Quantitative algorithm for airborne gamma spectrum of large sample based on improved shuffled frog leaping-particle swarm optimization convolutional neural network

In airborne gamma ray spectrum processing,different analysis methods,technical requirements,analysis models,and calculation methods need to be established.To meet the engineering practice requirements of airborne gamma-ray measurements and improve computational efficiency,an improved shuffled frog leaping algorithm-particle swarm optimization convolutional neural network(SFLA-PSO CNN)for large-sample quantitative analysis of airborne gamma-ray spectra is proposed herein.This method was used to train the weight of the neural network,optimize the structure of the network,delete redundant connections,and enable the neural network to acquire the capability of quantitative spectrum processing.In full-spectrum data processing,this method can perform the functions of energy spectrum peak searching and peak area calculations.After network training,the mean SNR and RMSE of the spectral lines were 31.27 and 2.75,respectively,satisfying the demand for noise reduction.To test the processing ability of the algorithm in large samples of airborne gamma spectra,this study considered the measured data from the Saihangaobi survey area as an example to conduct data spectral analysis.The results show that calculation of the single-peak area takes only 0.13~0.15 ms,and the average relative errors of the peak area in the U,Th,and K spectra are 3.11,9.50,and 6.18%,indicating the high processing efficiency and accuracy of this algorithm.The performance of the model can be further improved by optimizing related parameters,but it can already meet the requirements of practical engineering measurement.This study provides a new idea for the full-spectrum processing of airborne gamma rays.

Shuffled frog leaping algorithm with non-dominated sorting for dynamic weapon-target assignment

The dynamic weapon target assignment(DWTA)problem is of great significance in modern air combat.However,DWTA is a highly complex constrained multi-objective combinatorial optimization problem.An improved elitist non-dominated sorting genetic algorithm-II(NSGA-II)called the non-dominated shuffled frog leaping algorithm(NSFLA)is proposed to maximize damage to enemy targets and minimize the self-threat in air combat constraints.In NSFLA,the shuffled frog leaping algorithm(SFLA)is introduced to NSGA-II to replace the inside evolutionary scheme of the genetic algorithm(GA),displaying low optimization speed and heterogeneous space search defects.Two improvements have also been raised to promote the internal optimization performance of SFLA.Firstly,the local evolution scheme,a novel crossover mechanism,ensures that each individual participates in updating instead of only the worst ones,which can expand the diversity of the population.Secondly,a discrete adaptive mutation algorithm based on the function change rate is applied to balance the global and local search.Finally,the scheme is verified in various air combat scenarios.The results show that the proposed NSFLA has apparent advantages in solution quality and efficiency,especially in many aircraft and the dynamic air combat environment.

Test Case Prioritization in Unit and Integration Testing:A Shuffled-Frog-Leaping Approach

Both unit and integration testing are incredibly crucial for almost any software application because each of them operates a distinct process to examine the product.Due to resource constraints,when software is subjected to modifications,the drastic increase in the count of test cases forces the testers to opt for a test optimization strategy.One such strategy is test case prioritization(TCP).Existing works have propounded various methodologies that re-order the system-level test cases intending to boost either the fault detection capabilities or the coverage efficacy at the earliest.Nonetheless,singularity in objective functions and the lack of dissimilitude among the re-ordered test sequences have degraded the cogency of their approaches.Considering such gaps and scenarios when the meteoric and continuous updations in the software make the intensive unit and integration testing process more fragile,this study has introduced a memetics-inspired methodology for TCP.The proposed structure is first embedded with diverse parameters,and then traditional steps of the shuffled-frog-leaping approach(SFLA)are followed to prioritize the test cases at unit and integration levels.On 5 standard test functions,a comparative analysis is conducted between the established algorithms and the proposed approach,where the latter enhances the coverage rate and fault detection of re-ordered test sets.Investigation results related to the mean average percentage of fault detection(APFD)confirmed that the proposed approach exceeds the memetic,basic multi-walk,PSO,and optimized multi-walk by 21.7%,13.99%,12.24%,and 11.51%,respectively.

