Method for electromagnetic detection satellites scheduling based on genetic algorithm with alterable penalty coefficient

The electromagnetic detection satellite （EDS） is a type of earth observation satellites （EOSs）. The Information collected by EDSs plays an important role in some fields, such as industry, science and military. The scheduling of EDSs is a complex combinatorial optimization problem. Current research mainly focuses on the scheduling of imaging satellites and SAR satellites, but little work has been done on the scheduling of EDSs for its specific characteristics. A multi-satellite scheduling model is established, in which the specific constrains of EDSs are considered, then a scheduling algorithm based on the genetic algorithm （GA） is proposed. To deal with the specific constrains of EDSs, a penalty function method is introduced. However, it is hard to determine the appropriate penalty coefficient in the penalty function. Therefore, an adaptive adjustment mechanism of the penalty coefficient is designed to solve the problem, as well as improve the scheduling results. Experimental results are used to demonstrate the correctness and practicability of the proposed scheduling algorithm.

Hybrid Global Optimization Algorithm for Feature Selection

This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm(PLTVACIW-PSO).Its designed has introduced the benefits of Parallel computing into the combined power of TVAC(Time-Variant Acceleration Coefficients)and IW(Inertial Weight).Proposed algorithm has been tested against linear,non-linear,traditional,andmultiswarmbased optimization algorithms.An experimental study is performed in two stages to assess the proposed PLTVACIW-PSO.Phase I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIWPSO vs.IW based Particle Swarm Optimization(PSO)algorithms,TVAC based PSO algorithms,traditional PSO,Genetic algorithms(GA),Differential evolution(DE),and,finally,Flower Pollination(FP)algorithms.In phase II,the proposed PLTVACIW-PSO uses the same 12 known Benchmark functions to test its performance against the BAT(BA)and Multi-Swarm BAT algorithms.In phase III,the proposed PLTVACIW-PSO is employed to augment the feature selection problem formedical datasets.This experimental study shows that the planned PLTVACIW-PSO outpaces the performances of other comparable algorithms.Outcomes from the experiments shows that the PLTVACIW-PSO is capable of outlining a feature subset that is capable of enhancing the classification efficiency and gives the minimal subset of the core features.

Optimization of Submarine Hydrodynamic Coefficients Based on Immune Genetic Algorithm

Aiming at the demand for optimization of hydrodynamic coefficients in submarine's motion equations,an adaptive weight immune genetic algorithm was proposed to optimize hydrodynamic coefficients in motion equations.Some hydrodynamic coefficients of high sensitivity to control and maneuver were chosen as the optimization objects in the algorithm.By using adaptive weight method to determine the weight and target function,the multi-objective optimization could be translated into single-objective optimization.For a certain kind of submarine,three typical maneuvers were chosen to be the objects of study:overshoot maneuver in horizontal plane,overshoot maneuver in vertical plane and turning circle maneuver in horizontal plane.From the results of computer simulations using primal hydrodynamic coefficient and optimized hydrodynamic coefficient,the efficiency of proposed method is proved.

An Objective Penalty Functions Algorithm for Multiobjective Optimization Problem

By using the penalty function method with objective parameters, the paper presents an interactive algorithm to solve the inequality constrained multi-objective programming (MP). The MP is transformed into a single objective optimal problem (SOOP) with inequality constrains;and it is proved that, under some conditions, an optimal solution to SOOP is a Pareto efficient solution to MP. Then, an interactive algorithm of MP is designed accordingly. Numerical examples show that the algorithm can find a satisfactory solution to MP with objective weight value adjusted by decision maker.

Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm

To improve energy efficiency and protect the environment,the integrated energy system(IES)becomes a significant direction of energy structure adjustment.This paper innovatively proposes a wavelet neural network(WNN)model optimized by the improved particle swarm optimization(IPSO)and chaos optimization algorithm(COA)for short-term load prediction of IES.The proposed model overcomes the disadvantages of the slow convergence and the tendency to fall into the local optimum in traditional WNN models.First,the Pearson correlation coefficient is employed to select the key influencing factors of load prediction.Then,the traditional particle swarm optimization(PSO)is improved by the dynamic particle inertia weight.To jump out of the local optimum,the COA is employed to search for individual optimal particles in IPSO.In the iteration,the parameters of WNN are continually optimized by IPSO-COA.Meanwhile,the feedback link is added to the proposed model,where the output error is adopted to modify the prediction results.Finally,the proposed model is employed for load prediction.The experimental simulation verifies that the proposed model significantly improves the prediction accuracy and operation efficiency compared with the artificial neural network(ANN),WNN,and PSO-WNN.

