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.

基于EMD-MFOA-ELM的瓦斯涌出量时变序列预测研究

为准确分析工作面绝对瓦斯涌出量的非平稳特征,实现瓦斯涌出量的准确预测,基于经验模态分解(EMD)、修正的果蝇优化算法(MFOA)和极限学习机(ELM)基本原理,构建瓦斯涌出量的EMD-MFOA-ELM多尺度时变预测模型。通过EMD将瓦斯涌出量时变序列进行深层次分解,获得多尺度本征模态函数(IMF);采用MFOA-ELM对各IMF时变序列建立动态预测模型,等权叠加各预测值,得到模型最终预测结果。以晋煤某矿瓦斯涌出量监测时序样本为例进行研究分析,结果表明:EMD能充分挖掘出监测数据隐含信息,有效降低数据复杂度;该模型预测相对误差为0.024 3%~0.651 0%,平均值仅为0.252 6%,预测精度和泛化能力高于未经EMD分解模型,能很好地适用于非平稳时变序列预测。

基于MFOA-SVR露天矿边坡变形量预测研究

为实现边坡危险性及时预警预报,以露天矿边坡变形量为研究对象,提出采用七项影响指标作为边坡位移变形量的响应参数,建立支持向量机回归预测模型(SVR)。引入修正的果蝇优化算法(MFOA)对模型参数进行优化,构建基于MFOA-SVR露天矿边坡变形量协同预测模型,并以实际监测数据进行模型仿真预测。结果表明:该模型平均绝对误差为0.9167mm,平均相对误差为4.2737%,较其他模型预测精度高,综合性能好,将其运用于露天矿边坡变形量预测研究具有较好的适用性和可靠性。

基于MFOA的锅炉热效率及NO_X排放建模与优化

为达到提高锅炉热效率同时减少NO_X排放的目标,提出一种改进果蝇算法(MFOA)优化支持向量机(SVM)锅炉建模方法。针对果蝇(FOA)算法寻优精度低、收敛速度慢的问题,使用三维搜索及自适应变步长的策略改进果蝇算法,并完成对SVM中的惩罚因子C、核参数g和不敏感损失系数ε,这三个参数寻优,使支持向量机对锅炉燃烧预测更加准确。根据不同时间段的样本数据来检验MFOA-SVM模型的预测能力,仿真结果表明,改进的果蝇算法具有较强的参数寻优能力。另外以所建燃烧模型为基础,使用MFOA算法对锅炉进行单目标优和多目标优化,优化结果表明,所提出的燃烧优化方案可以有效提高锅炉效率和降低NO_X排放量。

基于MFOA和LW的混沌时间序列鲁棒模糊预测

针对含有例外点的混沌时间序列的预测问题,提出了一种基于修正型果蝇优化算法(MFOA)和最小Wilcoxon方法(LW)的混合学习算法来训练T-S模糊模型,以达到准确建模和提高模型鲁棒性的目的。首先采用修正型果蝇优化算法优化模糊前件的高斯型隶属函数参数,利用其编程简单、收敛速度快的优点提高辨识精度和收敛速度。然后采用最小Wilcoxon方法辨识模型的结论参数,在训练数据中出现例外点时,LW方法的强鲁棒性可以有效克服传统最小二乘方法对例外点敏感的缺点。最后以Mackey-Glass混沌时间序列的预测为例进行仿真研究,通过比较不同的优化算法的辨识结果来验证修正型果蝇优化算法的优越性,并在系统存在例外点的情况下验证了所提方法的有效性和鲁棒性。

冰雪天气下基于MFOA-KELM残差修正的跑道温度混合预测

道面温度短时精准预测是跑道积冰预警的关键因素之一,为了解决单一机理预测模型随预测时间延长而造成误差累积的问题,提出了一种冰雪天气下跑道温度混合预测方法。将跑道温度机理预测模型与核极限学习机(KELM)相结合,建立一种数据驱动修正残差的跑道温度机理预测模型。针对果蝇优化算法(FOA)收敛速度慢、易陷入局部最小值的问题,引入权值更新函数和距离扩充因子,调整果蝇的全局寻优效果,避免陷入局部极小值。利用改进的果蝇优化算法(MFOA)对KELM的正则化参数与核参数联合优化,以冰雪天气下跑道温度实际数据为例,建立基于改进果蝇优化核极限学习机(MFOA-KELM)的跑道温度混合预测模型,并在不同时间尺度下对该混合预测模型进行仿真测试。实验结果表明:与单一机理预测模型相比,当预测时长为120 min时,MFOA-KELM混合预测模型的平均绝对误差至少减小了61.43%,在残差阈值为±0.5℃时,平均预测准确率为91.25%。可见,MFOA-KELM混合预测模型具有更高的预测准确性,研究结论显示该混合预测方法能够为机场跑道温度短时精准预测提供新思路。

