基于新型非支配排序的多目标麻雀优化算法

针对麻雀搜索算法在求解多目标问题中的不足,并且在求解过程中易陷入局部最优与收敛性差的问题,提出了一种改进的多目标麻雀搜索算法。首先,引入了新型非支配排序,找到最优前沿面;其次,将多项式变异和正余弦算法融合到种群进化策略中,增强其搜索能力,通过竞争机制的种群选择方法,降低搜索过程中局部最优粒子和全局最优粒子导致的误差;最后,将改进算法与多种多目标算法在标准测试函数上进行对比,仿真结果表明,改进算法的收敛性与搜索能力均优于其他算法。由此说明该算法具有可靠的多目标寻优能力,能够有效解决多目标优化问题。     

面向交通信号优化改进快速非支配排序遗传算法研究

交叉口作为交通流调度的重要组成部分,其交通信号配时将直接影响道路通行效率。针对快速非支配排序遗传算法(NSGA II)的精英保留策略会使大量冗余的高排序级别个体同时作为精英保留到下一代,极易发生早熟收敛现象问题,提出了改进的快速非支配排序遗传算法(I-NSGA II),并将其应用于交通信号多目标优化问题。I-NSGA II提出了冗余个体标记方法,之后的精英保留策略会通过该标记来判断去除冗余个体并将其并入临时层级,最后在生成的新种群规模不足时,会从临时层级中取出相应规模的冗余个体,对其进行变异操作后并入新种群。实验表明I-NSGA II在保证停车率和排队长度基本不变的情况下,减少了车辆及行人延误,证明所提出的算法可提高交通路口综合交通效益。     

A hybrid multi-objective approach based on the genetic algorithm and neural network to design an incremental cellular manufacturing system

One important issue related to the implementation of cellular manufacturing systems (CMSs) is to decide whether to convert an existing job shop into a CMS comprehensively in a single run, or in stages incrementally by forming cells one after the other, taking the advantage of the experiences of implementation. This paper presents a new multi-objective nonlinear programming model in a dynamic environment. Furthermore, a novel hybrid multi-objective approach based on the genetic algorithm and artificial neural network is proposed to solve the presented model. From the computational analyses, the proposed algorithm is found much more efficient than the fast non-dominated sorting genetic algorithm (NSGA-II) in generating Pareto optimal fronts.     

基于多目标粒子群优化的虚拟网络映射算法

在虚拟网络映射中,多数研究只考虑一个映射目标,不能体现多方的利益。为此,将多目标算法和粒子群算法结合,提出了一种基于多目标粒子群优化（PSO）的虚拟网络映射算法（VNE-MOPSO）。首先,在基本的粒子群算法中引入交叉算子,扩大了种群优化的搜索空间;其次,在多目标优化算法中引入非支配排序、拥挤距离排序,从而加快种群的收敛;最后,以同时最小化成本和节点负载均衡度为虚拟网络映射目标函数,采用多目标粒子群优化算法求解虚拟网络映射问题（VNMP）。实验结果表明,采用该算法求解虚拟网络映射问题,在网络请求接受率、平均成本、平均节点负载均衡度、基础设施提供商的收益等方面具有优势。     

A genetic algorithm approach for multi-objective optimization of supply chain networks

Supply chain network (SCN) design is to provide an optimal platform for efficient and effective supply chain management. It is an important and strategic operations management problem in supply chain management, and usually involves multiple and conflicting objectives such as cost, service level, resource utilization, etc. This paper proposes a new solution procedure based on genetic algorithms to find the set of Pareto-optimal solutions for multi-objective SCN design problem. To deal with multi-objective and enable the decision maker for evaluating a greater number of alternative solutions, two different weight approaches are implemented in the proposed solution procedure. An experimental study using actual data from a company, which is a producer of plastic products in Turkey, is carried out into two stages. While the effects of weight approaches on the performance of proposed solution procedure are investigated in the first stage, the proposed solution procedure and simulated annealing are compared according to quality of Pareto-optimal solutions in the second stage.     

