Improved Fruit Fly Optimization Algorithm for Solving Lot-Streaming Flow-Shop Scheduling Problem

An improved fruit fly optimization algorithm( iFOA) is proposed for solving the lot-streaming flow-shop scheduling problem( LSFSP) with equal-size sub-lots. In the proposed iFOA,a solution is encoded as two vectors to determine the splitting of jobs and the sequence of the sub-lots simultaneously. Based on the encoding scheme,three kinds of neighborhoods are developed for generating new solutions. To well balance the exploitation and exploration,two main search procedures are designed within the evolutionary search framework of the iFOA,including the neighborhood-based search( smell-vision-based search) and the global cooperation-based search. Finally,numerical testing results are provided,and the comparisons demonstrate the effectiveness of the proposed iFOA for solving the LSFSP.

Binary Fruit Fly Swarm Algorithms for the Set Covering Problem

Currently,the industry is experiencing an exponential increase in dealing with binary-based combinatorial problems.In this sense,metaheuristics have been a common trend in the field in order to design approaches to solve them successfully.Thus,a well-known strategy consists in the use of algorithms based on discrete swarms transformed to perform in binary environments.Following the No Free Lunch theorem,we are interested in testing the performance of the Fruit Fly Algorithm,this is a bio-inspired metaheuristic for deducing global optimization in continuous spaces,based on the foraging behavior of the fruit fly,which usually has much better sensory perception of smell and vision than any other species.On the other hand,the Set Coverage Problem is a well-known NP-hard problem with many practical applications,including production line balancing,utility installation,and crew scheduling in railroad and mass transit companies.In this paper,we propose different binarization methods for the Fruit Fly Algorithm,using Sshaped and V-shaped transfer functions and various discretization methods to make the algorithm work in a binary search space.We are motivated with this approach,because in this way we can deliver to future researchers interested in this area,a way to be able to work with continuous metaheuristics in binary domains.This new approach was tested on benchmark instances of the Set Coverage Problem and the computational results show that the proposed algorithm is robust enough to produce good results with low computational cost.

An Inverse Power Generation Mechanism Based Fruit Fly Algorithm for Function Optimization

As a novel population-based optimization algorithm, fruit fly optimization(FFO) algorithm is inspired by the foraging behavior of fruit flies and possesses the advantages of simple search operations and easy implementation. Just like most population-based evolutionary algorithms, the basic FFO also suffers from being trapped in local optima for function optimization due to premature convergence.In this paper, an improved FFO, named IPGS-FFO, is proposed in which two novel strategies are incorporated into the conventional FFO. Specifically, a smell sensitivity parameter together with an inverse power generation mechanism(IPGS) is introduced to enhance local exploitation. Moreover,a dynamic shrinking search radius strategy is incorporated so as to enhance the global exploration over search space by adaptively adjusting the searching area in the problem domain. The statistical performance of FFO, the proposed IPGS-FFO, three state-of-the-art FFO variants, and six metaheuristics are tested on twenty-six well-known unimodal and multimodal benchmark functions with dimension 30, respectively. Experimental results and comparisons show that the proposed IPGS-FFO achieves better performance than three FFO variants and competitive performance against six other meta-heuristics in terms of the solution accuracy and convergence rate.

Seasonal Least Squares Support Vector Machine with Fruit Fly Optimization Algorithm in Electricity Consumption Forecasting

Electricity is the guarantee of economic development and daily life. Thus, accurate monthly electricity consumption forecasting can provide reliable guidance for power construction planning. In this paper, a hybrid model in combination of least squares support vector machine(LSSVM) model with fruit fly optimization algorithm(FOA) and the seasonal index adjustment is constructed to predict monthly electricity consumption. The monthly electricity consumption demonstrates a nonlinear characteristic and seasonal tendency. The LSSVM has a good fit for nonlinear data, so it has been widely applied to handling nonlinear time series prediction. However, there is no unified selection method for key parameters and no unified method to deal with the effect of seasonal tendency. Therefore, the FOA was hybridized with the LSSVM and the seasonal index adjustment to solve this problem. In order to evaluate the forecasting performance of hybrid model, two samples of monthly electricity consumption of China and the United States were employed, besides several different models were applied to forecast the two empirical time series. The results of the two samples all show that, for seasonal data, the adjusted model with seasonal indexes has better forecasting performance. The forecasting performance is better than the models without seasonal indexes. The fruit fly optimized LSSVM model outperforms other alternative models. In other words, the proposed hybrid model is a feasible method for the electricity consumption forecasting.

