To solve dynamic obstacle avoidance problems, a novel algorithm was put forward with the advantages of wireless sensor network (WSN). In view of moving velocity and direction of both the obstacles and robots, a mathem...To solve dynamic obstacle avoidance problems, a novel algorithm was put forward with the advantages of wireless sensor network (WSN). In view of moving velocity and direction of both the obstacles and robots, a mathematic model was built based on the exposure model, exposure direction and critical speeds of sensors. Ant colony optimization (ACO) algorithm based on bionic swarm intelligence was used for solution of the multi-objective optimization. Energy consumption and topology of the WSN were also discussed. A practical implementation with real WSN and real mobile robots were carried out. In environment with multiple obstacles, the convergence curve of the shortest path length shows that as iterative generation grows, the length of the shortest path decreases and finally reaches a stable and optimal value. Comparisons show that using sensor information fusion can greatly improve the accuracy in comparison with single sensor. The successful path of robots without collision validates the efficiency, stability and accuracy of the proposed algorithm, which is proved to be better than tradition genetic algorithm (GA) for dynamic obstacle avoidance in real time.展开更多
Evolutionary algorithms(EAs)were shown to be effective for complex constrained optimization problems.However,inflexible exploration in general EAs would lead to losing the global optimum nearby the ill-convergence reg...Evolutionary algorithms(EAs)were shown to be effective for complex constrained optimization problems.However,inflexible exploration in general EAs would lead to losing the global optimum nearby the ill-convergence regions.In this paper,we propose an iterative dynamic diversity evolutionary algorithm(IDDEA)with contractive subregions guiding exploitation through local extrema to the global optimum in suitable steps.In IDDEA,a novel optimum estimation strategy with multi-agents evolving diversely is suggested to e?ciently compute dominance trend and establish a subregion.In addition,a subregion converging iteration is designed to redistrict a smaller subregion in current subregion for next iteration,which is based on a special dominance estimation scheme.Meanwhile,an infimum penalty function is embedded into IDDEA to judge agents and penalize adaptively the unfeasible agents with the lowest fitness of feasible agents.Furthermore,several engineering design optimization problems taken from the specialized literature are successfully solved by the present algorithm with high reliable solutions.展开更多
With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality pred...With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.展开更多
针对传统人工操控塔式起重机在运输货物时易导致路径拐点多、负载摆动大的问题,提出一种改进的人工鱼群塔式起重机智能路径规划的新算法。根据塔式起重机的工作环境,建立三维的地图环境模型来模拟障碍物较多的复杂建筑环境,并结合起重...针对传统人工操控塔式起重机在运输货物时易导致路径拐点多、负载摆动大的问题,提出一种改进的人工鱼群塔式起重机智能路径规划的新算法。根据塔式起重机的工作环境,建立三维的地图环境模型来模拟障碍物较多的复杂建筑环境,并结合起重机在建筑场所的运行特点,对传统人工鱼群算法(artificial fish swarm algorithm, AFSA)进行改进,采用自适应策略让鱼群在寻优过程中的状态不断变化,及时调整自身的移动步长和视野,并基于生存竞争机制对人工鱼的随机行为进行改进,在一定程度上改善了算法的寻优能力,利用三次方样条数据插值拟合曲线得到更适合塔式起重机的光滑避障路径。仿真结果表明,改进后的算法为塔式起重机在障碍物较多的复杂建筑环境下找到一条最优避障路径。展开更多
