Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In ...Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints （TALB-MC）. Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization （TLBO） algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost fimction. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search （VNS） is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm （LAHC） for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.展开更多
教-学优化算法是一种新型启发式优化算法。针对教-学优化算法容易陷入局部最优的不足,提出了一种改进教-学优化算法(an improved teaching-learning based optimization,AITLBO)。在教学阶段通过扰动机制提高教师的教学效果,避免算法陷...教-学优化算法是一种新型启发式优化算法。针对教-学优化算法容易陷入局部最优的不足,提出了一种改进教-学优化算法(an improved teaching-learning based optimization,AITLBO)。在教学阶段通过扰动机制提高教师的教学效果,避免算法陷入局部最优。在学习阶段初期分别采取较差学生向优秀学生动态随机学习和优秀学生重新向教师随机学习的策略使当前解向最优方向进化,避免较差解破坏较优解的结构,提高了学习阶段学生的学习效率。在学习阶段后期引入了学生自我反思的学习策略,实现算法对局部信息的精细搜索,提高算法对解空间信息开发的能力,避免了算法因过早收敛易陷入局部最优的不足。将其与目前较优的几种改进TLBO算法和其他启发式优化算法进行性能测试对比,结果表明AITLBO算法具有较高的寻优精度和较快的收敛速度。展开更多
The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this wor...The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.展开更多
为解决教与学优化算法容易早熟收敛的问题,在原算法的基础上提出一种基于混合策略改进的教与学优化算法(Mixed Strategy Based Improved Teaching-Learning Based Optimization,M-SITLBO)。首先,利用Logistic-Tent混沌映射策略初始化种...为解决教与学优化算法容易早熟收敛的问题,在原算法的基础上提出一种基于混合策略改进的教与学优化算法(Mixed Strategy Based Improved Teaching-Learning Based Optimization,M-SITLBO)。首先,利用Logistic-Tent混沌映射策略初始化种群,保证种群的多样性;其次,在教师和学生阶段分别引入黄金正弦算法和基于莱维飞行与对数螺旋线的搜索策略优化个体的位置更新公式,增强并平衡算法的全局和局部收敛性能;最后,设计仿真对其寻优性能进行测试,结果表明改进后的教与学优化算法寻优速度、精度以及稳定性显著提升,且具有较强跳出局部最优的能力。展开更多
基金Supported by National Natural Science Foundation of China(Grant Nos.51275366,50875190,51305311)Specialized Research Fund for the Doctoral Program of Higher Education of China(Grant No.20134219110002)
文摘Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints （TALB-MC）. Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization （TLBO） algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost fimction. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search （VNS） is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm （LAHC） for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.
文摘教-学优化算法是一种新型启发式优化算法。针对教-学优化算法容易陷入局部最优的不足,提出了一种改进教-学优化算法(an improved teaching-learning based optimization,AITLBO)。在教学阶段通过扰动机制提高教师的教学效果,避免算法陷入局部最优。在学习阶段初期分别采取较差学生向优秀学生动态随机学习和优秀学生重新向教师随机学习的策略使当前解向最优方向进化,避免较差解破坏较优解的结构,提高了学习阶段学生的学习效率。在学习阶段后期引入了学生自我反思的学习策略,实现算法对局部信息的精细搜索,提高算法对解空间信息开发的能力,避免了算法因过早收敛易陷入局部最优的不足。将其与目前较优的几种改进TLBO算法和其他启发式优化算法进行性能测试对比,结果表明AITLBO算法具有较高的寻优精度和较快的收敛速度。
基金Projects(61573144,61773165,61673175,61174040)supported by the National Natural Science Foundation of ChinaProject(222201717006)supported by the Fundamental Research Funds for the Central Universities,China
文摘The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.
文摘为解决教与学优化算法容易早熟收敛的问题,在原算法的基础上提出一种基于混合策略改进的教与学优化算法(Mixed Strategy Based Improved Teaching-Learning Based Optimization,M-SITLBO)。首先,利用Logistic-Tent混沌映射策略初始化种群,保证种群的多样性;其次,在教师和学生阶段分别引入黄金正弦算法和基于莱维飞行与对数螺旋线的搜索策略优化个体的位置更新公式,增强并平衡算法的全局和局部收敛性能;最后,设计仿真对其寻优性能进行测试,结果表明改进后的教与学优化算法寻优速度、精度以及稳定性显著提升,且具有较强跳出局部最优的能力。
