA | 朱光宇 等. "基于最优觅食算法的增材制造中多种类零件分批排样研究" . | 计算机集成制造系统 30 . 7 (2024) : 2340-2349 . |
| APA | 朱光宇 , 蒋起爽 , 林晓斌 . 基于最优觅食算法的增材制造中多种类零件分批排样研究 . | 计算机集成制造系统 , 2024 , 30 (7) , 2340-2349 . |
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针对半导体制造车间产品重入机台时存在机台状态不一致,使得传统可重入调度方法难以适用的问题,根据半导体车间生产特性,提出了半导体车间多目标可重入混合流水车间调度问题,以最小化最大完工时间为基础,考虑以降低产品不合格率、减少机台工序切换次数为目标,建立此问题的多目标数学模型.提出基于实质不确定因子的最优觅食算法,采用灰色关联分析与MYCIN不确定因子的勾股模糊集的多目标处理策略,将帕累托(Pareto)解的实质不确定因子作为最优觅食算法的适应度值.编码采用基于工件号编码方案,解码通过三段式方法生成可行的调度解.通过仿真实验和半导体车间案例与其他三种算法对比,验证了所提出的模型,算法性能分析结果表明所提出的模型合理,算法具有明显优势.
Keyword ：
半导体 半导体 可重入混合流水车间 可重入混合流水车间 多目标 多目标 实质不确定因子 实质不确定因子 最优觅食算法 最优觅食算法
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| GB/T 7714 | 朱光宇 , 贾海斌 . 面向半导体车间多目标可重入调度研究 [J]. | 华中科技大学学报（自然科学版） , 2023 , 51 (2) : 122-130 . |
| MLA | 朱光宇 等. "面向半导体车间多目标可重入调度研究" . | 华中科技大学学报（自然科学版） 51 . 2 (2023) : 122-130 . |
| APA | 朱光宇 , 贾海斌 . 面向半导体车间多目标可重入调度研究 . | 华中科技大学学报（自然科学版） , 2023 , 51 (2) , 122-130 . |
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Multi-objective evolutionary algorithms have become the most important method to deal with multi-objective optimization problems (MOP). To improve the performance of particle swarm optimization (PSO) in addressing MOPs, a multi-objective PSO based on temporal-difference learning (TDLMOPSO) is proposed in this paper. The iteration process of TDLMOPSO is transformed into a Markov decision process, particles are treated as agents, each agent has a personal archive, the states are designed for the connection of actions, the actions of particles contain all necessary behavior of them: basic movement, jump out of local optimum, and local search, and the rewards depend on the relationship between particles' positions and their personal archives. Besides, the external archive deletion strategy and the leader selection strategy are redesigned based on the unsupervised learning algorithm to enhance the diversity of solutions in the external archive. The effectiveness of TDLMOPSO is verified by applying it with other seven advanced multi-objective algorithms in MOP benchmark test suites. Furthermore, the time complexity and parameter sensitivity of TDLMOPSO are analyzed.
Keyword ：
Multi-objective optimization Multi-objective optimization Particle swarm optimization Particle swarm optimization Reinforcement learning Reinforcement learning Temporal-difference learning Temporal-difference learning
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| GB/T 7714 | Zhang, Desong , Zhu, Guangyu . Particle swarm optimization based on temporal-difference learning for solving multi-objective optimization problems [J]. | COMPUTING , 2023 , 105 (8) : 1795-1820 . |
| MLA | Zhang, Desong 等. "Particle swarm optimization based on temporal-difference learning for solving multi-objective optimization problems" . | COMPUTING 105 . 8 (2023) : 1795-1820 . |
| APA | Zhang, Desong , Zhu, Guangyu . Particle swarm optimization based on temporal-difference learning for solving multi-objective optimization problems . | COMPUTING , 2023 , 105 (8) , 1795-1820 . |
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Permutation flow-shop scheduling problems with many objectives have wide applications in the modern manufacturing domain such as the printed circuit board (PCB) industry. In this paper, an upgraded method called reinforcement cumulative prospect theory is proposed for solving many-objective permutation flow-shop scheduling problems. Reinforcement cumulative prospect theory is determined by two reference points, the improved prospect value function, and the entropy-based decision weight function. A novel many-objective optimization algorithm, namely, an optimal foraging algorithm based on the reinforcement cumulative prospect theory (OFA/RCPT), is presented. The comprehensive prospect value is used as the fitness strategy of the OFA/RCPT algorithm to guide the optimization process. The performance of the proposed algorithm is assessed by a comparison with nine state-of-the-art algorithms. Three classification experiments are carried out on six Walking-Fish-Group test cases, seven permutation flow-shop scheduling benchmark instances, and a practical permutation flow-shop scheduling problem in low-volume PCB manufacturing. For the experiment, four performance metrics are adopted, and the commercial software Quest is used to simulate a PCB production line. The simulation results show that the proposed algorithm has better performance than the other algorithms.
