BIORV-NSA: Bidirectional inhibition optimization r-variable negative selection algorithm and its application

The original negative selection algorithm (NSA) has the disadvantages that many “black holes” cannot be detected and excessive invalid detectors are generated. To overcome its defects, this paper improves the detection performance of NSA and presents a kind of bidirectional inhibition optimization r-variable negative selection algorithm (BIORV-NSA). The proposed algorithm includes self set edge inhibition strategy and detector self-inhibition strategy. Self set edge inhibition strategy defines a generalized radius for self individual area, making self individual radius dynamically be variable. To a certain extent, the critical antigens close to self individual area are recognized and more non-self space is covered. Detector self-inhibition strategy, aiming at mutual cross-coverage among mature detectors, eliminates those detectors that are recognized by other mature detectors and avoids the production of excessive invalid detectors. Experiments on artificially generating data set and two standard real-world data sets from UCI are made to verify the performance of BIORV-NSA, by comparison with NSA and R-NSA, the experimental results demonstrate that the proposed BIORV-NSA algorithm can cover more non-self space, greatly improve the detection rates and obtain better detection performance by using fewer mature detectors.     

A Cuckoo Search Detector Generation-based Negative Selection Algorithm

The negative selection algorithm (NSA) is an adaptive technique inspired by how the biological immune system discriminates the self from non-self. It asserts itself as one of the most important algorithms of the artificial immune system. A key element of the NSA is its great dependency on the random detectors in monitoring for any abnormalities. However, these detectors have limited performance. Redundant detectors are generated, leading to difficulties for detectors to effectively occupy the non-self space. To alleviate this problem, we propose the nature-inspired metaheuristic cuckoo search (CS), a stochastic global search algorithm, which improves the random generation of detectors in the NSA. Inbuilt characteristics such as mutation, crossover, and selection operators make the CS attain global convergence. With the use of Lévy flight and a distance measure, efficient detectors are produced. Experimental results show that integrating CS into the negative selection algorithm elevated the detection performance of the NSA, with an average increase of 3.52% detection rate on the tested datasets. The proposed method shows superiority over other models, and detection rates of 98% and 99.29% on Fisher’s IRIS and Breast Cancer datasets, respectively. Thus, the generation of highest detection rates and lowest false alarm rates can be achieved.     

A Novel Radius Adaptive Based on Center-Optimized Hybrid Detector Generation Algorithm

Negative selection algorithm (NSA) is one of the classic artificial immune algorithm widely used in anomaly detection. However, there are still unsolved shortcomings of NSA that limit its further applications. For example, the nonself-detector generation efficiency is low; a large number of nonself-detector is needed for precise detection; low detection rate with various application data sets. Aiming at those problems, a novel radius adaptive based on center-optimized hybrid detector generation algorithm (RACO-HDG) is put forward. To our best knowledge, radius adaptive based on center optimization is first time analyzed and proposed as an efficient mechanism to improve both detector generation and detection rate without significant computation complexity. RACO-HDG works efficiently in three phases. At first, a small number of self-detectors are generated, different from typical NSAs with a large number of self-sample are generated. Nonself-detectors will be generated from those initial small number of self-detectors to make hybrid detection of self-detectors and nonself-detectors possible. Secondly, without any prior knowledge of the data sets or manual setting, the nonself-detector radius threshold is self-adaptive by optimizing the nonself-detector center and the generation mechanism. In this way, the number of abnormal detectors is decreased sharply, while the coverage area of the nonself-detector is increased otherwise, leading to higher detection performances of RACO-HDG. Finally, hybrid detection algorithm is proposed with both self-detectors and nonself-detectors work together to increase detection rate as expected. Abundant simulations and application results show that the proposed RACO-HDG has higher detection rate, lower false alarm rate and higher detection efficiency compared with other excellent algorithms.      

An antigen space density based real-value negative selection algorithm

The negative selection algorithm (NSA) is an important detector generation algorithm for artificial immune systems. In high-dimensional space, antigens (data samples) distribute sparsely and unevenly, and most of them reside in low-dimensional subspaces. Therefore, traditional NSAs, which randomly generate detectors without considering the distribution of the antigens, cannot effectively distinguish them. To overcome this limitation, the antigen space density based real-value NSA (ASD-RNSA) is proposed in this paper. The ASD-RNSA contains two new processes. First, in order to improve detection efficiency, ASD-RNSA utilizes the antigen space density to calculate the low-dimensional subspaces where antigens are densely gathered and directly generate detectors in these subspaces. Second, to eliminate redundant detectors and prevent the algorithm from prematurely converging in high-dimensional space, ASD-RNSA suppresses candidate detectors that are recognized by other mature detectors and adopts an “antibody suppression rate” to replace the “expected coverage” as the termination condition. Experimental results show that ASD-RNSA achieves a better detection rate and has better generation quality than classical real-value NSAs.     

