为解决现有粒子群改进策略无法帮助已陷入局部最优和过早收敛的粒子恢复寻优性能的问题,提出一种陷阱标记联合懒蚂蚁的自适应粒子群优化(adaptive particle swarm optimization based on trap label and lazy ant, TLLA-APSO)算法。陷...为解决现有粒子群改进策略无法帮助已陷入局部最优和过早收敛的粒子恢复寻优性能的问题,提出一种陷阱标记联合懒蚂蚁的自适应粒子群优化(adaptive particle swarm optimization based on trap label and lazy ant, TLLA-APSO)算法。陷阱标记策略为粒子群提供动态速度增量,使其摆脱最优解的束缚。利用懒蚂蚁寻优策略多样化粒子速度,提升种群多样性。通过惯性认知策略在速度更新中引入历史位置,增加粒子的路径多样性和提升粒子的探索性能,使粒子更有效地避免陷入新的局部最优。理论证明了引入历史位置的粒子群算法的收敛性。仿真实验结果表明,所提算法不仅能有效解决粒子群已陷入局部最优和过早收敛的问题,且与其他算法相比,具有较快的收敛速度和较高的寻优精度。展开更多
图G(V,E)的邻点可约全标号(adjacent vertex reducible total labeling,AVRTL)是一个从V(G)∪E(G)到连续整数集{1,2,…,|V(G)|+|E(G)|}的双射,且图中所有相邻同度顶点的标号之和均相同,为S(u)=f(u)+∑uw∈E(G)f(uw).该文结合现实问题,...图G(V,E)的邻点可约全标号(adjacent vertex reducible total labeling,AVRTL)是一个从V(G)∪E(G)到连续整数集{1,2,…,|V(G)|+|E(G)|}的双射,且图中所有相邻同度顶点的标号之和均相同,为S(u)=f(u)+∑uw∈E(G)f(uw).该文结合现实问题,借鉴传统遗传算法、蜂群算法等智能算法思路,设计了一种新型的AVRTL算法,通过预处理函数、调整函数等,利用循环迭代寻优的方式得到有限点内所有双圈图的邻点可约全标号结果.对实验结果进行分析,发现几类图的标号规律,总结得到若干定理并给出证明,最后给出猜想:所有的双圈图均为AVRTL图.展开更多
The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challengi...The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challenging.Feature selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant features.The ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection,because of its simplicity,efficiency,and similarity to reinforcement learning.Nevertheless,existing methods do not consider crucial correlation information,such as dynamic redundancy and label correlation.To tackle these concerns,the paper proposes a multi-label feature selection technique based on ant colony optimization algorithm(MFACO),focusing on dynamic redundancy and label correlation.Initially,the dynamic redundancy is assessed between the selected feature subset and potential features.Meanwhile,the ant colony optimization algorithm extracts label correlation from the label set,which is then combined into the heuristic factor as label weights.Experimental results demonstrate that our proposed strategies can effectively enhance the optimal search ability of ant colony,outperforming the other algorithms involved in the paper.展开更多
文摘为解决现有粒子群改进策略无法帮助已陷入局部最优和过早收敛的粒子恢复寻优性能的问题,提出一种陷阱标记联合懒蚂蚁的自适应粒子群优化(adaptive particle swarm optimization based on trap label and lazy ant, TLLA-APSO)算法。陷阱标记策略为粒子群提供动态速度增量,使其摆脱最优解的束缚。利用懒蚂蚁寻优策略多样化粒子速度,提升种群多样性。通过惯性认知策略在速度更新中引入历史位置,增加粒子的路径多样性和提升粒子的探索性能,使粒子更有效地避免陷入新的局部最优。理论证明了引入历史位置的粒子群算法的收敛性。仿真实验结果表明,所提算法不仅能有效解决粒子群已陷入局部最优和过早收敛的问题,且与其他算法相比,具有较快的收敛速度和较高的寻优精度。
文摘图G(V,E)的邻点可约全标号(adjacent vertex reducible total labeling,AVRTL)是一个从V(G)∪E(G)到连续整数集{1,2,…,|V(G)|+|E(G)|}的双射,且图中所有相邻同度顶点的标号之和均相同,为S(u)=f(u)+∑uw∈E(G)f(uw).该文结合现实问题,借鉴传统遗传算法、蜂群算法等智能算法思路,设计了一种新型的AVRTL算法,通过预处理函数、调整函数等,利用循环迭代寻优的方式得到有限点内所有双圈图的邻点可约全标号结果.对实验结果进行分析,发现几类图的标号规律,总结得到若干定理并给出证明,最后给出猜想:所有的双圈图均为AVRTL图.
基金supported by National Natural Science Foundation of China(Grant Nos.62376089,62302153,62302154,62202147)the key Research and Development Program of Hubei Province,China(Grant No.2023BEB024).
文摘The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challenging.Feature selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant features.The ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection,because of its simplicity,efficiency,and similarity to reinforcement learning.Nevertheless,existing methods do not consider crucial correlation information,such as dynamic redundancy and label correlation.To tackle these concerns,the paper proposes a multi-label feature selection technique based on ant colony optimization algorithm(MFACO),focusing on dynamic redundancy and label correlation.Initially,the dynamic redundancy is assessed between the selected feature subset and potential features.Meanwhile,the ant colony optimization algorithm extracts label correlation from the label set,which is then combined into the heuristic factor as label weights.Experimental results demonstrate that our proposed strategies can effectively enhance the optimal search ability of ant colony,outperforming the other algorithms involved in the paper.
