Multi-objective Trajectory Planning Method based on the Improved Elitist Non-dominated Sorting Genetic Algorithm

Robot manipulators perform a point-point task under kinematic and dynamic constraints.Due to multi-degreeof-freedom coupling characteristics,it is difficult to find a better desired trajectory.In this paper,a multi-objective trajectory planning approach based on an improved elitist non-dominated sorting genetic algorithm(INSGA-II)is proposed.Trajectory function is planned with a new composite polynomial that by combining of quintic polynomials with cubic Bezier curves.Then,an INSGA-II,by introducing three genetic operators:ranking group selection(RGS),direction-based crossover(DBX)and adaptive precision-controllable mutation(APCM),is developed to optimize travelling time and torque fluctuation.Inverted generational distance,hypervolume and optimizer overhead are selected to evaluate the convergence,diversity and computational effort of algorithms.The optimal solution is determined via fuzzy comprehensive evaluation to obtain the optimal trajectory.Taking a serial-parallel hybrid manipulator as instance,the velocity and acceleration profiles obtained using this composite polynomial are compared with those obtained using a quintic B-spline method.The effectiveness and practicability of the proposed method are verified by simulation results.This research proposes a trajectory optimization method which can offer a better solution with efficiency and stability for a point-to-point task of robot manipulators.

Improved Genetic Optimization Algorithm with Subdomain Model for Multi-objective Optimal Design of SPMSM

For an optimal design of a surface-mounted permanent magnet synchronous motor(SPMSM),many objective functions should be considered.The classical optimization methods,which have been habitually designed based on magnetic circuit law or finite element analysis(FEA),have inaccuracy or calculation time problems when solving the multi-objective problems.To address these problems,the multi-independent-population genetic algorithm(MGA)combined with subdomain(SD)model are proposed to improve the performance of SPMSM such as magnetic field distribution,cost and efficiency.In order to analyze the flux density harmonics accurately,the accurate SD model is first established.Then,the MGA with time-saving SD model are employed to search for solutions which belong to the Pareto optimal set.Finally,for the purpose of validation,the electromagnetic performance of the new design motor are investigated by FEA,comparing with the initial design and conventional GA optimal design to demonstrate the advantage of MGA optimization method.

Study on Optimization of Urban Rail Train Operation Control Curve Based on Improved Multi-Objective Genetic Algorithm

A multi-objective improved genetic algorithm is constructed to solve the train operation simulation model of urban rail train and find the optimal operation curve.In the train control system,the conversion point of operating mode is the basic of gene encoding and the chromosome composed of multiple genes represents a control scheme,and the initial population can be formed by the way.The fitness function can be designed by the design requirements of the train control stop error,time error and energy consumption.the effectiveness of new individual can be ensured by checking the validity of the original individual when its in the process of selection,crossover and mutation,and the optimal algorithm will be joined all the operators to make the new group not eliminate on the best individual of the last generation.The simulation result shows that the proposed genetic algorithm comparing with the optimized multi-particle simulation model can reduce more than 10%energy consumption,it can provide a large amount of sub-optimal solution and has obvious optimization effect.

The Objective Function Value Optimization of Cloud Computing Resources Security Allocation of Artificial Firefly Algorithm

Based on the current cloud computing resources security distribution model’s problem that the optimization effect is not high and the convergence is not good, this paper puts forward a cloud computing resources security distribution model based on improved artificial firefly algorithm. First of all, according to characteristics of the artificial fireflies swarm algorithm and the complex method, it incorporates the ideas of complex method into the artificial firefly algorithm, uses the complex method to guide the search of artificial fireflies in population, and then introduces local search operator in the firefly mobile mechanism, in order to improve the searching efficiency and convergence precision of algorithm. Simulation results show that, the cloud computing resources security distribution model based on improved artificial firefly algorithm proposed in this paper has good convergence effect and optimum efficiency.

