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.

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.

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.

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IWOA-BP神经网络图像复原

针对传统复原算法在退化图像复原过程中存在明显滞后的问题,建立了一种改进的鲸鱼算法(Improved Whale Optimization Algorithm,IWOA)-BP神经网络图像复原模型。首先,通过Tent混沌增强初始种群的均匀性和多样性;其次,采用非线性权重和改进的收敛因子,平衡算法的全局搜索与局部寻优能力;最后,结合Levy飞行策略更新个体位置,帮助算法跳出局部最优。随后采用经典图像数据,建立IWOA-BP模型。选取PSNR、SSIM和NMSE作为网络模型的评价指标,与BP、GWO-BP、WOA-BP进行对比。实验结果表明IWOA-BP模型图像复原视觉效果更好,提高了图像复原的质量。

基于多策略蜜獾算法的TDOA定位

针对超宽带传感器TDOA定位估计的非线性最优问题,文章提出一种多策略改进的蜜獾优化的TDOA定位算法,通过引入Tent混沌映射函数、正余弦策略和Levy飞行多种策略进行改进,解决了传统蜜獾优化算法存在的局限性问题。文章首先建立TDOA算法适应度函数,通过改进的蜜獾算法得到定位信息,其次将定位信息作为Taylor算法的初始值,通过Taylor级数展开算法的进一步迭代,减小非视距误差,获得更高精度的定位结果。仿真实验结果表明,改进的多策略蜜獾算法与其他多种智能算法相比,具有更高的定位精度。

基于FOA-RF模型下煤与瓦斯突出智能预警系统研究

为提高工作面煤与瓦斯突出预警系统的预警准确率及智能化程度,以山西省吕梁市某矿11301掘进工作面为研究背景,引入人工智能大数据挖掘算法构建基于FOA-RF的煤与瓦斯突出危险性智能预测模型,利用微震及红外甲烷传感器实时监测工作面煤与瓦斯突出危险状态,设计开发了煤与瓦斯突出智能预警系统,并将该系统应用至11301掘进工作面的突出危险性预警中。应用结果显示,智能预测模型的训练精度为99.59%,预测精度为94.57%,所建预警系统能较好地对实时预警指标数据进行可视化分析及管理,预警结果与工作面实际突出状态基本一致。

利用果蝇算法反演概率积分法开采沉陷预计参数

针对概率积分法开采沉陷预计参数反演时存在算法复杂、计算量大等问题,将具有算法简单、计算量小、精度高等特点的果蝇算法引入到概率积分法开采沉陷预计参数反演中,研究了利用果蝇算法反演概率积分法开采沉陷预计参数的基本原理,构造了下沉拟合值与实测值均方差最小的适应度函数模型。结合安徽省某煤矿的实测数据,分别采用果蝇算法、遗传算法以及粒子群算法反演概率积分法开采沉陷预计参数,并以下沉拟合值与实测值的均方差为各算法反演精度的评价标准进行对比分析,结果表明:利用果蝇算法反演出的下沉拟合值与实测值的均方差(33.7 mm)以及相对中误差(1.4%)均小于同类条件下遗传算法、粒子群算法的反演结果,说明果蝇算法适用于反演概率积分法开采沉陷预计参数,对于提高概率积分法开采沉陷预计的精度有一定的参考价值。

基于改进果蝇算法与最小二乘支持向量机的轧制力预测算法研究

铝合金板材精轧过程中，轧制力是影响板材质量的重要因素。为了满足轧制现场的轧制力预报精度要求，采用改进果蝇算法（FOA）与最小二乘支持向量机（LSSVM）相结合进行轧制力预测。改进了果蝇算法的味道浓度判定函数和步长设定方法，采用了分组并行搜索的策略，进而提出一种基于改进FOA—LSSVM的轧制力智能预报方法。将该方法用于铝热连轧现场数据的仿真实验，结果表明样本预测误差在10％以内，其中84％的样本误差在5％以内，精度优于传统模型。