GPR target detection using a neural network classifier designed by a multi-objective genetic algorithm

Ground Penetrating Radar (GPR) is an electromagnetic sensing technology employed for localization of underground utilities, pipes, and other types of objects. The radargrams typically obtained have a high dimensionality, containing a number of signatures with hyperbolic pattern shapes, and can be processed to retrieve information about the target’s locations, depths and material type of underground soil. The classical Hough Transform approach used to reconstruct these hyperbola shapes is computationally expensive, given the large dimensionality of the radargrams. In literature, several approaches propose to first approximate the location of hyperbolas to small segments through a classification stage, before applying the Hough transform over these segments. However, the published classifiers designed for this task present a relatively complex architecture.Aiming at an improved target localization, we propose an alternative classification methodology. The goal is to classify windows of GPR radargrams into two classes (with or without target) using a neural network radial basis function (RBF), designed via a multi-objective genetic algorithm (MOGA). To capture samples’ fine details, high order statistic cumulant features (HOS) were used. Feature selection was performed by MOGA, with an optional prior reduction using a mutual information (MIFS) approach. The obtained results demonstrate improvement of the classification performance when compared with other models designed with the same data and are among the best results available in the literature, albeit the large reduction in classifier complexity.     

改进的遗传算法在神经网络结构优化中的应用

为了解决人工神经网络隐层节点数目难以确定的问题,针对三层BP神经网络提出了一种最大上限隐层节点数模型,并用改进的遗传算法对其优化。最后,将优化的神经网络对语音特征信号进行分类。仿真结果表明优化后的神经网络具有很好的泛化能力,验证了该方法的有效性。     

基于伪并行混合遗传算法的神经网络优化

在分析并行多物种遗传算法应用于神经网络拓扑结构的设计和学习之后,提出一种伪并行遗传（PPGA-MBP）混合算法,结合改进的BP算法对多层前馈神经网络的拓扑结构进行优化。算法编码采用基于实数的层次混合方式,允许两个不同结构的网络个体交叉生成有效子个体。利用该算法对N-Parity问题进行了实验仿真,并对算法中评价函数各部分系数和种群规模对算法的影响进行了分析。实验证明取得了明显的优化效果,提高了神经网络的自适应能力和泛化能力,具有全局快速收敛的性能。     

递阶遗传粒子群算法在神经网络设计中的应用

将递阶遗传粒子群算法（HGAPSO）应用于神经网络设计,可以在对网络拓扑结构优化的同时对连接权重进行求解。该算法结合了遗传算法在解决离散问题和粒子群算法在解决连续问题上的优势,并利用BP算法沿误差最速下降的能力对连接权重进一步学习,达到全局最优和快速搜索的有机结合。通过对混沌时序信号的预测,表明递阶遗传粒子群算法在较大程度上提高了神经网络的学习性能和泛化能力。     

An optimal image watermarking approach based on a multi-objective genetic algorithm

In accordance with the multi-objective nature of image watermarking, an optimal image watermarking approach using a multi-objective genetic algorithm is presented in this paper. Both watermarking parameters and embedding positions are often important factors affecting the performance of watermarking systems. The proposed multi-objective watermarking method can automatically optimize system parameters, and a variable-length mechanism is specially designed to search the most suitable positions for embedding watermarks. The method can also remove the difficult issue of determining optimal watermarking parameters from previous watermarking algorithms. The proposed multi-objective watermarking method directly deals with the problem of optimizing watermarking under non-dominated meaning, thus it can effectively avoid the difficulty of determining the optimally weighted factor in existing single-objective watermarking schemes. In addition, a Pareto-optimal set generated by multi-objective optimization can provide flexibility in selecting the most suitable watermarking parameters according to practical requirements.     

基于改进遗传算法的RBF神经网络结构优化研究

针对RBF神经网络隐含层节点数过多导致网络结构复杂的问题,提出了一种基于改进遗传算法(IGA)的RBF神经网络优化算法。利用IGA优化基于正交最小二乘法的RBF神经网络结构,通过对隐含层输出矩阵的列向量进行全局寻优,从而设计出结构更优的基于IGA的RBF神经网络(IGA-RBF)。将IGA-RBF神经网络的学习算法应用于电子元器件贮存环境温湿度预测模型,与基于正交最小二乘法的RBF神经网络进行比较的结果表明:IGA-RBF神经网络设计出来的网络训练步数减少了44步,隐含层节点数减少了34个,且预测模型得到的温湿度误差较小,拟合精度大于0.95,具有更高的预测精度。     

利用遗传模拟退火算法优化神经网络结构

常用的神经网络是通过固定的网络结构得到最优权值,使网络的实用性受到影响。引入了一种基于方向的交叉算子和变异算子,同时把模拟退火算法引入了遗传算法,结合遗传算法和模拟退火算法的优点,提出了一种优化神经网络结构的遗传——模拟退火混合算法,实现了网络结构和权值的同时优化。仿真实验表明,与遗传算法和模拟退火算法相比,该算法优化的神经网络收敛速度较快、预测精度较高,提高了网络的处理能力。     

