基于改进人工蜂群算法的冗余机器臂逆解研究

针对具有移动关节的7自由度机械臂逆运动学求解困难的问题,提出了一种基于改进人工蜂群算法的逆运动学求解方法。首先根据冗余机械臂的物理结构简化出几何模型,利用D-H法建立运动学模型,由坐标系变换得到正运动学的解。由于该冗余机械臂不满足Pieper准则,难以用传统方法求得封闭解;因此采用改进人工蜂群智能算法,利用位置误差与姿态误差的标准差作为目标函数,求取逆运动学的最优解,并利用Matlab编程进行仿真验证,仿真结果表明该方法准确有效,为具有移动关节的冗余机械臂逆运动学的求解提供了一种新的途径。     

群体智能优化算法的研究进展与展望

针对群体智能算法在结构优化设计领域中的应用，介绍了当前存在的一些群体智能算法，包括蚁群算法、鱼群算法和粒子群算法，阐述了其工作原理和特点，同时，对该算法在结构优化设计中的应用发展进行了展望。     

Design of tall bridge piers by ant colony optimization

This paper describes a methodology for the analysis and design of Reinforced Concrete (RC) tall bridge piers with hollow rectangular sections, which are typically used in deep valley bridge viaducts. Piers are usually considered tall when the shaft has a height of 50 m or more. Three different types of rectangular hollow tall piers have been studied for road piers of 90.00 m in height: RTRA90, RLON90 and RLT90. RTRA90 has the two side walls inclined, RLON90 has the two frontal walls inclined and RLT90 has all four walls inclined. The procedure used in the present study to solve the combinatorial problem is a variant of the ant colony optimization. RTRA90 leads to the most economical pier, both in column and foundation cost, since it is the most efficient set up for horizontal loads. Regarding the cost of the vertical column only, i.e. excluding the foundation, the cost of RTRA90 and RLON90 are similar, but the cost of the column RLT90 is higher due to its larger unit cost of interior formwork.     

Antlion Algorithm Optimized Fuzzy PID Supervised On-line Recurrent Fuzzy Neural Network Based Controller for Brushless DC Motor

In this paper, Antlion algorithm optimized Fuzzy PID supervised on-line Recurrent Fuzzy Neural Network based controller is proposed for the speed control of Brushless DC motor. Learning parameters of the supervised on-line recurrent fuzzy neural network controller, i.e., learning rate (η), dynamic factor (α), and number nodes (N_i) are optimized using Genetic algorithm, Particle Swarm optimization, Ant colony optimization, Bat algorithm, and Antlion algorithm. The proposed controller is tested with different operating conditions of the Brushless DC motor, such as varying load conditions and varying set speed conditions. The time domain specifications such as rise time, overshoot, undershoot, settling time, recovery time, and steady state error and also integral performance indices such as root mean square error, integral of absolute error, integral of squared error, and integral of time multiplied absolute error are measured and compared for above optimized controller. Simulation results show Antlion algorithm optimized Fuzzy PID supervised on-line recurrent fuzzy neural network based controller has proved to be superior than other considered controllers in all aspects. In addition, the experimental verification of proposed control system is presented to test the effectiveness of the proposed controller with different operating conditions of the Brushless DC motor.     

基于AFSA-ACA自适应模糊滑模的采煤机滚筒液压调高控制

为研究采煤机滚筒在不同复杂工况下的位置跟踪控制,以液压机构的工作原理为基础,建立了滚筒液压调高机构的数学模型。基于自适应模糊微分积分滑模鲁棒性强的优点,提出了在鱼群-蚁群融合算法优化滑模控制器参数条件下的滚筒位置跟踪控制方案,并分析了滚筒在采用一般滑模控制器及自适应模糊微分积分滑模控制器下位置跟踪的特点。仿真结果表明:采用结合融合算法优化的自适应模糊微分积分滑模控制器的滚筒调高机构对内部参数摄动及外部时变干扰具有较好的鲁棒性,能够实现位置跟踪控制。     

Intelligent Optimization Methods for the Design of an Overhead Travelling Crane

In design optimization of crane metal structures, present approaches are based on simple models and mixed variables, which are difficult to use in practice and usually lead to failure of optimized results for rounding variables. Crane metal structure optimal design(CMSOD) belongs to a constrained nonlinear optimization problem with discrete variables. A novel algorithm combining ant colony algorithm with a mutation-based local search(ACAM) is developed and used for a real CMSOD for the first time. In the algorithm model, the encoded mode of continuous array elements is introduced. This not only avoids the need to round optimization design variables during mixed variable optimization, but also facilitates the construction of heuristic information, and the storage and update of the ant colony pheromone. Together with the proposed ACAM, a genetic algorithm(GA) and particle swarm optimization(PSO) are used to optimize the metal structure of a crane. The optimization results show that the convergence speed of ACAM is approximately 20% of that of the GA and around 11% of that of the PSO. The objective function value given by ACAM is 22.23% less than the practical design value, a reduction of 16.42% over the GA and 3.27% over the PSO. The developed ACAM is an effective intelligent method for CMSOD and superior to other methods.     