Membrane-inspired quantum shuffled frog leaping algorithm for spectrum allocation

To solve discrete optimization difficulty of the spectrum allocation problem,a membrane-inspired quantum shuffled frog leaping（MQSFL） algorithm is proposed.The proposed MQSFL algorithm applies the theory of membrane computing and quantum computing to the shuffled frog leaping algorithm,which is an effective discrete optimization algorithm.Then the proposed MQSFL algorithm is used to solve the spectrum allocation problem of cognitive radio systems.By hybridizing the quantum frog colony optimization and membrane computing,the quantum state and observation state of the quantum frogs can be well evolved within the membrane structure.The novel spectrum allocation algorithm can search the global optimal solution within a reasonable computation time.Simulation results for three utility functions of a cognitive radio system are provided to show that the MQSFL spectrum allocation method is superior to some previous spectrum allocation algorithms based on intelligence computing.

Improved Shuffled Frog Leaping Algorithm Optimizing Integral Separated PID Control for Unmanned Hypersonic Vehicle

To solve the flight control problem for unmanned hypersonic vehicles,a novel intelligent optimized control method is proposed.A flight control system based on integral separated proportional-integral-derivative(PID)control is designed for hypersonic vehicle,and an improved shuffled frog leaping algorithm is presented to optimize the control parameters.A nonlinear model of hypersonic vehicle is established to examine the dynamic characteristics achieved by the flight control system.Simulation results demonstrate that the proposed optimized controller can effectively achieve better flight control performance than the traditional controller.

Modified Shuffled Frog Leaping Algorithm for Solving Economic Load Dispatch Problem

In the recent restructured power system scenario and complex market strategy, operation at absolute minimum cost is no longer the only criterion for dispatching electric power. The economic load dispatch (ELD) problem which accounts for minimization of both generation cost and power loss is itself a multiple conflicting objective function problem. In this paper, a modified shuffled frog-leaping algorithm (MSFLA), which is an improved version of memetic algorithm, is proposed for solving the ELD problem. It is a relatively new evolutionary method where local search is applied during the evolutionary cycle. The idea of memetic algorithm comes from memes, which unlike genes can adapt themselves. The performance of MSFLA has been shown more efficient than traditional evolutionary algorithms for such type of ELD problem. The application and validity of the proposed algorithm are demonstrated for IEEE 30 bus test system as well as a practical power network of 203 bus 264 lines 23 machines system.

A ε-indicator-based shuffled frog leaping algorithm for many-objective optimization problems

Many-objective optimization problems take challenges to multi-objective evolutionary algorithms.A number of nondominated solutions in population cause a difficult selection towards the Pareto front.To tackle this issue,a series of indicatorbased multi-objective evolutionary algorithms(MOEAs)have been proposed to guide the evolution progress and shown promising performance.This paper proposes an indicator-based manyobjective evolutionary algorithm calledε-indicator-based shuffled frog leaping algorithm(ε-MaOSFLA),which adopts the shuffled frog leaping algorithm as an evolutionary strategy and a simple and effectiveε-indicator as a fitness assignment scheme to press the population towards the Pareto front.Compared with four stateof-the-art MOEAs on several standard test problems with up to 50 objectives,the experimental results show thatε-MaOSFLA outperforms the competitors.