Optimization of the seismic processing phase-shift plus finite-difference migration operator based on a hybrid genetic and simulated annealing algorithm

Although the phase-shift seismic processing method has characteristics of high accuracy, good stability, high efficiency, and high-dip imaging, it is not able to adapt to strong lateral velocity variation. To overcome this defect, a finite-difference method in the frequency-space domain is introduced in the migration process, because it can adapt to strong lateral velocity variation and the coefficient is optimized by a hybrid genetic and simulated annealing algorithm. The two measures improve the precision of the approximation dispersion equation. Thus, the imaging effect is improved for areas of high-dip structure and strong lateral velocity variation. The migration imaging of a 2-D SEG/EAGE salt dome model proves that a better imaging effect in these areas is achieved by optimized phase-shift migration operator plus a finite-difference method based on a hybrid genetic and simulated annealing algorithm. The method proposed in this paper is better than conventional methods in imaging of areas of high-dip angle and strong lateral velocity variation.

Extended Range Guided Munition Parameter Optimization Based on Genetic Algorithms

Many factors influencing range of extended range guided munition （ERGM） are analyzed. The definition domain of the most important three parameters are ascertained by preparatory mathematical simulation, the optimized mathematical model of ERGM maximum range with boundary conditions is created, and parameter optimization based on genetic algorithm （GA） is adopted. In the GA design, three-point crossover is used and the best chromosome is kept so that the convergence speed becomes rapid. Simulation result shows that GA is feasible, the result is good and it can be easy to attain global optimization solution, especially when the objective function is not the convex one for independent variables and it is a multi-parameter problem.

A New Augmented Lagrangian Objective Penalty Function for Constrained Optimization Problems

In this paper, a new augmented Lagrangian penalty function for constrained optimization problems is studied. The dual properties of the augmented Lagrangian objective penalty function for constrained optimization problems are proved. Under some conditions, the saddle point of the augmented Lagrangian objective penalty function satisfies the first-order Karush-Kuhn-Tucker (KKT) condition. Especially, when the KKT condition holds for convex programming its saddle point exists. Based on the augmented Lagrangian objective penalty function, an algorithm is developed for finding a global solution to an inequality constrained optimization problem and its global convergence is also proved under some conditions.

Improving the Cellular Characteristics of Aluminum Foam for Maximum Sound Absorption Coefficient Using Genetic Algorithm

Fabricating of metal foams with desired morphological parameters including pore size,porosity and pore opening is possible now using sintering technology.Thus,if it is possible to determine the morphology of metal foam to absorb sound at a given frequency,and then fabricate it through sintering,it is expected to have optimized metal foams for the best sound absorption.Theoretical sound absorption models such as Lu model describe the relationship between morphological parameters and the sound absorption coefficient.In this study,the Lu model was used to optimize the morphological parameters of aluminum metal foam for the best sound absorption coefficient.For this purpose,the Lu model was numerically solved using written codes in MATLAB software.After validating the proposed codes with benchmark data,the genetic algorithm(GA)was applied to optimize the affecting morphological parameters on the sound absorption coefficient.The optimization was carried out for the thicknesses of 5 mm to 40 mm at the sound frequency range of 250 Hz–8000 Hz.The optimized parameters ranged from 50%to 95%for porosity,0.1 mm to 4.5 mm for pore size,and 0.07 mm to 0.6 mm for pore opening size.The result of this study was applied to fabricate the desired aluminum metal foams for the best sound absorption.The novel approach applied in this study,is expected to be successfully applied in for best sound absorption in desired frequencies.

Generalized Algorithms of Discrete Optimization and Their Power Engineering Applications

Generalized algorithms for solving problems of discrete, integer, and Boolean programming are discussed. These algorithms are associated with the method of normalized functions and are based on a combination of formal and heuristic procedures. This allows one to obtain quasi-optimal solutions after a small number of steps, overcoming the NP-completeness of discrete optimization problems. Questions of constructing so-called “duplicate” algorithms are considered to improve the quality of discrete problem solutions. An approach to solving discrete problems with fuzzy coefficients in objective functions and constraints on the basis of modifying the generalized algorithms is considered. Questions of applying the generalized algorithms to solve multicriteria discrete problems are also discussed. The results of the paper are of a universal character and can be applied to the design, planning, operation, and control of systems and processes of different purposes. The results of the paper are already being used to solve power engineering problems.

