Hybrid-Flash Butterfly Optimization Algorithm with Logistic Mapping for Solving the Engineering Constrained Optimization Problems

Only the smell perception rule is considered in the butterfly optimization algorithm (BOA), which is prone to falling into a local optimum. Compared with the original BOA, an extra operator, i.e., color perception rule, is incorporated into the proposed hybrid-flash butterfly optimization algorithm (HFBOA), which makes it more in line with the actual foraging characteristics of butterflies in nature. Besides, updating the strategy of the control parameters by the logistic mapping is used in the HFBOA for enhancing the global optimal ability. The performance of the proposed method was verified by twelve benchmark functions, where the comparison experiment results show that the HFBOA converges quicker and has better stability for numerical optimization problems, which are compared with six state-of-the-art optimization methods. Additionally, the proposed HFBOA is successfully applied to six engineering constrained optimization problems (i.e., tubular column design, tension/compression spring design, cantilever beam design, etc.). The simulation results reveal that the proposed approach demonstrates superior performance in solving complex real-world engineering constrained tasks.     

基于改进蛙跳算法的无线传感器网络覆盖优化

针对传统算法在解决无线传感器网络覆盖优化上存在的覆盖率较低和节点分布不够均匀的问题,提出了一种改进的蛙跳算法;为了同时达到增加算法的种群多样性和加快算法收敛速度的目的,改进蛙跳算法分别增加了个体高斯学习机制和根据粒子群思想改进的更新策略,让族内最差个体在自身附近进行局部搜索,若无效,则使族内最差个体同时向族内最优个体和全局最优个体学习;在性能评估实验中,对改进的蛙跳算法分别进行了标准函数测试和无线传感器网络覆盖优化测试;测试结果表明,在6个标准测试函数中,改进的蛙跳算法与其他算法相比在4个测试函数上的收敛精度有了明显提高;在无线传感器网络覆盖优化中,改进的蛙跳算法也能够使节点分布更加均匀,使网络覆盖率达到了85.6%。      

An Electric Fish-Based Arithmetic Optimization Algorithm for Feature Selection

With the widespread use of intelligent information systems, a massive amount of data with lots of irrelevant, noisy, and redundant features are collected; moreover, many features should be handled. Therefore, introducing an efficient feature selection (FS) approach becomes a challenging aim. In the recent decade, various artificial methods and swarm models inspired by biological and social systems have been proposed to solve different problems, including FS. Thus, in this paper, an innovative approach is proposed based on a hybrid integration between two intelligent algorithms, Electric fish optimization (EFO) and the arithmetic optimization algorithm (AOA), to boost the exploration stage of EFO to process the high dimensional FS problems with a remarkable convergence speed. The proposed EFOAOA is examined with eighteen datasets for different real-life applications. The EFOAOA results are compared with a set of recent state-of-the-art optimizers using a set of statistical metrics and the Friedman test. The comparisons show the positive impact of integrating the AOA operator in the EFO, as the proposed EFOAOA can identify the most important features with high accuracy and efficiency. Compared to the other FS methods whereas, it got the lowest features number and the highest accuracy in 50% and 67% of the datasets, respectively.     

A New Two-Stage Algorithm for Solving Optimization Problems

Optimization seeks to find inputs for an objective function that result in a maximum or minimum. Optimization methods are divided into exact and approximate (algorithms). Several optimization algorithms imitate natural phenomena, laws of physics, and behavior of living organisms. Optimization based on algorithms is the challenge that underlies machine learning, from logistic regression to training neural networks for artificial intelligence. In this paper, a new algorithm called two-stage optimization (TSO) is proposed. The TSO algorithm updates population members in two steps at each iteration. For this purpose, a group of good population members is selected and then two members of this group are randomly used to update the position of each of them. This update is based on the first selected good member at the first stage, and on the second selected good member at the second stage. We describe the stages of the TSO algorithm and model them mathematically. Performance of the TSO algorithm is evaluated for twenty-three standard objective functions. In order to compare the optimization results of the TSO algorithm, eight other competing algorithms are considered, including genetic, gravitational search, grey wolf, marine predators, particle swarm, teaching-learning-based, tunicate swarm, and whale approaches. The numerical results show that the new algorithm is superior and more competitive in solving optimization problems when compared with other algorithms.     