Keyword ：
Many-objective optimization Many-objective optimization Optimal foraging algorithm Optimal foraging algorithm Permutation flow-shop scheduling problems Permutation flow-shop scheduling problems Reinforcement cumulative prospect theory Reinforcement cumulative prospect theory
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| GB/T 7714 | Ding, Chen , Qiao, Fei , Zhu, GuangYu . Solving a many-objective PFSP with reinforcement cumulative prospect theory in low-volume PCB manufacturing [J]. | NEURAL COMPUTING & APPLICATIONS , 2023 , 35 (27) : 20403-20422 . |
| MLA | Ding, Chen 等. "Solving a many-objective PFSP with reinforcement cumulative prospect theory in low-volume PCB manufacturing" . | NEURAL COMPUTING & APPLICATIONS 35 . 27 (2023) : 20403-20422 . |
| APA | Ding, Chen , Qiao, Fei , Zhu, GuangYu . Solving a many-objective PFSP with reinforcement cumulative prospect theory in low-volume PCB manufacturing . | NEURAL COMPUTING & APPLICATIONS , 2023 , 35 (27) , 20403-20422 . |
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In the flexible job shop scheduling problem (FJSP), immediate predecessor operations have an explicit impact on the scheduling results, and are often ignored. To analyze the influence of immediate predecessor operations on machine tool manufacturing workshops, four forms of immediate predecessor operations with the consideration of uncertain transportation and preparation time are defined. Then, the four forms of immediate predecessor operations are integrated into the FJSP, and a multiobjective FJSP is constructed. An improved optimal foraging algorithm (OFA) and Pythagorean fuzzy set (PFS) are combined to establish a multiobjective optimization algorithm, named PFSOFA. This algorithm is then used to solve the multiobjective FJSP. In PFSOFA, the Pareto fronts are mapped into PFSs. The Pythagorean fuzzy numbers (PFNs) in a PFS are transformed into right triangles, and the distances between the PFNs and the reference PFNs are defined as the distances between their right triangle centroids. Then, all distances of a PFS are integrated by the distance prospect function to obtain a distance prospect value. This value is then used to lead the iteration of PFSOFA. Through extensive experiments, including a real factory application case, the performance of PFSOFA is verified to be better than four classical multiobjective optimization algorithms for solving the multiobjective FJSP constructed in this article. And for the factory case, PFSOFA also obtained the better schemes.
Keyword ：
Distance prospect function Distance prospect function immediate predecessor operations immediate predecessor operations multiobjective flexible job shop multiobjective flexible job shop optimal foraging algorithm (OFA) optimal foraging algorithm (OFA) Pythagorean fuzzy set (PFS) Pythagorean fuzzy set (PFS)
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| GB/T 7714 | Wang, Hao-Jie , Zhu, Guang-Yu . Multiobjective Optimization for FJSP Under Immediate Predecessor Constraints Based OFA and Pythagorean Fuzzy Set [J]. | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2023 , 31 (9) : 3108-3120 . |
| MLA | Wang, Hao-Jie 等. "Multiobjective Optimization for FJSP Under Immediate Predecessor Constraints Based OFA and Pythagorean Fuzzy Set" . | IEEE TRANSACTIONS ON FUZZY SYSTEMS 31 . 9 (2023) : 3108-3120 . |
| APA | Wang, Hao-Jie , Zhu, Guang-Yu . Multiobjective Optimization for FJSP Under Immediate Predecessor Constraints Based OFA and Pythagorean Fuzzy Set . | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2023 , 31 (9) , 3108-3120 . |
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For the multi-objective flow shop scheduling problem in the supply chain environment, this paper proposes the Fuzzy Relevance Entropy method (FREM) to solve the adaptive value assignment problem in the multi-objective optimization process of the supply chain environment by combining Fuzzy Information Entropy Theory (FIET) and Degree of Membership Function (DMF). Firstly, the uncertainty of each sub-objective of the ideal solution and Pareto solution of the objective is extracted using the Degree of Membership Function. Secondly, each solution is mapped into an affiliation degree fuzzy set and the information contained in the fuzzy set is reprocessed using Fuzzy Information Entropy Theory. Finally, the amount of information contained in the ideal solution solved by the Pareto method is used to guide the evolution of the Particle Swarm Optimization (PSO) algorithm, thus avoiding the traditional multi-objective optimization process of assigning weights to solve the fitness link. This paper combines both the Fuzzy Relevance Entropy method and the Stochastic Weight method with Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms to address the five-objective flow shop scheduling problem in the supply chain environment. Experimental results demonstrate that the proposed Fuzzy Relevance Entropy method effectively solves the multi-objective flow shop scheduling problem in the supply chain environment and achieves better optimization results compared to the Stochastic Weight method.
Keyword ：
flow shop scheduling flow shop scheduling Fuzzy information entropy strategy Fuzzy information entropy strategy multi-objective optimization multi-objective optimization Particle swarm optimization (PSO) Particle swarm optimization (PSO) Supply chain Supply chain
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| GB/T 7714 | Luo, Zhe , Tan, Yonghong , Zhu, Guangyu et al. Research on multi-objective flow shop scheduling optimization in supply chain environment based on Fuzzy Relevance Entropy Method [J]. | ADVANCES IN MECHANICAL ENGINEERING , 2023 , 15 (12) . |
| MLA | Luo, Zhe et al. "Research on multi-objective flow shop scheduling optimization in supply chain environment based on Fuzzy Relevance Entropy Method" . | ADVANCES IN MECHANICAL ENGINEERING 15 . 12 (2023) . |
| APA | Luo, Zhe , Tan, Yonghong , Zhu, Guangyu , Xia, Yuping , Wang, Xinyu . Research on multi-objective flow shop scheduling optimization in supply chain environment based on Fuzzy Relevance Entropy Method . | ADVANCES IN MECHANICAL ENGINEERING , 2023 , 15 (12) . |
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