基于群智能加权核聚类的水电机组故障诊断

针对核聚类中核参数选择依赖经验,最优聚类中心难以有效获取的问题,提出了一种仿电磁蜂群加权核聚类算法。首先,考虑不同特征对聚类结果的影响,对样本进行加权处理,利用核空间的Xie-Beni指标建立加权核聚类模型;然后,提出并引入仿电磁蜂群算法求解聚类模型,实现聚类中心、特征权重与核参数的同步寻优。利用该方法分别对3组标准测试样本集以及水电机组故障样本进行聚类测试,并与传统方法进行对比分析。试验结果表明,提出的仿电磁蜂群加权核聚类算法较传统聚类方法具有更高的精度,能够有效实现水电机组振动故障的准确聚类与识别,完成故障诊断。     

A novel artificial bee colony detection algorithm for massive MIMO system

As a hot‐spot of 5G, the research on detection algorithms for massive multiple input multiple output (MIMO) system is significant but difficult. The traditional MIMO detection algorithms or their improvements are not appropriate for large scaled antennas. In this paper, we propose artificial bee colony (ABC) detection algorithm for massive MIMO system. As one advanced technology of swarm intelligence, ABC algorithm is most efficient for large scaled constrained numerical combinatorial optimization problem. Therefore, we employ it to search the optimum solution vector in the modulation alphabet with linear detection result as initial. Simulation and data analysis prove the correctness and efficiency. Versus the scale of massive MIMO systems from 64 × 64 to 1024 × 1024 with uncoded four‐quadrature‐amplitude‐modulation signals, the proposed ABC detection algorithm obtains bit error rate of 10 ^− 5 at low average received signal‐to‐noise‐ratio of 12 dB with rapid convergence rate, which approximates the optimum bit error rate performance of the maximum likelihood and achieves the theoretical optimum spectral efficiency with low required average received signal‐to‐noise‐ratio of 10 dB in similar increasing regularity, over finite time of low polynomial computational complexity of per symbol, where N_T denotes the transmitting antennas' number. The proposed ABC detection algorithm is efficient for massive MIMO system. Copyright © 2016 John Wiley & Sons, Ltd.     

Controller Design Using Ant Colony Algorithm for a Non-inverting Buck–Boost Chopper Based on a Detailed Average Model

In this article, the non-inverting buck–boost converter and its operation modes are scrutinized. The closed-loop stability of the converter in buck and boost modes is analyzed, and the necessity of using an appropriated controller is demonstrated. Then the application of an adapted ant colony optimization to design a feedback controller is proposed, and a controller based on its existing model is tuned. Simulation and experimental results obtained from the ant colony optimization designed controller are then compared with a controller designed with the classic method. Although the simulation and experimental results prove the efficiency of the proposed control approach, a significant difference between controller behavior in practice and simulation is obvious. Finding these differences, more detailed models, including all parasitic elements, in the buck and boost modes are derived. Applying the proposed model in controller design illustrates that the desired performance of the converter can be guaranteed with a simple proportional-integral (PI) controller. The suggested ant colony-based controller is again tuned based on the more detailed model, which improves the performance of the converter system even more. Furthermore, good agreement between analytical and experimental outputs validates the accuracy of the modeling and simulation.     

考虑多目标优化模型的含分布式电源的无功优化

在配电网的调压过程中,将能够发出无功功率的分布式电源与电容器相结合,分析含分布式电源的配电网无功优化的问题,建立有功网损最小、静态电压稳定裕度最大的数学模型。在此基础上,应用超效率数据包分析评价方法,明确各目标函数的权重组合方案,把双目标无功优化问题转换成单目标规划问题。并且运用一种新颖的智能优化算法—细菌菌落算法,解决分布式电源在配电网中的无功优化问题。细菌菌落算法根据单群体菌落生长演化过程来寻找最优解,建立了细菌菌落的生成和死亡的寻优机制,并提供了一种新的算法结束方式。通过IEEE-33测试系统验证该算法具有良好实用性和适应性,并且也验证了所提模型的实际意义。