针对二维基于特征分解的多重信号分类(Multiple Signal Classificaion,MUSIC)算法在多谱峰搜索时计算量大、估计失败率高以及传统蚁群算法在进行二维多谱峰搜索时无法同时搜索多个谱峰的问题,将蚁群算法进行改进,同时与聚类思想相结合,...针对二维基于特征分解的多重信号分类(Multiple Signal Classificaion,MUSIC)算法在多谱峰搜索时计算量大、估计失败率高以及传统蚁群算法在进行二维多谱峰搜索时无法同时搜索多个谱峰的问题,将蚁群算法进行改进,同时与聚类思想相结合,加上动态调整搜索范围,使得改进后的蚁群算法可以进行二维MUSIC多谱峰搜索,同时可以分辨出相距较近的信号源的波达方向。通过仿真验证了改进后的蚁群算法在一定信噪比下进行谱峰搜索成功率高,鲁棒性强,且不受信号源距离大小的影响,证明了该算法适合进行多谱峰搜索的任务。展开更多
基金Project(60475035) supported by the National Natural Science Foundation of China
文摘To solve dynamic obstacle avoidance problems, a novel algorithm was put forward with the advantages of wireless sensor network (WSN). In view of moving velocity and direction of both the obstacles and robots, a mathematic model was built based on the exposure model, exposure direction and critical speeds of sensors. Ant colony optimization (ACO) algorithm based on bionic swarm intelligence was used for solution of the multi-objective optimization. Energy consumption and topology of the WSN were also discussed. A practical implementation with real WSN and real mobile robots were carried out. In environment with multiple obstacles, the convergence curve of the shortest path length shows that as iterative generation grows, the length of the shortest path decreases and finally reaches a stable and optimal value. Comparisons show that using sensor information fusion can greatly improve the accuracy in comparison with single sensor. The successful path of robots without collision validates the efficiency, stability and accuracy of the proposed algorithm, which is proved to be better than tradition genetic algorithm (GA) for dynamic obstacle avoidance in real time.
基金Supported by National Natural Science Foundation of China(61074020)
文摘Evolutionary algorithms(EAs)were shown to be effective for complex constrained optimization problems.However,inflexible exploration in general EAs would lead to losing the global optimum nearby the ill-convergence regions.In this paper,we propose an iterative dynamic diversity evolutionary algorithm(IDDEA)with contractive subregions guiding exploitation through local extrema to the global optimum in suitable steps.In IDDEA,a novel optimum estimation strategy with multi-agents evolving diversely is suggested to e?ciently compute dominance trend and establish a subregion.In addition,a subregion converging iteration is designed to redistrict a smaller subregion in current subregion for next iteration,which is based on a special dominance estimation scheme.Meanwhile,an infimum penalty function is embedded into IDDEA to judge agents and penalize adaptively the unfeasible agents with the lowest fitness of feasible agents.Furthermore,several engineering design optimization problems taken from the specialized literature are successfully solved by the present algorithm with high reliable solutions.
基金supported by the National Science and Technology Innovation 2030 Next-Generation Artifical Intelligence Major Project(2018AAA0101801)the National Natural Science Foundation of China(72271188)。
文摘With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.
文摘针对传统人工操控塔式起重机在运输货物时易导致路径拐点多、负载摆动大的问题,提出一种改进的人工鱼群塔式起重机智能路径规划的新算法。根据塔式起重机的工作环境,建立三维的地图环境模型来模拟障碍物较多的复杂建筑环境,并结合起重机在建筑场所的运行特点,对传统人工鱼群算法(artificial fish swarm algorithm, AFSA)进行改进,采用自适应策略让鱼群在寻优过程中的状态不断变化,及时调整自身的移动步长和视野,并基于生存竞争机制对人工鱼的随机行为进行改进,在一定程度上改善了算法的寻优能力,利用三次方样条数据插值拟合曲线得到更适合塔式起重机的光滑避障路径。仿真结果表明,改进后的算法为塔式起重机在障碍物较多的复杂建筑环境下找到一条最优避障路径。
文摘针对二维基于特征分解的多重信号分类(Multiple Signal Classificaion,MUSIC)算法在多谱峰搜索时计算量大、估计失败率高以及传统蚁群算法在进行二维多谱峰搜索时无法同时搜索多个谱峰的问题,将蚁群算法进行改进,同时与聚类思想相结合,加上动态调整搜索范围,使得改进后的蚁群算法可以进行二维MUSIC多谱峰搜索,同时可以分辨出相距较近的信号源的波达方向。通过仿真验证了改进后的蚁群算法在一定信噪比下进行谱峰搜索成功率高,鲁棒性强,且不受信号源距离大小的影响,证明了该算法适合进行多谱峰搜索的任务。