Neural Network Pruning Algorithm with Penalty OBS Process

Aimed at the great computing complexity of optimal brain surgeon (OBS) process, a pruning algorithm with penalty OBS process is presented. Compared with sensitive and regularized methods, the penalty OBS algorithm not only avoids time-consuming defect and low pruning efficiency in OBS process, but also keeps higher generalization and pruning accuracy than Levenberg-Marquardt method.

基于遗传算法的磨削力模型系数优化及验证

在磨削力模型求解问题中,目前大多使用分段计算法或列方程组直接计算各个待求系数,不仅计算量大且其精度也无法保证。另外,传统的回归模型容易陷入局部最优,难以描述非线性关系。为此,将遗传算法引入到非线性优化函数参数优化中,基于外圆横向磨削力模型、平面磨削力模型、外圆纵向磨削力模型等现有的模型数据,开展磨削力理论模型的系数优化方法研究。相关性分析结果表明:通过计算得到的3种模型磨削力的预测精度提高了14.69%~42.54%,且3种模型所预测的法向磨削力的平均误差分别为5.9%、9.13%、3.23%,切向力平均误差分别为6.78%、8.36%、3.69%。经对比知,优化后的模型拟合度较好,模型预测精度显著提高。遗传算法优化后的非线性优化函数GA-LSQ算法更适合磨削力模型的求解,可对磨削力的预测及实际加工生产中的参数优化提供参考。

顾及特征离散程度的SEaTH特征优化选择方法

特征选择是面向对象信息提取的关键步骤之一。本文针对分离阈值(separability and threshold,SEaTH)这一特征选择方法在实际应用中存在的不足,例如未考虑特征值的离散程度,仅利用J-M距离评判单一特征,特征间可能存在较强相关性,以及无法有效确定出分类顺序,提出了一种改进的SEaTH算法(optimized SEaTH,OPSEaTH)。OPSEaTH算法首先在J-M距离基础上构建了一类特征评价指标(E值),有效解决了特征值的离散度问题;然后,基于E值构建出特征组合评价指标(C_(e)值),可有效评估得到每种地物的最佳特征组合并自动确定出地物的分类顺序;最后基于eCognition等分类器可完成对地物对象的最终有效分类。利用高分二号遥感影像数据对本文方法进行了测试,并将结果分别与SEaTH算法、DPC、OIF和最近邻分类器的分类结果进行了对比,结果表明:OPSEaTH算法不仅能有效降低特征维数、优化特征空间,还能够对分类顺序进行自动化合理确定,总体精度和Kappa系数及其他精度指标,均显著优于基于SEaTH算法的特征选择结果。本文方法无论从特征降维效果、分类结果精度还是计算效率方面均优于DPC、OIF和最近邻分类器结果。OPSEaTH是一种更优的特征选择方法。

混沌自适应非洲秃鹫优化算法训练多层感知器

针对训练多层感知器(MLP)时,算法对初始值敏感、易陷入局部最优和收敛速度慢等问题,对新型启发式算法非洲秃鹫优化算法提出改进算法IAVOA。在初始化种群时引入Logistic混沌映射,增加种群的多样性;对最优秃鹫和次优秃鹫增加自适应权重系数,自动调整这两类秃鹫对普通秃鹫的引导作用;IAVOA用于MLP的训练,采用均方误差的平均值作为适应度函数寻找MLP的连接权重和偏差的最佳组合。选取4个不同复杂度的分类数据集,比较IAVOA算法与现有启发式算法对MLP训练后,MLP对数据分类的性能,仿真结果表明,IAVOA算法训练的MLP在数据分类准确率、全局搜索能力、收敛速度和稳定性方面均具有良好的性能。

基于特征融合和B-SVM的鸟鸣声识别算法

为了实现在野外通过低成本嵌入式系统识别鸟类,提出了基于特征融合和B-SVM的鸟鸣声识别方法。对鸟鸣声信号提取梅尔频率倒谱系数、翻转梅尔频率倒谱系数、短时能量和短时过零率组成特征参数,通过线性判别算法对特征参数进行特征融合。利用黑寡妇算法通过测试集对支持向量机模型的核参数和损失值进行优化得到B-SVM模型。利用Xeno-canto鸟鸣声数据集对本文算法进行了测试,结果表明该方法的识别准确率为93.23%。算法维度参数的大小和融合特征维度的高低是影响算法识别效果的重要因素。在相同条件下,文中所提的基于特征融合和B-SVM模型的鸟鸣声识别算法相较于其他特征参数和模型,识别的准确率更高,为野外鸟类识别提供了参考。