利用影响因子遗传算法优化前馈神经网络

提出了一种带有影响因子的改进遗传算法并以此来优化前馈神经网络.染色体的每个基因都有一个影响因子,其不同取值体现了基因对整条染色体的不同影响程度.在遗传进化过程中,通过影响因子等遗传操作以达到对前馈神经网络的权值、阈值和结构优化的目的.仿真实验表明,该算法能够快速地确定神经网络的结构并且有效地提高了神经网络的收敛速度.     

遗传算法优化神经网络权值盲均衡算法的研究

将遗传算法与神经网络盲均衡算法相结合,提出了两段式优化神经网络权值的方案。首先利用遗传算法全局搜索能力强的特点优化初始权值,然后发挥BP算法局部搜索速度快的特点得到最佳权值。经计算机仿真表明,该算法与传统BP神经网络盲均衡算法相比,收敛速度加快,稳态剩余误差减小,误码率降低。     

Input-dependent neural network trained by real-coded genetic algorithm and its industrial applications

This paper presents an input-dependent neural network (IDNN) with variable parameters. The parameters of the neurons in the hidden nodes adapt to changes of the input environment, so that different test input sets separately distributed in a large domain can be tackled after training. Effectively, there are different individual neural networks for different sets of inputs. The proposed network exhibits a better learning and generalization ability than the traditional one. An improved real-coded genetic algorithm (RCGA) Ling and Leung (Soft Comput 11(1):7–31, 2007) is proposed to train the network parameters. Industrial applications on short-term load forecasting and hand-written graffiti recognition will be presented to verify and illustrate the improvement.     

基于自适应递阶遗传算法的神经网络优化策略

基于递阶结构的遗传算法可以同时对多层前向神经网络进行结构优化和权重求解。与基本的遗传算法相比，这种算法不仅在权重训练方面更加快速稳定，而且能在学习过程中确定网络的拓扑结构，具有较高的学习效率，而在遗传过程中采用自适应的交叉和变异概率能有效加快遗传速度和避免早熟现象的出现。     

基于无约束优化和遗传算法的贝叶斯网络结构学习方法

基于无约束优化和遗传算法,提出一种学习贝叶斯网络结构的限制型遗传算法.首先构造一无约束优化问题,其最优解对应一个无向图.在无向图的基础上,产生遗传算法的初始种群,并使用遗传算法中的选择、交叉和变异算子学习得到最优贝叶斯网络结构.由于产生初始种群的空间是由一些最优贝叶斯网络结构的候选边构成,初始种群具有很好的性质.与直接使用遗传算法学习贝叶斯网络结构的效率相比,该方法的学习效率相对较高.     

自适应遗传算法优化神经网络的入侵检测研究

入侵检测是一种动态的安全防护技术,能够对网络内部、外部攻击进行防御.基于神经网络的入侵检测是常见的智能入侵检测方法.针对神经网络算法易陷入局部极值和简单遗传算法收敛速度慢的问题,提出了一种将神经网络和遗传算法相结合,用遗传算法优化神经网络权值,在遗传算法优化神经网络时采用自适应遗传操作.将自适应遗传算法优化神经网络算法应用于入侵检测系统中,实验结果表明,该方法能够有效的提高系统的检测率,降低误报率和漏报率.     

用随机神经网络优化求解改进算法的研究

随机神经网络是一种仿照实际的生物神经网络的生理机制而定义的网络,其网络结构及应用具有自身的特点。在详细讨论了动态随机神经网络求解典型NP优化问题TSP的算法的同时,特别提出了一种有效改进算法,使得参数在简单选取的情况下保证能量函数的下降,在组合优化问题上具有普遍意义,并且在10城市TSP对改进算法进行验证,指出RNN是解决TSP问题的有效途径。     

物流配送路径多目标优化的聚类-改进遗传算法

探讨运输车辆路线安排调度问题的解决方法，提出一种先用优先级综合聚类分析法将客户分类，再用带有控制开关系统的改进遗传算法求解多目标VRP的优化方法。构造了一种随机开关，以此控制遗传算法中的变异运算，增加了群体的多样性，避免了遗传算法中“局部最优现象”的发生。计算机仿真实验证明了该算法的有效性。