An Adaptive Fruit Fly Optimization Algorithm for Optimization Problems

In this paper, we present a new fruit fly optimization algorithm with the adaptive step for solving unconstrained optimization problems, which is able to avoid the slow convergence and the tendency to fall into local optimum of the standard fruit fly optimization algorithm. By using the information of the iteration number and the maximum iteration number, the proposed algorithm uses the floor function to ensure that the fruit fly swarms adopt the large step search during the olfactory search stage which improves the search speed;in the visual search stage, the small step is used to effectively avoid local optimum. Finally, using commonly used benchmark testing functions, the proposed algorithm is compared with the standard fruit fly optimization algorithm with some fixed steps. The simulation experiment results show that the proposed algorithm can quickly approach the optimal solution in the olfactory search stage and accurately search in the visual search stage, demonstrating more effective performance.

面向无线异构传感器网络的三维覆盖研究

【目的】为达到增强无线异构传感器网络(HWSN)三维覆盖能力的目的,提出了一种基于改进蜜獾优化算法(IHBA)的无线异构传感器网络三维部署方法。【方法】首先,结合自适应果蝇优化算法,增强算法的随机搜索性,便于算法得到全局最优解,然后引入替换最差个体策略,避免适应度过低的个体占据种群位置,提高算法收敛速度,同时引入新个体提高种群多样性,避免算法个体早熟。【结果】将该算法应用于无线异构传感器网络的覆盖优化,相比标准蜜獾算法,其网络覆盖率提升18.1%。【结论】仿真结果表明,该算法收敛速度更快,可以有效提高无线异构传感器的网络覆盖能力,整个网络的节点分布也更加均匀。

基于果蝇优化算法的风功率预测数据可信度量化分析

针对风功率预测数据精度较低,可信度难以度量的问题,研究了基于果蝇优化算法的风功率预测数据可信度量化分析方法。采用一次指数平滑方法平滑处理风功率预测的历史风速数据,将完成平滑处理的数据输入LSSVM风功率预测模型中,该模型设置线性最小二乘系统作为支持向量机的损失函数。选取果蝇优化算法优化LSSVM风功率预测模型,设置风功率预测的均方根误差作为果蝇优化算法的适应度函数,获取LSSVM风功率预测模型的最优参数,量化分析了风功率预测数据可信度。实验结果表明,该方法预测风功率的均方根误差低于0.3,具有较高的风功率预测数据可信度。

换热站并联水泵分布式优化控制

针对现有换热站并联水泵优化算法在集中式架构下控制适应性不足的问题,本文提出了一种改进的分布式并联水泵优化算法.首先,建立了并联水泵的分布式控制系统,并对该优化问题的数学模型进行描述,在目标函数中引入自适应非线性因子;然后,设计了改进的分布式果蝇优化算法,在该算法中每台水泵的控制器仅通过与邻居控制器交互信息即可完成并联水泵的优化;并且,在嗅觉搜索阶段,使用正弦余弦策略替代赋予个体距离与方向的随机策略;最后,以两个实际换热站中不同并联水泵系统为例对算法进行仿真验证,并基于仿真结果进行性能分析.结果表明,相较于传统算法,改进的分布式果蝇优化算法能得到更优的控制策略,有着收敛速度快、稳定性好和鲁棒性强的特点;并且该算法适用于不同系统的并联水泵优化问题,具有可扩展性.在实际工程验证中相较于集中式算法,该算法在总功率和计算时间上分别平均降低了5.47%和29.90%,因此,能够满足实际换热站中对并联水泵热负荷优化分配的需求.

基于FOA优化PID参数的永磁同步电机转速控制

为提高永磁同步电机转速控制的效果,提出一种基于果蝇优化算法(Fruit Fly Optimization Algorithm,FOA)优化比例积分微分控制(proportional-integral-derivative control)的方法。其中,以PMSM调速系统为背景,构建调速系统的PID方法,然后构建FOA优化PID参数的PID控制器,以实现永磁同步电机转速系统的自适应控制。仿真结果表明,FOA-PID方法具有响应速度快、超调小、抗干扰能力和速度调节能力强的特点,在固定负载改变转速条件下,仅需0.02 s即可达到稳定状态,有效改善了永磁同步电机转速控制系统的控制性能;相较于标准PID和RBF-PID方法,FOA-PID方法在正向起动转速情况下的超调量为1.36%,分别低5.73%和1.12%;在负向起动转速情况下,FOA-PID方法的最大超调量为0.56%,分别低15.13%和8.22%。由此得出,本FOA-PID方法可行,具有一定的优越性。