基于自适应变异果蝇优化算法和广义回归神经网络的布里渊散射谱特征提取

针对布里渊光时域反射光纤传感系统散射谱的高精度特征提取的要求,提出了一种基于自适应变异果蝇优化算法和广义回归神经网络的布里渊散射谱特征提取算法。不仅利用了广义回归神经网络在逼近能力、学习速度、模型的泛化等方面具有的优势,而且采用搜索能力较强的自适应变异果蝇优化算法进一步增强了神经网络的学习能力,从而提高了布里渊散射谱的拟合度和频移提取的准确度。在布里渊散射谱中心频率为11.213GHz,线宽为40~50,30~60和20~70 MHz的散射谱白噪声实验模型中,将新算法分别与基于有限元分析的Levenberg-Marquardt拟合法、粒子群优化和拉凡格式混合拟合法、最小二乘法进行预测比较,新算法获得的最大拟合频移误差为0.4MHz,平均拟合度为0.991 2,均方根误差为0.024 1。仿真结果表明所提出的算法拟合度较好,绝对误差小。因此,将此算法用于基于布里渊光时域反射的分布式光纤传感系统,可有效提高布里渊散射谱的拟合度和频移提取的准确度。

修正型果蝇算法优化GRNN的大梁自动焊障碍预测

大梁自动焊时,必须自动避开工件上的筋板、隔板和空洞等障碍物.但因产品的种类多,工件上障碍物的位置存在随机性,难以通过单一的方法进行障碍物预测.针对该问题,利用超声波传感器采集障碍物信息,提出一种修正型果蝇算法优化广义回归神经网络(AFOA-GRNN)的大梁自动焊障碍物预测模型.该方法在传统果蝇算法中引入信息素和灵敏度两个因子,改进了寻优策略和果蝇位置的替换方式,对GRNN进行参数优化,进行大梁自动焊障碍物的预测.结果表明,建立的修正型AFOA-GRNN预测模型相比于FOA-GRNN,训练速度更快,预测精度更高.

回采工作面瓦斯涌出量耦合预测模型研究

为准确、快速地预测回采工作面瓦斯涌出量,提出一种基于主成分分析法(PCA)和改进的果蝇算法(MFOA)优化支持向量机(SVM)的回采工作面绝对瓦斯涌出量预测模型。模型首先运用PCA方法对原始数据进行降维处理,消除数据冗余,而后采用改进的果蝇算法对SVM参数进行全局寻优,避免SVM参数的选取对模型预测结果的不利影响,最终建立基于PCA-MFOA-SVM的耦合预测模型,并以实际监测数据为例进行仿真预测。结果表明:该模型预测的平均绝对误差为0.077 5 m3/t,平均相对误差为1.323 7%,与其他模型相比,预测精度高,综合性能好,能够实现回采工作面瓦斯涌出量的动态预测。

基于FOA-SVM模型的输油管道内腐蚀速率预测

针对管道内腐蚀速率相关问题,采集某输油管道内腐蚀的实测数据,应用多元统计分析算法,在支持向量机(SVM)的基础上建立管道内腐蚀速率预测模型。采用果蝇优化算法(FOA)对预测模型进行优化训练,建立FOASVM预测模型,利用实测数据样本对模型的预测结果进行检验。结果表明:综合方差和均差分别为1.397×10-3和0.037 4,FOA-SVM预测模型相比灰色组合模型预测值和最小二乘支持向量机(LS-SVM)模型预计结果稳定性好、精度高,但是FOA-SVM预测模型训练时间较长,今后在提高模型预测效率上需要进一步研究。

基于多状态信息修正优化组合的电力设备故障率计算方法

采用多状态修正优化组合预测方法,建立天气因素、材料绝缘老化和设备检修影响的3种状态修正优化组合预测电力设备故障率模型。针对各随机影响因素的特点,依据可拓性原理预测3种天气状态模型的故障率,由3种参数威布尔分布-Copula函数的联合失效概率密度法计算绝缘老化引起的设备故障率,基于Holt-Winters模型来预估设备检修造成的故障率,再采用果蝇算法加权组合优化所求得的各子模型的故障率,算出具有高准确度的预测值。以某地区的电力系统为实例进行分析,所得结果表明所述模型可有效提高设备故障率的预测精度,同时也验证了果蝇优化算法在求解多状态修正优化组合预测问题时的有效性。