Control Strategy for a Quadrotor Based on a Memetic Shuffled Frog Leaping Algorithm

This work presents a memetic Shuffled Frog Leaping Algorithm(SFLA)based tuning approach of an Integral Sliding Mode Controller(ISMC)for a quadrotor type of Unmanned Aerial Vehicles(UAV).Based on the Newton–Euler formalism,a nonlinear dynamic model of the studied quadrotor is firstly established for control design purposes.Since the main parameters of the ISMC design are the gains of the sliding surfaces and signum functions of the switching control law,which are usually selected by repetitive and time-consuming trials-errors based procedures,a constrained optimization problem is formulated for the systematically tuning of these unknown variables.Under time-domain operating constraints,such an optimization-based tuning problem is effectively solved using the proposed SFLA metaheuristic with an empirical comparison to other evolutionary computation-and swarm intelligence-based algorithms such as the Crow Search Algorithm(CSA),Fractional Particle Swarm Optimization Memetic Algorithm(FPSOMA),Ant Bee Colony(ABC)and Harmony Search Algorithm(HSA).Numerical experiments are carried out for various sets of algorithms’parameters to achieve optimal gains of the sliding mode controllers for the altitude and attitude dynamics stabilization.Comparative studies revealed that the SFLA is a competitive and easily implemented algorithm with high performance in terms of robustness and non-premature convergence.Demonstrative results verified that the proposed metaheuristicsbased approach is a promising alternative for the systematic tuning of the effective design parameters in the integral sliding mode control framework.

基于改进SFLA-Elman神经网络的电离层杂波抑制方法

针对高频地波雷达目标检测中电离层杂波的干扰问题,提出了一种基于改进混合蛙跳算法优化Elman神经网络预测抑制电离层杂波的策略。为解决混合蛙跳算法初始种群分布不均匀、收敛精度低、易陷于局部极值等问题,引入Cubic混沌映射、莱维飞行策略、非线性平衡因子和复制操作,增强种群多样性,提高算法搜索能力。利用改进后的算法和其他算法分别优化Elman神经网络预测抑制模型,结果表明,改进后的算法无论是在收敛精度和稳定性上,还是在临近距离单元电离层杂波的预测抑制上,都取得了显著的提升。在基本保留目标信号的基础上,平均信杂比较原始回波提升18.52 dB,较原始混合蛙跳算法提升1.08 dB,对于电离层杂波的抑制具有较高应用价值。

基于RF-SFLA-SVM的装配式建筑高空作业工人不安全行为预警

为有效预警装配式建筑高空作业工人不安全行为的发生趋势或状态,增强对装配式建筑工人不安全行为(PBWUBs)的管控,采用随机森林(RF)-混合蛙跳算法(SFLA)-支持向量机(SVM)模型,开展工人不安全行为预警研究。首先,采用SHEL模型分析处于高空作业危险中的PBWUBs的影响因素,并通过RF确定关键预警指标;然后,采用SFLA对SVM的参数进行寻优改进;最后,利用RF-SFLA-SVM预警高空作业PBWUBs,提出应对措施,并与其他预警模型对比。研究结果表明:基于RF-SFLA-SVM预警高空作业PBWUBs,准确率最高,为91.67%,与其他模型的预警性能相比,最高提升14%。研究结果可为高空作业PBWUBs的防控提供参考。

基于SFLA优化变分模态提取的滚动轴承故障诊断

为解决变分模态提取(variational mode extraction, VME)在分解轴承故障信号过程中近似中心频率和惩罚因子的选择过于依赖专家经验的问题,提出混合蛙跳算法(shuffled frog leaping algorithm, SFLA)与VME相结合的滚动轴承故障诊断方法。首先,为解决单一指标作为目标函数提取特征时信息不全面的问题,结合信息熵(information entropy, IE)、包络谱峭度和相关系数建立新的参数优化指标—KIC;然后,将KIC的极小值作为SFLA的目标函数自适应地选取VME期望模态的中心频率和惩罚因子;最后,通过包络解调分析期望模态进行故障诊断。仿真信号与轴承试验台相关数据集的分析结果表明,所提出的SFLA-VME方法能够准确地提取出期望模态并诊断轴承故障。

Recognition of practical speech emotion using improved shuffled frog leaping algorithm