面向大型客机翼身对接的增强现实跟踪注册方法研究

飞机大部件的装配质量往往对整机的装配质量起决定性作用。随着增强现实技术的发展,可对大部件装配对接过程涉及的多源异构数据以图形或数字形式进行直观展示,辅助工艺人员更好地完成复杂装配任务。为了将虚拟信息叠加到现实世界中,需要对大部件的位姿进行准确定位。然而在实际翼身对接过程中,大部件本身的复杂性以及装配环境的限制导致传统的跟踪注册方法在应对这些挑战时出现定位精度不高、计算效率较低的问题。因此,文中提出了新的跟踪注册方法。首先,基于Unity Perception构建了图像数据集;然后,基于Harris和尺度不变特征变换法(Scale Invariant Feature Transform,SIFT)实现了特征点识别和模板匹配;最后,基于果蝇优化算法优化了匹配阈值,并通过翼身对接模拟实验平台验证了该方法的有效性,新方法的总误差百分比平均值仅为2.0069%。该方法解决了大部件跟踪注册定位中的关键问题,为增强现实技术在飞机装配领域的进一步应用提供了更为可靠的支持。

基于FOA-BP-AdaBoost的大坝变形预测模型及应用

为提升大坝变形监测预测精度,解决变形量受多因素影响等问题,笔者提出了基于果蝇优化算法(FOA)、BP神经网络的AdaBoost强预测组合模型(FOA-BP-AdaBoost),并与BP神经网络模型、FOA-BP神经网络模型应用于工程实例中的预测精度进行多方位量化对比。结果表明:强预测模型集齐了果蝇算法全局优化、BP神经网络局部寻优和AdaBoost“优中选优”的特点,最大程度优化了预测效果;实例应用证实了FOA-BP-AdaBoost模型在大坝变形预测领域的准确性和有效性。该模型已成功应用于工程实例,可为类似工程提供参考。

基于相似日和EM-ELM的短期光伏功率预测

光伏功率精准预测有助于电网调度中心提前编制出科学的调度计划,从而提升经济效益。提出一种基于相似日理论结合极限学习机(extreme learning machine,ELM)的光伏功率预测模型。首先,采用皮尔森相关系数法挑选出与光伏出力相关程度最强的气象特征;其次,采用模糊C均值聚类法(fuzzy-c-means algorithm,FCM)将历史样本数据按照所选的气象特征划分相似日;最后,根据预测日天气类型选取对应相似日数据样本集,利用增量型极限学习机进行预测,将影响光伏出力的主要天气特征量作为输入,光伏发电功率作为输出,并采用改进的果蝇优化算法对极限学习机隐含层参数进行优化。预测结果表明,提出的方法对光伏功率预测精度有明显的提升。

基于主成分分析的果蝇算法优化支持向量机回归的红枣产量预测

随着大数据技术和人工智能的快速发展,针对当前红枣产量预测模型精度低、模型优化时间过长等问题,以山西省1993—2020年的红枣产量及17个维度的因素作为基础数据,提出一种基于主成分分析的果蝇算法优化支持向量机回归(principal component analysis-fruit fly optimization algorithm-support vector regression,PCA-FOA-SVR)的红枣产量预测模型。首先利用主成分分析(principal component analysis,PCA)对数据进行降维处理,以5维的指标作为输入变量,产量作为输出变量;其次以支持向量机回归(support vector regression,SVR)为基础模型,利用果蝇优化算法(fruit fly optimization algorithm,FOA)对SVR参数惩罚因子c和核函数参数g进行寻优,构建PCA-FOA-SVR模型。对试验结果进行验证。发现PCA-FOA-SVR的均方根误差(root mean square error,RMSE)、平均绝对误差(mean absolute error,MAE)、决定系数R 2分别为3.11、3.01、0.96,SVR的各指标分别为5.33、4.07、0.9,分别提高了41.7%、26%、6.7%,最后通过GM(1,1)对各维度的数据进行预测,利用PCA-FOA-SVR模型对未来10年山西省红枣产量进行预测,结果显示在2025年红枣产量会达到一个峰值,对后续相关研究提供了一定的科学依据。

基于IFS-FOA-ELM的网络安全态势预测方法

针对传统的网络安全态势预测方法在解决复杂网络环境下各种不确定性问题时往往准确性较差,不能快速准确预测网络安全态势变化趋势的问题,提出基于IFS-FOA-ELM的网络安全态势预测方法。基于直觉模糊集,构建网络安全信息特征要素,并计算出网络安全态势值。构建ELM预测模型,并利用果蝇优化算法对ELM的输入权值与阈值进行优化。最后,利用某互联网中心大数据环境下网络安全态势数据,验证IFS-FOA-ELM预测模型的精度与实时度。仿真结果表明:所提算法能够快速有效对网络安全态势进行预测。