新型果蝇优化算法的研究

由于传统果蝇优化算法(FOA)具有寻优精度低和易陷入局部极小点的缺点,提出了一种具有不同飞行半径的分群搜索策略,使得在搜索区间内果蝇的种群多样性大大增加;同时在果蝇个体的飞行距离与方向的步长函数上,针对不同的果蝇子群引入了不同的函数,该类函数具有周期震荡性质,可以很好地避免果蝇群陷入局部极小点而无法求得最优解。通过对8个测试函数的仿真实验,验证了这些策略能够有效地提高搜索精度、收敛速度和稳定性。

基于全景摄像头的柱面展开及实时目标跟踪

传统摄像头在获取大范围复杂场景中的感兴趣目标时,容易出现目标物体丢失或遮挡等问题。为此,提出一种基于全景摄像头的柱面展开及运动目标实时跟踪算法。通过改进的柱面展开算法对360°摄像头获取的全景图像进行还原展开,解决全景图像中的成像扭曲问题。利用CamShift和Kalman预测相结合的算法跟踪运动目标。实验结果表明,在运动目标存在遮挡、短暂消失或同色物体干扰的情况下,该方法能实现对全景范围复杂环境中运动目标实时鲁棒的跟踪。

基于果蝇算法的Boltzmann机谐波检测

基于传统网络的全局搜索易陷入局部极优及滤波器的静态滤波性能,通过果蝇算法搜寻Boltzmann机结构,不仅在优化网络结构的同时提高了搜寻精度,还提高了滤波效率。通过对燃机发电机机端谐波检测,验证了这种新方法对电力有源滤波器及谐波抑制有很大的改善作用。

基于局部搜索的逆向物流车辆最短路径优化

降低运输成本的关键是车辆最短路径优化,为此研究基于局部搜索的逆向物流车辆最短路径优化,提升车辆最短路径优化效果,降低运输成本与CO_(2)排放量。以车辆最短路径为目标函数,以车辆容量、车辆服务回收点的次数与时间为约束条件,构建逆向物流车辆短路径优化模型;通过结合局部搜素与果蝇算法求解该模型,利用果蝇算法生成果蝇路径飞行方案,通过局部搜索策略获取最优飞行方案,利用最优飞行方案更新最短路径的轨迹强度,获取最优解,完成最短路径优化。仿真结果表明,上述模型引入局部搜索策略可有效缩短最短路径总长度,提高最优解质量;上述模型优化的最短路径显著短于原始最短路径,且求解过程中收敛速度较快;应用上述模型后可有效降低运输成本与CO_(2)排放量。

Data-Driven Anomaly Diagnosis for Machining Processes

To achieve zero-defect production during computer numerical control(CNC)machining processes,it is imperative to develop effective diagnosis systems to detect anomalies efficiently.However,due to the dynamic conditions of the machine and tooling during machining processes,the relevant diagnosis systems currently adopted in industries are incompetent.To address this issue,this paper presents a novel data-driven diagnosis system for anomalies.In this system,power data for condition monitoring are continuously collected during dynamic machining processes to support online diagnosis analysis.To facilitate the analysis,preprocessing mechanisms have been designed to de-noise,normalize,and align the monitored data.Important features are extracted from the monitored data and thresholds are defined to identify anomalies.Considering the dynamic conditions of the machine and tooling during machining processes,the thresholds used to identify anomalies can vary.Based on historical data,the values of thresholds are optimized using a fruit fly optimization(FFO)algorithm to achieve more accurate detection.Practical case studies were used to validate the system,thereby demonstrating the potential and effectiveness of the system for industrial applications.