Optimized deployment of a radar network based on an improved firefly algorithm

The threats and challenges of unmanned aerial vehicle(UAV) invasion defense due to rapid UAV development have attracted increased attention recently. One of the important UAV invasion defense methods is radar network detection. To form a tight and reliable radar surveillance network with limited resources, it is essential to investigate optimized radar network deployment. This optimization problem is difficult to solve due to its nonlinear features and strong coupling of multiple constraints. To address these issues, we propose an improved firefly algorithm that employs a neighborhood learning strategy with a feedback mechanism and chaotic local search by elite fireflies to obtain a trade-off between exploration and exploitation abilities. Moreover, a chaotic sequence is used to generate initial firefly positions to improve population diversity. Experiments have been conducted on 12 famous benchmark functions and in a classical radar deployment scenario. Results indicate that our approach achieves much better performance than the classical firefly algorithm(FA) and four recently proposed FA variants.

Dynamic Self-Adaptive Double Population Particle Swarm Optimization Algorithm Based on Lorenz Equation

In order to improve some shortcomings of the standard particle swarm optimization algorithm, such as premature convergence and slow local search speed, a double population particle swarm optimization algorithm based on Lorenz equation and dynamic self-adaptive strategy is proposed. Chaotic sequences produced by Lorenz equation are used to tune the acceleration coefficients for the balance between exploration and exploitation, the dynamic self-adaptive inertia weight factor is used to accelerate the converging speed, and the double population purposes to enhance convergence accuracy. The experiment was carried out with four multi-objective test functions compared with two classical multi-objective algorithms, non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results show that the proposed algorithm has excellent performance with faster convergence rate and strong ability to jump out of local optimum, could use to solve many optimization problems.

基于改进萤火虫算法的配电网故障恢复策略

为提高含高比例新能源主动配电网的故障恢复可靠性,提出一种基于改进萤火虫算法的两阶段故障恢复策略。首先构建了风光储系统模型,减少了风光发电的不确定性对故障恢复的影响;第一阶段采用先广度后深度的组合算法对故障后的配电网进行孤岛划分,该组合算法充分考虑了负荷时变性及用户侧需求,保证了重要负荷被优先恢复;第二阶段采用改进萤火虫算法对配电网重构进行求解,最大限度地保证恢复供电,同时尽可能地减少网络损耗;最后以IEEE33节点配电网作为算例仿真,结果表明所提故障恢复策略能够得到配电网故障恢复最优方案,同时满足负荷用户侧需求,保证重要负荷不断电,提高供电恢复率,降低配电网故障后网损,验证了所述方法的有效性和优越性。

基于改进遗传算法的机械臂时间最优轨迹优化

针对传统遗传算法在优化机械臂轨迹时间时存在易陷入局部最优、后期局部搜索精度不高等问题,提出了一种改进型遗传算法。使用Tent映射初始化种群,增强遗传算法全局搜索能力。引入模拟退火算法对每一代子代种群进行局部退火操作,提升算法跳出局部最优的能力,在算法后期使用萤火虫算法替代常规变异算子,提高算法后期局部搜索精度。以aubo-i10协作机械臂为对象进行时间优化仿真,结果表明,改进后的遗传算法优化时间相比传统遗传算法减少了约1.2 s,并使机械臂各关节的角速度、角加速度曲线光滑连续,且满足最大约束条件,证明了算法的可行性和有效性。

一种改进萤火虫算法的指印图像分割方法

为提高指印图像准确率,有效提取指印采集图像中与指印相关的部分,提出一种改进萤火虫算法的指印图像分割方法。实验结果表明,与对比算法相比,该算法成功降低了时间复杂度,减少了计算所需的时间,并且算法表现稳定。