粒子群算法在非线性系统应用中的早熟现象及其改进

通过分析粒子群算法早熟现象的机理,研究早熟收敛的本质,并提出一种克服粒子群算法早熟现象的局部"飞跃"策略.应用仿真及系统工程实例表明,该方法能有效地改善粒子群算法在非线性全局优化上的早熟问题,提高了粒子群算法的全局搜索能力.     

改进粒子群优化算法的高光谱波段选择

高光谱图像具有数百个连续、狭窄的光谱带,光谱范围跨越可见光到红外光,可提供地物的精细光谱属性,对于地物材质和属性的识别分类具有重要应用价值.针对感兴趣目标选择有限的光谱波段进行传输和处理,对于提升高光谱数据处理时效性、以及设计面向特定应用的实用化光谱仪都具有重要意义.而如何结合目标特征选择最优波段成为在提升处理效率的同...     

基于改进蚁群算法的无线传感器网络路由的优化

为了延长无线传感器网络(Wireless Sensor Network ,WSN)的生命周期,均衡各个节点间能量消耗,针对现有的WSN路由优化算法存在的问题,提出了一种基于改进蚁群算法的路由优化算法。首先通过对蚁群算法和遗传算法的优劣性比较,在蚁群算法的基础上,结合遗传算法的选择、交叉和变异的操作,从而提高蚁群算法的搜索速度和寻优能力。最优路径评价函数综合考虑节点能耗及节点的剩余能量,使剩余能量多的节点优先参与数据转发,均衡节点间的能量消耗。通过与经典蚁群算法及遗传算法的对比实验表明,随着数据转发轮数增加,改进的蚁群算法能耗小,剩余能量多,网络生命周期明显延长;随着整个网络运行时间的增长,改进的蚁群算法,节点均衡能耗性好,最优路径搜索的成功率也明显优于其他两种算法。     

一种适用换热网络同步综合的改进混沌蚁群算法

提出一种适用于换热网络同步综合的改进混沌蚁群算法.在标准算法的基础上引入组织变量更新策略、新型动态邻域拓扑结构,增强算法的全局搜索能力和局部搜索能力.提出两条整型变量优化策略使算法具备优化整型变量的能力和特性.选取两个不同规模的换热网络问题对算法进行测试,其鲁棒性和有效性得到验证.     

A Diversity Model Based on Dimension Entropy and Its Application to Swarm Intelligence Algorithm

The swarm intelligence algorithm has become an important method to solve optimization problems because of its excellent self-organization, self-adaptation, and self-learning characteristics. However, when a traditional swarm intelligence algorithm faces high and complex multi-peak problems, population diversity is quickly lost, which leads to the premature convergence of the algorithm. In order to solve this problem, dimension entropy is proposed as a measure of population diversity, and a diversity control mechanism is proposed to guide the updating of the swarm intelligence algorithm. It maintains the diversity of the algorithm in the early stage and ensures the convergence of the algorithm in the later stage. Experimental results show that the performance of the improved algorithm is better than that of the original algorithm.     

保护有效结构的整型/连续变量分离优化策略改进RWCE算法

强制进化随机游走算法优化换热网络时,连续变量进化阶段产生年综合费用更低的结构会被整型变量进化操作所破坏,导致连续变量进化后形成的结构未能充分发挥优势,制约了连续变量进化.本文提出保护有效结构的整型/连续变量分离优化策略,将连续变量进化与整型变量进化两个阶段的结构费用分别进行判断,从而保护具有发展潜力的结构,使年综合费用...     

Optimal Performance and Application for Seagull Optimization Algorithm Using a Hybrid Strategy

This paper aims to present a novel hybrid algorithm named SPSOA to address problems of low search capability and easy to fall into local optimization of seagull optimization algorithm. Firstly, the Sobol sequence in the low-discrepancy sequences is used to initialize the seagull population to enhance the population’s diversity and ergodicity. Then, inspired by the sigmoid function, a new parameter is designed to strengthen the ability of the algorithm to coordinate early exploration and late development. Finally, the particle swarm optimization learning strategy is introduced into the seagull position updating method to improve the ability of the algorithm to jump out of local optimization. Through the simulation comparison with other algorithms on 12 benchmark test functions from different angles, the experimental results show that SPSOA is superior to other algorithms in stability, convergence accuracy, and speed. In engineering applications, SPSOA is applied to blind source separation of mixed images. The experimental results show that SPSOA can successfully realize the blind source separation of noisy mixed images and achieve higher separation performance than the compared algorithms.     