Due to the drawbacks in Support Vector Machine（SVM）parameter optimization,an Improved Shuffled Frog Leaping Algorithm（Im-SFLA）was proposed,and the learning ability in practical speech emotion recognition was improved.Firstly,we introduced Simulated Annealing（SA）,Immune Vaccination（Iv）,Gaussian mutation and chaotic disturbance into the basic SFLA,which bManced the search efficiency and population diversity effectively.Secondly,Im-SFLA Was applied to the optimization of SVM parameters,and an Im-SFLA-SVM method Was proposed.Thirdly,the acoustic features of practical speech emotion,such aS ridgetiness,were analyzed.The pitch frequency,short-term energy,formant frequency and chaotic characteristics were analyzed corresponding to different emotion categories,and we constructed a 144-dimensional emotion feature vector for recognition and reduced to 4-dimension by adopting Linear Discriminant Analysis（LDA） Finally,the Im-SFLA-SVM method Was tested on the practical speech emotion database,and the recognition results were compared with Shuffled Frog Leaping Algorithm optimization-SVM（SFLA-SVM）method,Particle Swarm Optimization algorithm optimization-SVM（PSo-SVM） method,basic SVM,Gaussian Mixture Model（GMM）method and Back Propagation（BP）neural network method.The experimentM resuits showed that the average recognition rate of Im-SFLA-SVM method was 77.8%,which had improved 1.7%,2.7%,3.4%,4.7%and 7.8%respectively,compared with the other methods.The recognition of fidgetiness was significantly improve,thus verifying that Im-SFLA was an effective SVM parameter selection method,and the Im-SFLA-SVM method may significantly improve the practical speech emotion recognition.

基于离散混合蛙跳算法的地震应急物资调度

建立震后应急物资调度数学模型。该模型根据各灾区的受灾情况评估其救援紧急程度,并设计一种需求拆分供应的运输机制,提高车辆的利用效率。为求解该模型,提出一种多源信息学习的离散混合蛙跳算法。所提算法引入多种信息源以扩展算法的搜索方向,降低种群的同化速度。同时,让子组最差个体学习种群中的有效信息,提高算法的收敛精度。实验结果表明,所提算法能够搜索到精度更优的调度方案,对问题规模具有良好的可扩展性。

基于SFLA-FCM聚类的城市交通状态判别研究

针对城市道路交通状态判别的问题,提出了一种混合蛙跳算法(SFLA)与模糊C-均值算法(FCM)相结合的SFLA-FCM聚类算法。SFLA是一种全新的后启发式群体进化算法,具有高效的计算性能和优良的全局搜索能力。SFLA-FCM使用SFLA的优化过程代替FCM的基于梯度下降的迭代过程,有效地避免了FCM对初值敏感及容易陷入局部极小的缺陷。将该算法用于城市交通流数据的聚类分析结果表明,与单一FCM聚类算法相比,SFLA-FCM聚类算法更准确,效果更佳,能够快速而有效地对城市交通流状况进行判别,为动态交通拥堵预警和交通诱导策略的制定提供依据。

基于混沌优化策略的SFLA算法

针对基本混合蛙跳算法的缺陷,提出了一种基于混沌优化策略的改进混合蛙跳算法(SFLA)。在青蛙更新策略中引入自适应扰动机制,平衡了算法搜索深度,并利用高斯变异算子代替随机更新操作,提高了算法搜索速度;在全局迭代中借鉴混沌优化策略思想,以概率形式对最优个体进行优化,避免了族群陷入局部最优,并证明了改进算法以概率1收敛于全局最优解。最后用MATLAB对测试函数进行了仿真,仿真结果表明改进的混合蛙跳算法在收敛速度、优化精度上有较大改善。