具有劳动异质性的订单分批优化研究

合理的订单分批和快速的拣选商品对于配送的时效性具有重要的意义。针对O2O超市仓储配送中心的订单分批问题展开研究,为了使具有劳动异质性的拣选员工更快地拣选订单商品和保证高效,同时使拣选员工工作更加均衡,构建了一个双目标混合整数规划模型,其目标是拣选员工行走的最小距离和拣选员工的工作量最平衡。应用了一种动态自适应步长萤火虫算法,证明了所提模型和目标之间的相关性。通过数据实验验证模型和算法的有效性,结果表明该模型对订单分批和具有劳动异质性的员工具有较强的影响,为提高超市配送的时效性提供了科学依据。

关联驱动下配电网同期线损异常数据辨识

配电网同期线损数据的可靠性对于有效实现电网降损与节能是非常关键的,辨识异常数据能够提升配电网同期线损数据的可靠性。为此,设计了关联驱动下配电网同期线损异常数据辨识方法。采用基于多值属性的关联规则挖掘算法,挖掘配电网同期线损数据。利用改进小波阈值去噪算法,对挖掘的配电网同期线损数据实施去噪处理。基于K-means聚类算法、改进型萤火虫算法与聚类可靠性评估指标,设计线损异常数据辨识模型,实现配电网同期线损异常数据辨识。测试结果表明,设计方法的平均误辨识点数和漏辨识点数分别低于10个和5个,平均相对辨识误差保持在1.0以下,具有较好的同期线损异常数据辨识性能。

基于改进萤火虫群算法的大气污染源自动定位方法研究

由于传统污染源定位方法的定位结果严重偏离实际情况。因此,文章研究一种基于改进萤火虫群算法的大气污染源自动定位方法。为筛选大气污染监测点,计算空气污染指数并采集污染浓度数据。构建三维扩散模型以模拟预测污染浓度。基于实际监测与预测结果,构建目标函数,将污染源参数反演转为目标函数求解。改进萤火虫群算法,将固定步长改为动态,用于求解目标函数。最优解即为污染源定位结果。结果表明:所研究方法应用下,偏离度值相对更小,说明该方法的定位结果更接近真实结果,定位准确性高。

铁路货车车钩钩舌组装机械手移动自动智能控制

铁路货车车钩钩舌组装以人工为主,智能化程度低,为此设计铁路货车车钩钩舌组装机械手移动自动智能控制方法。首先分析伺服电机控制机构,确定机械手控制相关参数。然后通过名义模型改进机械手动力学约束模型,获取机械手关节位置控制约束误差,将关节位置控制误差输入PID控制器中,利用改进萤火虫算法对PID控制器参数进行优化,实现铁路货车车钩钩舌组装机械手移动自动控制。实验结果表明,所提方法能够有效减小机械手位姿跟踪误差,平缓控制力矩波动,机械手移动控制效果好。

基于雾计算的电网消纳与储能优化调度算法设计

针对可再生能源的发电量使用率较低、电网运行经济性不理想等问题,设计一种基于雾计算的电网消纳与储能优化调度算法。在雾计算技术的基础上,构建电网调度系统,以分散化地就近处理海量终端数据。同时,以可再生能源的消纳率最大化及运行成本最小化为目标,利用改进萤火虫算法进行求解,从而得到最佳的电网调度方案。基于IEEE-9节点系统对所提算法进行的实验结果表明,应用该算法,系统的可再生能源消纳率、运行成本分别为92.58%和48.13万元,能够有效调控储能电站并实现能源的最大化消纳。

萤火虫算法结合人工势场法的机器人路径规划

鉴于已有机器人全局路径规划算法存在的易出现目标不可达问题、计算效率较低、所规划路径平滑性欠佳等不足,提出将新型智能优化算法——萤火虫算法与传统人工势场法相结合的最优全局路径规划算法。首先,根据已知全局地图,利用算法结构简单、计算量较小、运算速度较快的人工势场法作为初始化引导因子对萤火虫算法参数进行初始化,再以极坐标系代替直角坐标系利用萤火虫算法对规划路径进行寻优求解。极坐标法能自动舍弃规划路径上的冗余点,增强所规划路径的平滑性;同时考虑到传统萤火虫算法所存在的不足,对萤火虫算法进行了相应的改进:引入自适应步长改进随机步长,加快算法的收敛,并以混沌逻辑改进萤火虫算法的吸收系数,避免算法收敛到局部最优,在一定程度上解决人工势场法所存在局部震荡和目标不可达问题。实验结果表明所提算法实现效率高、避障效果好,安全可靠性好,最优路径更加接近理想路径,平均误差在0.08 m以下,能够很好地完成路径规划的目标。