基于磁耦合谐振的多自由度电机无线电能传输

针对多自由度电机无线电能传输中传输效率较低的问题,提出基于磁耦合谐振的多自由度电机无线电能传输方法。该方法根据共振原理构建磁耦合谐振式无线电能传输模型,通过反射系数描述阻抗匹配状态,获取最佳负载阻抗;采用混沌优化算法优化电机线圈损耗率,实现多自由度电机无线电能传输优化。实验结果表明,应用该方法后,多自由度电机的电能输出功率达到了893 W,传输效率提升了0.10以上,提高了电能输出功率和传输效率,方法有效,具备可行性。

基于集合经验模态分解和多目标遗传算法的火-多储系统调频功率双层优化

针对分布于区域电网不同网络节点的多座储能电站参与电网调频功率调度问题,该文提出一种基于集合经验模态分解(EEMD)和多目标遗传算法(MOGA)的火-多储系统调频功率双层优化策略。该策略包含火-储调频功率优化层和多储能电站调频功率优化层:上层计及火-储调配资源各自优势及剩余调频能力,构建火-储调频功率优化分配模型,完成火-储调频功率的分配;下层引入关于调频成本和荷电状态(SOC)的自适应权重系数,以调频成本最低和SOC均衡为优化目标,完成调频功率在多储能电站之间的分配。仿真结果表明,所提策略可以提升区域电网调频效果并降低调频成本,均衡控制多个储能电站的调频成本和SOC,可以防止经济性较好的储能电站长期处于SOC越限边缘状态,提升储能电站参与调频的积极性和可持续性。

基于F-score和二进制灰狼优化的肿瘤基因选择方法

针对肿瘤基因数据维度高、噪声多、冗余性高的现状,结合Spearman相关系数改进F-score算法,在此基础上优化二进制灰狼算法,提出了一种基于改进F-score和二进制灰狼算法的肿瘤基因选择算法.首先,考虑特征之间的相关性,计算每个特征的F-score值和特征之间的Spearman相关系数的绝对值;然后,计算权重系数得出各个特征的权重值,依据重要性进行排序,选出初选特征子集;最后,通过收敛因子的衰减曲线和初始化方法优化二进制灰狼算法,调整全局搜索和局部搜索所占比例,增强全局搜索能力并提高局部搜索速度,有效节省时间开销,提升特征选择的分类性能和效率,得到最优特征子集.在9个肿瘤基因数据集上测试所提算法,在分类准确率和筛选特征数目两个指标上进行仿真实验,并与4种其他算法进行对比,实验结果证明所提算法表现良好,可有效降低基因数据维度,并具有较好的分类精度.

基于改进NSGA-Ⅱ算法的考虑人机系数生产线平衡多目标优化研究

在现代制造加工生产线中,生产设备是制造业生产的重要组成部分。设备的充分利用对于生产需求的匹配度产生直接影响。文章参考“人机系数”的概念,构建了考虑生产线平衡率、平滑指数和人机系数的多目标优化函数问题,采用了改进的自适应NSGA-Ⅱ算法来求解该模型。研究结果表明,在满足客户节拍要求的前提下,调整人机系数可以提高生产线平衡率,降低平滑指数以及生产成本;通过数值算例验证了相比于传统NSGA-Ⅱ算法,改进的自适应NSGA-Ⅱ算法能寻找出质量较高、覆盖更广的近似Pareto解集,为决策者提供选择。证明了本研究方法的有效性和实用性,该研究为现代制造加工生产线提供了一种有效的优化方法,并为其他类似行业提供了借鉴。

基于混合平衡优化算法的疫苗配送路径优化

针对疫苗配送路径优化问题,在同时考虑固定成本、运输成本、制冷成本、碳排放成本和惩罚成本的情况下,提出以疫苗配送成本最小化为目标的车辆路径优化模型。为求解模型,在平衡优化器算法中引入模拟退火算法,改进平衡优化器算法容易陷入局部最优的不足,通过加入可变参数,提升算法平衡全局搜索和局部寻优的能力,得到一个能够稳定求出较高质量解的混合平衡优化算法。对2种不同规模的算例分别进行20次实验,将混合平衡优化算法与并行平衡优化算法、知识型蚁群算法、混合变邻域搜索算法、改进混合粒子群算法和平衡优化器算法进行对比。实验结果表明,混合平衡优化算法在小规模算例和大规模算例下得到的最小配送成本和配送成本的标准差都小于其他5种算法,其中,在小规模算例下进行实验后得到的最小配送成本分别为其他5种算法的73.5%、53.9%、69.1%、64.1%和33.4%。