基于FOA-BP的风电运维人员不安全行为风险评价

为评价风电运维人员不安全行为风险,减少行为安全事故的发生,在确定不安全行为风险因素集合的基础上构建不安全行为风险评价指标体系,利用果蝇优化算法调整BP神经网络权值和阈值,建立基于FOA-BP的不安全行为风险评价模型。以某风电场为例收集数据对模型进行测试,实现不安全行为风险评价并计算风险评价指标权重。结果表明:该模型预测性能较好,能够评价不安全行为风险,企业可依据评价指标、指标权重值及风险等级制定针对性的防控措施。

基于混合果蝇算法的桩锚支护深基坑临界滑面搜索

进行基坑整体稳定性分析常采用极限平衡法,但仍然需要依据经验试算一系列滑面,将安全系数最小的滑面确定为最危险滑面.针对此问题,提出将果蝇优化(FOA)算法与禁忌搜索(TS)算法融合,提出自适应步长的混合果蝇优化算法(HFOA),以克服基本果蝇算法局部寻优精度不高且易陷入局部最优的缺点,确保获得全局最优解,并结合简化Bishop算法用于临界滑面的搜索.在Matlab中编程实现该算法,通过与6种启发式算法进行对比,结果表明,HFOA适用于均质土悬臂支护基坑、成层土和含软弱夹层的桩锚支护基坑,相较于遗传算法等6种算法具有更快的收敛速度、更高的收敛精度和可靠性,为深基坑临界滑动面搜索提供了一种新的求解策略.

基于果蝇优化算法的加速度计温控系统研究

针对硅微谐振式加速度计在复杂的常温环境下输出不稳定问题,设计了基于改进的模糊PID控制的加速度计温控系统。通过对加热片的热分析和加速度计的温度场仿真,验证了温控系统加热片放置的有效性。采用果蝇优化算法(FOA)和积分分离对模糊PID控制进行优化,给出了优化后温控系统的整体设计。搭建温控系统实验装置,对优化方案进行验证。与PID控制相比,改进的模糊PID控制算法使得温控系统收敛时间减少到60s,超调量减少了9.8℃,稳态误差减少到0.1℃。与无温控相比,加速度计在40℃恒温下启动20min后的零偏稳定性值减少了71.4%。实验结果表明:基于改进模糊PID控制的温控系统可显著提高温度控制的精度。

基于果蝇算法的永磁同步电机多目标优化设计

为了提高永磁同步电机(PMSM)的性能,本文以一台72槽60极永磁同步电机为例,针对永磁同步电机多目标优化过程中多次有限元迭代导致的计算时间长和优化效率低的问题,提出了一种基于果蝇优化算法(FOA)的多目标优化方法。选取磁钢尺寸作为优化变量,以电机平均转矩、转矩波动和齿槽转矩作为优化目标,采用权重系数的多目标优化函数。首先通过有限元仿真获得各变量的样本空间,其次采用广义回归神经网络(GRNN)对仿真数据集进行拟合训练,得到非线性模型,然后运用FOA进行优化。最后,通过有限元仿真分析,结果表明FOA能有效抑制转矩波动以及增大平均转矩,且具有参数设置少、收敛速度快等优点,具有较好的应用价值。

基于果蝇算法的无线传感网络信标节点定位

在无线传感网络中,多个节点需要协同工作来实现节点定位。但在传播过程中,信号受反射、散射等因素的影响,导致信号路径复杂、不确定性大,增加了信标节点定位的难度。因此,提出一种果蝇算法下无线传感网络信标节点精准定位方法。通过建立无线传感网络拓扑模型,分析节点结构,根据构建的模型获取无线传感网络信标节点的定位优化目标函数。通过改进果蝇算法求解定位优化目标函数,实现无线传感网络信标节点的精准定位。实验结果表明,所提算法的无线传感网络信标节点定位准确度在85%以上,且定位误差在0.1-0.4之间,表明所提算法定位精度高、整体应用效果好。

An improved fruit fly optimization algorithm for solving traveling salesman problem

The traveling salesman problem(TSP), a typical non-deterministic polynomial(NP) hard problem, has been used in many engineering applications. As a new swarm-intelligence optimization algorithm, the fruit fly optimization algorithm(FOA) is used to solve TSP, since it has the advantages of being easy to understand and having a simple implementation. However, it has problems, including a slow convergence rate for the algorithm, easily falling into the local optimum, and an insufficient optimization precision. To address TSP effectively, three improvements are proposed in this paper to improve FOA. First, the vision search process is reinforced in the foraging behavior of fruit flies to improve the convergence rate of FOA. Second, an elimination mechanism is added to FOA to increase the diversity. Third, a reverse operator and a multiplication operator are proposed. They are performed on the solution sequence in the fruit fly's smell search and vision search processes, respectively. In the experiment, 10 benchmarks selected from TSPLIB are tested. The results show that the improved FOA outperforms other alternatives in terms of the convergence rate and precision.