A Hybridization of Dragonfly Algorithm Optimization and Angle Modulation Mechanism for 0-1 Knapsack Problems

The dragonfly algorithm (DA) is a new intelligent algorithm based on the theory of dragonfly foraging and evading predators. DA exhibits excellent performance in solving multimodal continuous functions and engineering problems. To make this algorithm work in the binary space, this paper introduces an angle modulation mechanism on DA (called AMDA) to generate bit strings, that is, to give alternative solutions to binary problems, and uses DA to optimize the coefficients of the trigonometric function. Further, to improve the algorithm stability and convergence speed, an improved AMDA, called IAMDA, is proposed by adding one more coefficient to adjust the vertical displacement of the cosine part of the original generating function. To test the performance of IAMDA and AMDA, 12 zero-one knapsack problems are considered along with 13 classic benchmark functions. Experimental results prove that IAMDA has a superior convergence speed and solution quality as compared to other algorithms.     

粒子群及遗传算法在相对论返波管中的应用

在全三维粒子模拟软件CHIPIC平台上,分别开发了粒子群及基因算法模块.以相对论返波管为例,采用三种不同类型的参数(连续参数、离散参数、混合参数),对粒子群及基因算法进行比较.优化结果表明:粒子群算法的收敛速度更快,在有限的迭代步数内得到的目标结果也更优良,总体表现优于基因算法.     

一种采用种群多样性监控和实时更新策略的粒子群优化算法

针对粒子群算法优化后期容易出现早熟收敛问题,建立一种具有种群多样性监测和实时更新策略的改进方法.首先建立种群健康度指标用来评价粒子群进化状态;其次提出随机扰动策略和离心搜索策略用于丰富粒子群的种群多样性,增强算法的全局搜索能力,并提出梯度搜索策略用于精确、高效地搜寻当前邻域内的局部极值点,提高算法的计算效率.最后建立种群健康度反馈机制,使粒子可以实时感知种群的健康程度,并自适应地采用不同的粒子更新策略,保证粒子群处于健康进化水平.将新方法应用于优化实例,并与其它改进方法进行性能比较,结果验证了新方法的有效性.     

Application of Euler Neural Networks with Soft Computing Paradigm to Solve Nonlinear Problems Arising in Heat Transfer

In this study, a novel application of neurocomputing technique is presented for solving nonlinear heat transfer and natural convection porous fin problems arising in almost all areas of engineering and technology, especially in mechanical engineering. The mathematical models of the problems are exploited by the intelligent strength of Euler polynomials based Euler neural networks (ENN’s), optimized with a generalized normal distribution optimization (GNDO) algorithm and Interior point algorithm (IPA). In this scheme, ENN’s based differential equation models are constructed in an unsupervised manner, in which the neurons are trained by GNDO as an effective global search technique and IPA, which enhances the local search convergence. Moreover, a temperature distribution of heat transfer and natural convection porous fin are investigated by using an ENN-GNDO-IPA algorithm under the influence of variations in specific heat, thermal conductivity, internal heat generation, and heat transfer rate, respectively. A large number of executions are performed on the proposed technique for different cases to determine the reliability and effectiveness through various performance indicators including Nash–Sutcliffe efficiency (NSE), error in Nash–Sutcliffe efficiency (ENSE), mean absolute error (MAE), and Thiel’s inequality coefficient (TIC). Extensive graphical and statistical analysis shows the dominance of the proposed algorithm with state-of-the-art algorithms and numerical solver RK-4.     

基于粒子群与遗传算法的矩阵式梯度线圈优化设计

磁共振成像系统中的梯度线圈产生用于选层、频率编码和相位编码的梯度磁场.目前常用的梯度线圈是通过目标场法设计得到的.近些年来,由多个形状相同的线圈组成的矩阵式梯度线圈的梯度磁场均匀度和功率等指标也达到了较为满意的效果.本文首先提出了一种基于粒子群与遗传算法的、适用于开放式永磁型磁共振成像系统的矩阵式梯度线圈设计方法.然后对三个方向上的矩阵梯度线圈的电流分布进行了设计,每个方向上的矩阵式梯度线圈系统由224个大小相同的圆形线圈组成.最后利用有限元仿真软件对设计方案进行仿真计算,得到x、y方向上的平均非均匀度为0.851%,z方向上的平均非均匀度为1.013%,验证了本文提出的方法的有效性.基于该方法可以有效快速地对开放式磁共振成像系统的矩阵梯度线圈进行设计.     