改进的萤火虫算法在神经网络中的应用

基本萤火虫算法存在容易陷入局部最优及收敛速度低的问题,提出了一种改进进化机制的萤火虫算法(IEMFA)。在群体进化过程中赋予萤火虫改进的位置移动策略,并利用改进后的萤火虫算法来优化传统BP神经网络的网络参数。测试结果表明,基于改进萤火虫算法的BP神经网络具有更好的收敛速度和精度。

基于改进萤火虫算法优化BP神经网络的水电站厂房振动预测

利用萤火虫算法优化BP神经网络权值和阈值基础上,建立水电站厂房振动响应预测模型。针对萤火虫算法存在的收敛速度慢、易陷入局部最优等问题,引入动态随机局部搜索机制加快收敛速度,对当前最优解进行变异操作避免陷入局部最优,提出动态步长更新措施提高计算精度,改进最优解振荡问题。仿真实例表明,基于改进萤火虫算法优化的BP网络模型预测精度和收敛速度等性能得到明显改善,可用于水电站厂房结构振动响应预测。

基于改进萤火虫算法神经网络的刮板输送机减速器故障诊断

为了对刮板输送机减速器故障进行准确诊断研究,提出了一种基于改进萤火虫算法优化神经网络故障诊断方法。首先对刮板输送机减速器故障特征参数进行特征提取,其次应用特征数据样本进行基于神经网络的故障诊断模型训练,利用改进萤火虫算法对神经网络权值、阈值进行优化,加快目标的优化求解,得到最优的网络模型。初步研究表明将改进萤火虫算法与BP(back propagation)神经网络结合可以有效地解决神经网络收敛速度慢,易陷入局部最优等问题,可以对刮板输送机减速器的故障进行准确诊断。

基于改进萤火虫算法的T-S模型辨识

提出改进萤火虫算法的T-S模型辨识方法。针对传统T-S模型辨识方法中将前件参数和后件参数分开辨识而不能全局优化辨识的缺点,应用改进萤火虫算法对前件参数和后件参数整体编码整体辨识。改进萤火虫算法是在原始算法基础上对吸引度系数作自适应变化,目的是增强算法在迭代初期的搜索能力,防止其陷入局部极值点,并降低算法在迭代后期在最优解附近的振荡,以提高解的精度。提出的方法能较好地找到全局最优解,具有较高的辨识精度。仿真示例证明了改进方法的有效性。

基于改进萤火虫算法的矿井水害避灾路径规划

矿井水害是煤矿生产中的一种突发性灾害。为了有效地预防矿井突水事故,降低突水事故中的人员伤亡和财产损失,提出了一种基于改进萤火虫算法的矿井水害避灾路径规划。本文针对标准萤火虫算法收敛效果不佳、易陷入局部最优并结合突水环境中路径规划的特点,通过改变和设计萤火虫算法的初始化方式、萤火虫之间的距离、最大亮度、相对亮度等要素,引入考虑逃生巷道干扰因素的当量长度,并调整搜索策略,实现了对避灾路径规划的最优设计。本文结合王家岭煤矿实例将改进的萤火虫算法与A*算法进行比较,仿真结果表明:改进后的萤火虫算法具有较好的收敛性和稳定性,路径更优,具有大范围搜索优化的能力,在发生矿井突水时可有效帮助巷道受困人员脱离危险。