基于ICEEMDAN能量矩和MFOA-PNN的轴承故障诊断

为了提高滚动轴承故障诊断的准确性,实现对故障的精准定位,提出一种基于改进的自适应噪声的完备集成经验模态分解(Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,ICEEMDAN)能量矩和修正型果蝇优化算法-概率神经网络(Modified Fruit Fly Optimization Algorithm-Probabilistic Neural Network,MFOA-PNN)的轴承故障诊断方法。首先利用ICEEMDAN算法对滚动轴承原始序列信号进行预处理,通过能量矩计算公式求取特征值,并将其作为PNN模型的输入;其次运用MFOA搜索PNN模型的最优平滑参数,通过建立MFOA-PNN模型诊断故障类别。实验表明,MFOA-PNN模型相比PNN模型的诊断准确性有所提高,准确率可以达到99.50%,提高了滚动轴承的经济性和安全性。

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.

基于MFOA-SVM算法的乳腺肿瘤识别

针对乳腺肿瘤良恶性二值分类的特点,提出了一种基于修正的果蝇优化算法和支持向量机(MFOASVM)的乳腺肿瘤识别方法.为提高SVM分类器的泛化性能,将MFOA算法引入SVM,进而优化SVM中的惩罚参数和核函数参数.为了综合评估提出算法的有效性,在威斯康新诊断乳腺癌(Wisconsin diagnostic breast cancer,WDBC)数据集进行了实验对比分析.实验结果表明:MFOA-SVM与BP,LVQ及PSO-SVM 3种方法相比,其分类准确性和稳定性显著提高.

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.

冰雪天气下基于MFOA-LSSVR的跑道温度预测

跑道温度是跑道结冰的重要因素。针对机场跑道温度短时预测问题,提出一种改进果蝇算法优化最小二乘支持向量回归机(MFOA-LSSVR)的跑道温度预测方法。在原始果蝇算法(Fruit Fly Optimization Algorithm, FOA)中引入指数更新函数和距离扩张因子以增强果蝇种群全局寻优能力,避免其陷入局部极小值。同时考虑跑道温度多种影响因素对模型预测精度的影响,选用Spearman相关系数确定跑道温度主要影响特征。以冰雪天气下跑道温度的实际数据,对该模型进行仿真测试。结果显示:与果蝇优化最小二乘支持向量回归机(FOA-LSSVR)、反向传播(Backpropagation, BP)神经网络、机理模型、MFOA-LSSVR单变量模型相比,MFOA-LSSVR预测模型的平均绝对误差至少分别提高了17.24%、25.37%、69.76%和10.88%。实验结果验证了所提方法的有效性和泛化性,能够为跑道温度短时预测提供有效思路。

基于MFOA-GRNN模型的三维定位研究

由于传统室内定位模型容易受到外界因素影响导致定位精度大幅下降,提出一种将多种群果蝇优化算法(Multi-population Fruit Fly Optimization Algorithm,MFOA)与广义回归神经网络(Generalized Regression Neural Network,GRNN)相结合的MFOA-GRNN三维室内定位模型。基于射频识别技术并引入多种群的果蝇优化算法用以选择GRNN的平滑参数,并通过MFOA-GRNN模型将阅读器接收信号强度与目标坐标进行对应进而判断目标位置。仿真结果表明,该模型克服了传统室内定位模型受主观因素影响大、学习效率低等不足,同时使算法的全局寻优能力得到了加强,定位精度显著提高。

基于MFOA算法的电力系统无功优化和补偿控制研究

基于使用优化算法来计算果蝇,提出了一种修正算法研究,即无功优化和控制算法。这些算法有利于降低电力系统的有功损耗。设β为修正因子,然后代入基础的算法中对这个基础的FOA算法进行修正和优化,从而避免FOA算法容易仅将焦点关注于局部而非整体。采用FOA、PSO、MFOA以及内点法来研究IEEE30节点系统,通过研究对比发现,MFOA相较于其他几种算法,计算结果较为准确,且收敛效率更高。

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.

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。