The flying foxes optimization(FFO)algorithm,as a newly introduced metaheuristic algorithm,is inspired by the survival tactics of flying foxes in heat wave environments.FFO preferentially selects the best-performing in...The flying foxes optimization(FFO)algorithm,as a newly introduced metaheuristic algorithm,is inspired by the survival tactics of flying foxes in heat wave environments.FFO preferentially selects the best-performing individuals.This tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search area.To address this issue,the paper introduces an opposition-based learning-based search mechanism for FFO algorithm(IFFO).Firstly,this paper introduces niching techniques to improve the survival list method,which not only focuses on the adaptability of individuals but also considers the population’s crowding degree to enhance the global search capability.Secondly,an initialization strategy of opposition-based learning is used to perturb the initial population and elevate its quality.Finally,to verify the superiority of the improved search mechanism,IFFO,FFO and the cutting-edge metaheuristic algorithms are compared and analyzed using a set of test functions.The results prove that compared with other algorithms,IFFO is characterized by its rapid convergence,precise results and robust stability.展开更多
针对商用车车架制造商中纵梁以及总装的生产工艺的多样性和生产调度的复杂性,以最小化最大完工时间、物料积压程度和耗电量为优化目标,提出了一个NSGA-Ⅱ和红狐算法的混合算法(hybrid algorithm of non-dominant sorting genetic algori...针对商用车车架制造商中纵梁以及总装的生产工艺的多样性和生产调度的复杂性,以最小化最大完工时间、物料积压程度和耗电量为优化目标,提出了一个NSGA-Ⅱ和红狐算法的混合算法(hybrid algorithm of non-dominant sorting genetic algorithm and red fox algorithm,HNSGA2RFA),用于解决多目标的柔性流水车间调度问题。通过ROV规则实现GA和RFA的编码转换,并提出了归一化分组策略(normalized grouping strategy)。试验结果表明,HNSGA2RFA算法在优化速度和最优解集数量上均优于原NSGA-Ⅱ算法。展开更多
采用非稳腔结构的激光器,当系统的菲涅耳数较大时,球面腔镜尺寸随之增大,尽管可以获得高输出功率,但由于不满足傍轴条件,球面腔镜的球差对输出模式的影响变大。采用Fox-Li迭代算法,分析了腔镜类型不同时谐振腔有效菲涅耳数为675的2 k W...采用非稳腔结构的激光器,当系统的菲涅耳数较大时,球面腔镜尺寸随之增大,尽管可以获得高输出功率,但由于不满足傍轴条件,球面腔镜的球差对输出模式的影响变大。采用Fox-Li迭代算法,分析了腔镜类型不同时谐振腔有效菲涅耳数为675的2 k W射频板条CO_2激光器的输出模式,并进行了实验研究。结果表明,当腔镜采用双球面镜时球差的影响显著,输出光束近似为球面波,输出平面上光束质量因子M!2=14.48,光束质量差,聚焦后光束偏离光轴,难以实现高功率、高光束质量的激光输出;当腔镜均采用抛物面镜时球差的影响得以消除,输出光束近似为平面波,此时输出平面上光束质量得到改善,M!2=3.96,实验结果与数值模拟结果一致。展开更多
基金support from the Ningxia Natural Science Foundation Project(2023AAC03361).
文摘The flying foxes optimization(FFO)algorithm,as a newly introduced metaheuristic algorithm,is inspired by the survival tactics of flying foxes in heat wave environments.FFO preferentially selects the best-performing individuals.This tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search area.To address this issue,the paper introduces an opposition-based learning-based search mechanism for FFO algorithm(IFFO).Firstly,this paper introduces niching techniques to improve the survival list method,which not only focuses on the adaptability of individuals but also considers the population’s crowding degree to enhance the global search capability.Secondly,an initialization strategy of opposition-based learning is used to perturb the initial population and elevate its quality.Finally,to verify the superiority of the improved search mechanism,IFFO,FFO and the cutting-edge metaheuristic algorithms are compared and analyzed using a set of test functions.The results prove that compared with other algorithms,IFFO is characterized by its rapid convergence,precise results and robust stability.
文摘针对商用车车架制造商中纵梁以及总装的生产工艺的多样性和生产调度的复杂性,以最小化最大完工时间、物料积压程度和耗电量为优化目标,提出了一个NSGA-Ⅱ和红狐算法的混合算法(hybrid algorithm of non-dominant sorting genetic algorithm and red fox algorithm,HNSGA2RFA),用于解决多目标的柔性流水车间调度问题。通过ROV规则实现GA和RFA的编码转换,并提出了归一化分组策略(normalized grouping strategy)。试验结果表明,HNSGA2RFA算法在优化速度和最优解集数量上均优于原NSGA-Ⅱ算法。
文摘采用非稳腔结构的激光器,当系统的菲涅耳数较大时,球面腔镜尺寸随之增大,尽管可以获得高输出功率,但由于不满足傍轴条件,球面腔镜的球差对输出模式的影响变大。采用Fox-Li迭代算法,分析了腔镜类型不同时谐振腔有效菲涅耳数为675的2 k W射频板条CO_2激光器的输出模式,并进行了实验研究。结果表明,当腔镜采用双球面镜时球差的影响显著,输出光束近似为球面波,输出平面上光束质量因子M!2=14.48,光束质量差,聚焦后光束偏离光轴,难以实现高功率、高光束质量的激光输出;当腔镜均采用抛物面镜时球差的影响得以消除,输出光束近似为平面波,此时输出平面上光束质量得到改善,M!2=3.96,实验结果与数值模拟结果一致。