基于SFLA-M-L模型的景观格局优化研究

以内蒙古自治区巴彦淖尔市磴口县为研究区,基于混合蛙跳算法,耦合逻辑回归与马尔可夫模型构建了SFLA-M-L(Shuffled frog leaping algorithm-Markov-logistic regression)模型。利用逻辑回归,综合考虑高程、坡度、地下水埋深、干旱度指数、归一化植被指数与当前景观分布进行了景观适宜性分析;利用Markov模型,构造了县域景观转移概率矩阵。利用景观适宜性指数和景观聚集度指数构造目标函数,以景观转移概率矩阵为景观变异的控制条件,对2016年景观格局分布进行了县域景观格局优化。优化结果中,景观聚集度为96.71%,比2016年景观分布提升了6.43个百分点;景观适宜性指数为96.23%,比2016年景观分布提升了4.18个百分点;不同景观类型间相互转移超出转移概率矩阵控制仅4.66 km^2,确保了优化结果的合理性。

基于SFLA改进卷积神经网络的滚动轴承故障诊断

针对卷积神经网络(CNN)用于滚动轴承故障诊断时,训练次数比较多,网络结构不容易确定等问题,设计了一种基于混合蛙跳(SFLA)优化CNN的算法(SFLA-CNN),以及基于该算法的滚动轴承故障诊断模型。该模型利用SFLA强大的全局寻优能力和局部深度搜索能力来优化CNN结构,随后运用具有最优结构的CNN模型直接从原始振动信号中提取低维故障特征,并将其输入到Softmax分类器中进行故障识别。与BP神经网络、CNN等方法对比分析,试验结果表明,SFLA-CNN算法具有更高的准确率以及更少的训练次数。

基于MapReduce的并行SFLA-FCM聚类算法

模糊C均值算法(Fuzzy C-Means,FCM)是目前应用比较广泛的一种聚类算法。FCM算法的聚类质量依赖于初始聚类中心的选择并且易陷入局部极值,结合混合蛙跳算法(Shuffled Frog Leaping Algorithm,SFLA)较强的搜索能力,提出一种基于MapReduce的并行SFLA-FCM聚类算法。该算法利用SFLA算法的子群内模因信息传递和全局信息交换来搜索高质量的聚类中心,根据MapReduce编程模型设计算法流程,实现并行化,使其具有处理大规模数据集的能力。实验证明,并行SFLA-FCM算法提高了的搜索能力和聚类结果的精度,并且具有良好的加速比和扩展性。

基于SFLA-GA混合算法求解时间最优的旅行商问题

该文以经典的对称旅行商问题(Symmetric Traveling Salesman Problem,STSP)为基础,求解时间最优的旅行商问题(Time Optimal TSP,TOTSP),将拟合函数引入到混合蛙跳遗传算法(SFLA-GA)的适应度函数来反映景点客流量随时间的变化,旨在旅游旺季为游客提供一条游览时间最短的路径推送服务。实验结果表明:相对于随机游览路径,SFLA-GA混合算法得到的游览路径明显节省了游览时间;与SFLA和混合粒子群遗传算法(PSO-GA)相比较,SFLA-GA混合算法具有计算量少、收敛速度快、对初始种群依赖性低以及全局性更好等优点,在求解TOTSP上搜索性能更强、时间更优。

基于KDDA和SFLA-LSSVR算法的WLAN室内定位算法

针对接收信号强度(received signal strength,RSS)的时变性降低WLAN室内定位精度的问题,提出了一种基于核直接判别分析(kernel direct discriminant analysis,KDDA)和混洗蛙跳最小二乘支持向量回归机(SFLA-LSSVR)的定位算法,该算法通过核函数策略将采集的各接入点(access point,AP)的RSS信号映射到非线性领域,有效提取了非线性定位特征,重组定位信息,去除冗余定位特征和噪声;然后采用LSSVR算法构建指纹点定位特征数据与物理位置的映射关系模型,采用SFLA算法优化该关系模型的参数,并用该关系模型对测试点的位置进行回归预测.实验结果表明:提出算法在相同的采样次数下的定位精度明显优于WKNN,ANN,LSSVR算法,并且在相同的定位精度下,采样次数较大减少,是一种性能良好的WLAN室内定位算法.