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.

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.

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.

Performance Prediction of Switched Reluctance Motor using Improved Generalized Regression Neural Networks for Design Optimization

Since practical mathematical model for the design optimization of switched reluctance motor(SRM)is difficult to derive because of the strong nonlinearity,precise prediction of electromagnetic characteristics is of great importance during the optimization procedure.In this paper,an improved generalized regression neural network(GRNN)optimized by fruit fly optimization algorithm(FOA)is proposed for the modeling of SRM that represent the relationship of torque ripple and efficiency with the optimization variables,stator pole arc,rotor pole arc and rotor yoke height.Finite element parametric analysis technology is used to obtain the sample data for GRNN training and verification.Comprehensive comparisons and analysis among back propagation neural network(BPNN),radial basis function neural network(RBFNN),extreme learning machine(ELM)and GRNN is made to test the effectiveness and superiority of FOA-GRNN.

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 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.

An Optimization Algorithm for Service Composition Based on an Improved FOA

Large-scale service composition has become an important research topic in Service-Oriented Computing（SOC）. Quality of Service（Qo S） has been mostly applied to represent nonfunctional properties of web services and to differentiate those with the same functionality. Many studies for measuring service composition in terms of Qo S have been completed. Among current popular optimization methods for service composition, the exhaustion method has some disadvantages such as requiring a large number of calculations and poor scalability. Similarly,the traditional evolutionary computation method has defects such as exhibiting slow convergence speed and falling easily into the local optimum. In order to solve these problems, an improved optimization algorithm, WS FOA（Web Service composition based on Fruit Fly Optimization Algorithm） for service composition, was proposed, on the basis of the modeling of service composition and the FOA. Simulated experiments demonstrated that the algorithm is effective, feasible, stable, and possesses good global searching ability.

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。

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。

涡轮转子径向变形稳健性优化设计

考虑到参数不确定性对转子径向变形的影响,提出了1种基于分布式协同响应面的涡轮转子径向变形稳健性优化方法。首先,利用Kriging模型建立各部件参数与径向变形响应面子模型,然后利用分布式协同响应面方法建立全局参数与转子径向变形的系统响应面模型。其次,利用系统响应面模型建立涡轮转子径向变形稳健性优化模型,并采用果蝇优化算法来进行稳健性优化求解。优化后涡轮转子径向变形的均值以及标准差比优化前分别降低了7.3%和4.97%,计算结果表明:该方法在工程应用中的可行性和有效性。

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.

基于粒子群搜索策略的混合果蝇优化算法

果蝇优化算法是一种群智能优化算法,它存在一些缺点如在后期收敛速度和求解精度等方面。论文提出一种基于粒子群搜索策略的混合果蝇优化算法,充分利用PSO算法中个体极值和全局极值位置来更新果蝇个体位置,从而弥补了果蝇优化算法的不足。为了验证该算法的性能通过测试6个标准多元非线性函数并且与标准果蝇优化算法相比较,测试结果表明该算法的收敛性和求解精度都得到了提高。

粉煤灰颗粒分布重建果蝇算法分析

为了适应工业场合粉煤灰颗粒粒径的快速检测,对果蝇优化算法粉煤灰颗粒粒径分布重建进行研究。搭建了颗粒粒径在线测量平台,选用标准粉煤灰颗粒为实验材料,在无噪声环境下进行实验模拟。采用果蝇算法反演待测粉煤灰颗粒粒径并将得到的结果与遗传算法和粒子群算法反演得到的结果进行比较。结论表明:采用果蝇优化算法反演颗粒粒径,相对误差为1.07%,运算时间为1.21 s,相比于另外2种算法,具有较好的反演精度和较快的迭代速度。

Prediction and determination of mildew grade in grain storage based on FOA-SVM algorithm

Grain mildew is a significant hazard that causes food loss and poses a serious threat to human health when severe.Therefore,effective prediction and determination of mildew grade is essential for the prevention and control of mildew and global food security.In the present study,a model for predicting and determining the mildew grade of rice was constructed using logistic regression,back propagation neural network and GS-SVM(a grid search-based support vector machine algorithm)based on laboratory culture data and actual data from a granary,respectively.The results show that the GS-SVM model has a better prediction effect,but the model cannot automatically adjust the parameters and is more subjective,and the accuracy may decrease when the data set changes.Therefore,this paper establishes a new model for a support vector machine based on a fruit fly optimization algorithm(FOA-SVM),which can achieve automatic parameter search and automatically adjust its parameters to find the best result when the data set changes,with a strong ability of self-adjustment of parameters.In addition,the FOA-SVM converges quickly and the model is stable.The results of this study provide a technical method for early identification of mildew grade during grain storage,which is beneficial for the prevention and control of rice mildew during grain storage.

基于混合变异的果蝇优化算法

果蝇算法是新提出一种的群智能优化算法,它存在一些不足尤其是在收敛性和求解精度方面.基于以上提出了一种基于混合变异算子的果蝇优化算法,充分利用柯西变异算子所具有全局搜索能力强的特点和高斯变异算子的局部搜能力强的优点,将这两个算子结合在一起来更新果蝇的位置从而很好的避免了各自算子的缺点.为了验证算法的性能通过测试7个标准多元非线性函数同果蝇优化算法及参考文献中算法结果相比较,实验表明该算法的收敛速度和求解的精度都得到了提高.

电力市场环境下基于FOALSSVM的电价预测

提出了一种基于果蝇优化算法(FOA)和最小二乘支持向量机(LSSVM)模型的日均电价混合预测模型。将日均电价的历史数据和负荷数据作为输入变量,利用FOA优化选择用于电价预测的LSSVM模型最优参数值,进而对日均电价进行预测。以澳大利亚NSW电力市场的实际数据为例对该模型进行了仿真测试,其结果表明:与自适应LSSVM、模拟退火LSSVM和ARIMA-GARCH模型相比,本文提出的预测模型的预测性能最好,其收敛速度快,预测精度高。