The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various ...The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives.The Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ)is an effective approach for solving the multi-objective job shop scheduling problem.Nevertheless,it has some limitations in solving scheduling problems,including inadequate global search capability,susceptibility to premature convergence,and challenges in balancing convergence and diversity.To enhance its performance,this paper introduces a strengthened dominance relation NSGA-Ⅲ algorithm based on differential evolution(NSGA-Ⅲ-SD).By incorporating constrained differential evolution and simulated binary crossover genetic operators,this algorithm effectively improves NSGA-Ⅲ’s global search capability while mitigating pre-mature convergence issues.Furthermore,it introduces a reinforced dominance relation to address the trade-off between convergence and diversity in NSGA-Ⅲ.Additionally,effective encoding and decoding methods for discrete job shop scheduling are proposed,which can improve the overall performance of the algorithm without complex computation.To validate the algorithm’s effectiveness,NSGA-Ⅲ-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test instances.The experimental results demonstrate that NSGA-Ⅲ-SD achieves better solution quality and diversity,proving its effectiveness in solving the multi-objective job shop scheduling problem.展开更多
针对传统的蚂蚁算法容易出现早熟和停滞现象,提出一种新型蚂蚁算法(new ant colony algorithm,NACA),即将转移规则、全局信息素灾变规则和局部混合调整信息素规则。选择几个典型TSP问题进行实验。研究结果表明:新型蚂蚁算法一方面提高...针对传统的蚂蚁算法容易出现早熟和停滞现象,提出一种新型蚂蚁算法(new ant colony algorithm,NACA),即将转移规则、全局信息素灾变规则和局部混合调整信息素规则。选择几个典型TSP问题进行实验。研究结果表明:新型蚂蚁算法一方面提高了算法种群的多样性,同时将轮盘赌算子利用到城市转移规则中,有利于提高算法的收敛速度;另一方面,将种群个体的差分信息应用于局部信息素更新规则中,有利于搜索全局解;最后灾变算子避免算法陷入局部最优,而达到全局最优。新型的蚁群算法具有更强的搜索全局最优解的能力以及更好的稳定性和收敛性,同时为解决其他优化问题提供新的思路。展开更多
基金in part supported by the Key Research and Development Project of Hubei Province(Nos.2020BAB1141,2023BAB094)the Key Project of Science and Technology Research ProgramofHubei Educational Committee(No.D20211402)+1 种基金the Teaching Research Project of Hubei University of Technology(No.XIAO2018001)the Project of Xiangyang Industrial Research Institute of Hubei University of Technology(No.XYYJ2022C04).
文摘The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives.The Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ)is an effective approach for solving the multi-objective job shop scheduling problem.Nevertheless,it has some limitations in solving scheduling problems,including inadequate global search capability,susceptibility to premature convergence,and challenges in balancing convergence and diversity.To enhance its performance,this paper introduces a strengthened dominance relation NSGA-Ⅲ algorithm based on differential evolution(NSGA-Ⅲ-SD).By incorporating constrained differential evolution and simulated binary crossover genetic operators,this algorithm effectively improves NSGA-Ⅲ’s global search capability while mitigating pre-mature convergence issues.Furthermore,it introduces a reinforced dominance relation to address the trade-off between convergence and diversity in NSGA-Ⅲ.Additionally,effective encoding and decoding methods for discrete job shop scheduling are proposed,which can improve the overall performance of the algorithm without complex computation.To validate the algorithm’s effectiveness,NSGA-Ⅲ-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test instances.The experimental results demonstrate that NSGA-Ⅲ-SD achieves better solution quality and diversity,proving its effectiveness in solving the multi-objective job shop scheduling problem.
文摘针对自动化仓库自动导引车(automated guided vehicle,AGV)调度问题,文章在考虑车辆载重约束的情况下,建立车辆行驶总距离和总能耗最小为目标的数学模型,并通过离散差分进化算法与蚁群算法相结合的混合算法进行求解。将混合算法与改进蚁群算法、遗传算法、模拟退火算法、粒子群算法在CVRPLIB SET P算例集上的求解结果进行对比,验证该混合算法的有效性;通过数值仿真实验对提出的自动化分拣仓库AGV调度问题进行求解,证明该混合算法对实际算例有较好的求解结果,可以有效提高自动化仓库作业效率。
文摘针对传统的蚂蚁算法容易出现早熟和停滞现象,提出一种新型蚂蚁算法(new ant colony algorithm,NACA),即将转移规则、全局信息素灾变规则和局部混合调整信息素规则。选择几个典型TSP问题进行实验。研究结果表明:新型蚂蚁算法一方面提高了算法种群的多样性,同时将轮盘赌算子利用到城市转移规则中,有利于提高算法的收敛速度;另一方面,将种群个体的差分信息应用于局部信息素更新规则中,有利于搜索全局解;最后灾变算子避免算法陷入局部最优,而达到全局最优。新型的蚁群算法具有更强的搜索全局最优解的能力以及更好的稳定性和收敛性,同时为解决其他优化问题提供新的思路。
基金国家教育部新世纪人才支持计划( the New Century Excellent Talent Foundation from MOE of China under Grant No.NCET- 05- 0734)广东省自然科学基金(the Natural Science Foundation of Guangdong Province of China under Grant No.04020079)+2 种基金 南京大学软件国家重点实验室开放基金( No.200603) 东南大学移动通讯国家重点实验室开放基金( No.A200605) 广东省科技厅科技攻关项目( No.2005B10101010)