应用粒子群优化算法对鸭肉中四环素残留含量的同步荧光光谱快速测定

四环素在NaOH存在的条件下能降解生成具有强荧光特性的异四环素,应用同步荧光光谱结合小波去噪、粒子群优化算法(PSO)和支持向量回归(SVR)建立鸭肉中四环素残留含量的预测模型,可实现鸭肉中四环素残留含量的快速测定和提高预测模型的精度。首先应用平行因子分析法(PARAFAC)确定检测鸭肉中四环素含量的最佳波长差Δλ为70 nm;然后对同步荧光光谱进行db6小波的2层分解的小波去噪及去噪后的光谱归一化处理,并利用PSO筛选出了6个荧光特征波长;最后应用PSO优化SVR模型参数(c, g),进而对在PSO筛选的特征波长光谱条件下建立的PSO-SVR,PLS,PCR模型以及在全光谱条件下建立的PSO-SVR模型进行性能比较,结果表明,以在PSO筛选的特征波长光谱条件下建立的PSO-SVR模型预测能力更强,其预测集的相关系数(r)和均方根误差(RMSEP)分别为0.952 0和17.6 mg·kg-1。说明PSO能够有效提取鸭肉中残留四环素所对应的荧光特征波长,且PSO-SVR预测模型能满足鸭肉中残留四环素的快速测定要求。     

一种基于多准则决策和PSO-LM混合优化算法的多峰Brillouin散射谱的特征提取方法

采用传统方法对多峰Brillouin散射谱进行拟合的过程中,通常是以谱线最大功率点为基准的,却忽略了其他比该点小但却是极值的功率点。这样获得的拟合曲线通常只有一个峰值,相当于把除最高峰之外还有多个小峰的多峰Brillouin散射谱进行了简化,导致大量有用信息的丢失。为了提高Brillouin散射谱的特征提取精度,提出了一种基于MCDM和PSO-LM混合优化算法的多峰Brillouin散射谱特征提取方法(MCDM-PSO-LM)。MCDM可以识别和准确定位多峰Brillouin散射谱的各个波峰和波谷;PSO-LM混合优化算法可以实现分别对各个波峰和波谷的曲线进行拟合并找到每一个波峰的中心频率,该算法既克服了PSO算法过早收敛于局部极值和LM算法依赖初值的问题,又可以将PSO算法的全局搜索能力和LM算法的局部收敛能力结合在一起。较传统算法而言,MCDM-PSO-LM算法保证了对最优值求解的速度和精度,提高了运算能力,使解析解最大限度地接近最优值。分别在不同信噪比和不同线宽条件下进行仿真验证,频移和温度误差分析结果表明,MCDM-PSO-LM方法可以对多峰Brillouin散射谱的各个波峰与波谷进行准确定位,可用于多峰Brillouin散射谱的特征提取,识别效果明显强于传统算法,提高了信息分析的准确性。     

Intelligent Fault Identification for Rolling Bearings Fusing Average Refined Composite Multiscale Dispersion Entropy-Assisted Feature Extraction and SVM with Multi-Strategy Enhanced Swarm Optimization

Rolling bearings act as key parts in many items of mechanical equipment and any abnormality will affect the normal operation of the entire apparatus. To diagnose the faults of rolling bearings effectively, a novel fault identification method is proposed by merging variational mode decomposition (VMD), average refined composite multiscale dispersion entropy (ARCMDE) and support vector machine (SVM) optimized by multistrategy enhanced swarm optimization in this paper. Firstly, the vibration signals are decomposed into different series of intrinsic mode functions (IMFs) based on VMD with the center frequency observation method. Subsequently, the proposed ARCMDE, fusing the superiorities of DE and average refined composite multiscale procedure, is employed to enhance the ability of the multiscale fault-feature extraction from the IMFs. Afterwards, grey wolf optimization (GWO), enhanced by multistrategy including levy flight, cosine factor and polynomial mutation strategies (LCPGWO), is proposed to optimize the penalty factor C and kernel parameter g of SVM. Then, the optimized SVM model is trained to identify the fault type of samples based on features extracted by ARCMDE. Finally, the application experiment and contrastive analysis verify the effectiveness of the proposed VMD-ARCMDE-LCPGWO-SVM method.     

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11.213 GHz,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0.4 MHz,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。