An Opposition-Based Learning-Based Search Mechanism for Flying Foxes Optimization Algorithm

The flying foxes optimization(FFO)algorithm,as a newly introduced metaheuristic algorithm,is inspired by the survival tactics of flying foxes in heat wave environments.FFO preferentially selects the best-performing individuals.This tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search area.To address this issue,the paper introduces an opposition-based learning-based search mechanism for FFO algorithm(IFFO).Firstly,this paper introduces niching techniques to improve the survival list method,which not only focuses on the adaptability of individuals but also considers the population’s crowding degree to enhance the global search capability.Secondly,an initialization strategy of opposition-based learning is used to perturb the initial population and elevate its quality.Finally,to verify the superiority of the improved search mechanism,IFFO,FFO and the cutting-edge metaheuristic algorithms are compared and analyzed using a set of test functions.The results prove that compared with other algorithms,IFFO is characterized by its rapid convergence,precise results and robust stability.

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.

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.

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.

基于启发式on-the-fly的扩展TGBA模型检测算法

以广义Büchi自动机为研究对象,对其作判空检测能为解决系统的状态空间爆炸问题提供有效途径.但广义Büchi自动机难以适用于安全苛求计算机系统中,只需满足某个可接受条件子集便可作出非空性判断,进而能判断出系统的安全性的情形.文中提出了基于启发式on-the-fly的扩展TGBA模型检测算法,该算法采用ETGBA模型,通过启发式on-the-fly判空检测方法对ETGBA作判空检测时,加强了对不能构成其可接受运行的结点的处理,节省了内存空间,提高了检测效率,从而能较快地作出非空性判断.通过算法的正确性证明及复杂度分析、实验比较与实例研究验证了所提出算法的正确性与实际可行性.与已有算法相比,该算法的通用性更强,当应用于广义Büchi自动机的判空检测时,其时空性能均优于已有算法.

基于on-the-fly的Petri网模型检查技术研究与实现

Petri网是一种应用非常广泛的建模工具,它能深刻、简洁地描述控制系统,特别是能较好地描述并发系统的结构,并能对系统的动态性质进行分析。在探讨了Petri网的模型检查的基础上,采用双DFS算法,对基于Petri网的模型检查的算法进行了改进,提出了针对Petri网的on-the-fly算法,同时给出了基于on-the-fly的Petri网模型检查的实现和测试,从而可以有效地对Petri网表示的系统模型进行模型检查。

Flying-V型仓储布局货位分配优化方法研究

Flying-V是一种典型的非传统布局方式,根据其布局方式的特性,针对仓储货位分配优化问题,以货物出入库效率最高和货物存放的重心最低为优化目标,建立了货位分配多目标优化模型,并采用自适应策略的遗传算法(GA),以及粒子群算法(PSO)进行求解。根据货位分配的优化特点,在GA算法的选择、交叉和变异环节均采用自适应策略,同时采用惯性权重线性递减的方法设计了PSO算法,有效地解决了两种算法收敛速度慢和易"早熟"的问题,提高了算法的寻优性能。为了更好地表现两种优化求解算法的有效性和优越性,结合具体的货位分配实例利用MATLAB软件编程实现。通过对比分析优化结果表明,PSO算法在收敛速度和优化效果方面相比于自适应GA算法更具有优势,更加合适于解决Flying-V型仓储布局货位分配优化问题。

Flying-V型仓储布局下的拣货路径优化

为了提高现代仓库作业中的拣货作业这一核心环节的拣货效率,提出了一种以蚁群算法作为优化算法来解决仓库订单拣选的路径优化问题。这种算法采用正反馈机制,其使得搜索过程不断收敛,最终逼近最优解。而根据Flying-V型仓库布局的特点,建立了订单拣选优化路径模型,并通过仿真实验,对传统穿越策略,S形启发式算法,蚁群算法三种算法的性能进行了比较,比较结果表明蚁群算法优化拣货路径可以明显地缩短拣货距离,大大提高拣货效率,在实际运用中也具有良好的实用性。

Fly算法基本代数分析

借鉴对Rijndael进行代数分析时写方程的方法,对Fly算法进行了基本的代数分析,给出GF(24)上的一个明密对的高次方程的代数次数,同时给出了GF(2)上的二次方程组的形式代数项数和线性独立方程的个数,并分析了它们之间的联系与区别。

存储系统数据通道的On-The-Fly纠错

对于高性能、高可靠性存储系统，其控制器须具备实时在线检纠错能力。为了便于硬件实现ＯＴＦ（Ｏｎ－Ｔｈｅ－Ｆｌｙ）检纠错，本文提出了ＲＳ（Ｒｅｅｄ－Ｓｏｌｏｍｎ）码译码的并行Ｂｅｒｌｅｋａｍｐ算法，并设计了实现算法的模块化纠错硬件单元和相应控制逻辑。根据不同应用场合对纠错能力的要求，可以对这些模块化纠错硬件单元进行灵活的组合。

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X87指令集的浮点除法运算单元设计

基于X87指令集架构,在TSMC 65nm的工艺库下,采用SRT-16算法设计一种浮点除法运算单元。SRT-16算法主要是通过采用重叠商与余数计算部分的结构来进行优化处理,使得在原有传统算法SRT-4的基础上增加的电路面积较少,并且使得循环次数减少一半,并且在处理商的结果中引入商的飞速转换技术。通过对基于SystemVerilog自动对比平台的搭建,通过C对比模型,完成自动对比,加速验证的速度,进行功能覆盖统计,完成覆盖率100%。通过DC综合检测电路的时序情况,对电路进行时序优化,完成满足时序要求1.08ns,工作频率为900MHz。

一种基于智能算法的GNSS高程拟合方法

广义回归神经网络(GRNN)是一种新型的前馈神经网络模型,具有训练次数少、耗时短、非线性参数的预报能力较强等优点。但GRNN唯一的调节参数SPREAD不能自动获取限制其进一步的应用。针对该缺陷,本文采用果蝇优化算法(FOA)与GRNN相结合构建FOAGRNN模型对GRNN进行优化,自动获取调节参数的值。为了检验FOAGRNN模型的GNSS高程拟合精度,进行了实验分析。实验结果证明了FOAGRNN模型的GNSS高程拟合精度可达6mm。为进一步检验FOAGRNN模型的优越性,采用与平面拟合模型、二次曲面拟合模型进行对比。实验结果表示FOAGRNN模型的拟合精度要优于平面拟合模型和二次曲面拟合模型,证明了FOAGRNN模型在数据样本较少的情况下,其GNSS高程拟合精度仍然可以达到较高精度。

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。