基于免疫粒子群算法的多用户检测技术研究

将免疫系统的免疫机制引入到粒子群优化算法的设计中,模拟免疫系统、群集智能和神经网络的信息处理机制,提出了免疫粒子群优化算法。这种免疫粒子群算法结合了粒子群的近似全局优化能力和由Hopfield神经网络构成的免疫系统的快速信息处理机制,加快了算法的收敛速度,并提高了粒子群算法的全局收敛能力。然后利用此算法对CDMA系统的多用户检测性能改进问题进行实验研究,证明了本文的方法有较快的收敛速度,并且无论是抗多址干扰能力还是抗远近效应能力都优于传统方法和一些应用优化算法的多用户检测器。     

QoS-aware web service selection with negative selection algorithm

Web service selection, as an important part of web service composition, has direct influence on the quality of composite service. Many works have been carried out to find the efficient algorithms for quality of service (QoS)-aware service selection problem in recent years. In this paper, a negative selection immune algorithm (NSA) is proposed, and as far as we know, this is the first time that NSA is introduced into web service selection problem. Domain terms and operations of NSA are firstly redefined in this paper aiming at QoS-aware service selection problem. NSA is then constructed to demonstrate how to use negative selection principle to solve this question. Thirdly, an inconsistent analysis between local exploitation and global planning is presented, through which a local alteration of a composite service scheme can transfer to the global exploration correctly. It is a general adjusting method and independent to algorithms. Finally, extensive experimental results illustrate that NSA, especially for NSA with consistency weights adjusting strategy (NSA+), significantly outperforms particle swarm optimization and clonal selection algorithm for QoS-aware service selection problem. The superiority of NSA+ over others is more and more evident with the increase of component tasks and related candidate services.     

基于AKPSO算法的加速度计快速标定方法

针对粒子群优化（ PSO）算法在加速度计标定优化后期出现的早熟、陷入局部最优的不足,以及KalmanPSO（ KPSO）算法在设计与应用过程中存在的缺陷,提出了基于自适应 Kalman 滤波的改进 PSO （ AKPSO）算法,并将其成功应用于加速度计快速标定。利用粒子群状态空间Markov链模型,建立了粒子群系统状态方程和观测方程;采用指数加权的自适应衰减记忆Kalman滤波来对粒子的位置进行估计。加速度计标定仿真结果表明：所提出的算法在收敛速度、收敛精度方面都要优于PSO,KPSO算法,有效地提高了加速度计的标定精度。     

Multiuser detection based on modified PSO algorithm for synchronous CDMA systems

The computational complexity of the optimum maximum-likelihood detector does not allow its utility for multiuser detection (MUD) in code-division multiple-access (CDMA) systems. In this paper, a novel particle swarm optimization (PSO) algorithm is suggested to carry out MUD for synchronous CDMA systems. This work considers two new aspects, namely an adaptive velocity updating mechanism based on Newton method and a dynamic inertia weight into the standard PSO. These mechanisms can provide more diversity to help avoiding premature convergence and significantly improve the bit error rate performance. Several computer simulation results demonstrate that the proposed modified PSO detector significantly outperforms the decorrelating detector, the linear minimum mean square error detector, and the standard PSO-based detector.     

Hybrid forecasting model based on support vector machine and particle swarm optimization with adaptive and Cauchy mutation

This paper presents a novel hybrid forecasting model based on support vector machine and particle swarm optimization with Cauchy mutation objective and decision-making variables. On the basis of the slow convergence of particle swarm algorithm (PSO) during parameters selection of support vector machine (SVM), the adaptive mutation operator based on the fitness function value and the iterative variable is also applied to inertia weight. Then, a hybrid PSO with adaptive and Cauchy mutation operator (ACPSO) is proposed. The results of application in regression estimation show the proposed hybrid model (ACPSO–SVM) is feasible and effective, and the comparison between the method proposed in this paper and other ones is also given, which proves this method is better than other methods.     

混合粒子群和改进细菌觅食的不平衡数据分类

针对细菌觅食优化（Bacterial Foraging Optimization,BFO）算法易陷入局部最优的缺点,提出了混合粒子群优化（Particle Swarm Optimization,PSO）算法与改进的细菌觅食优化（Improved BFO）算法应用于不平衡数据的分类。使用三个数据集测试所提算法的性能,其一是卵巢癌微阵列真实数据,另两个来自UCI数据库的垃圾电子邮件数据最优集和动物园数据集。采用边界合成少数过采样技术（Borderline-SMOTE）和Tomek Link对不平衡数据进行预处理,利用所提算法对不平衡数据进行分类。在改进细菌觅食优化算法的过程中,对趋化过程进行改进,采用粒子群优化算法先进行搜索,将粒子作为细菌进行处理,提高了细菌觅食优化的全局搜索能力。改进复制操作过程,提高优胜劣汰的选择标准。改进迁徙操作过程,防止种群陷入局部最优,防止进化停滞。仿真结果表明,所提算法分类准确度优于现有方法。     

混合拓扑结构的粒子群算法及其在测试数据生成中的应用研究

粒子群算法(PSO)的拓扑结构是影响算法性能的关键因素,为了从根源上避免粒子群算法易陷入局部极值及早熟收敛等问题,提出一种混合拓扑结构的粒子群优化算法(MPSO)并将其应用于软件结构测试数据的自动生成中。通过不同邻域拓扑结构对算法性能影响的分析,采用一种全局寻优和局部寻优相结合的混合粒子群优化算法。通过观察粒子群的多样性反馈信息,对每一代种群粒子以进化时选择全局拓扑结构模型(GPSO)或局部拓扑结构模型(LPSO)的方法进行。实验结果表明,MPSO使得种群的多样性得到保证,避免了粒子群陷入局部极值,提高了算法的收敛速度。     

改进的SAMPSO的软件测试数据自动生成

针对软件测试数据的自动生成提出了一种简化的自适应变异的粒子群算法（SAMPSO）。该算法在运行过程中根据群体适应度方差以及当前最优解的大小来确定当前最佳粒子的变异概率,变异操作增强了粒子群优化算法前期全局搜索能力,去掉了粒子群优化（PSO）算法中进化方程的粒子速度项,仅由粒子位置控制进化过程,避免了由粒子速度项引起的粒子发散而导致后期收敛变慢和精度低问题。实验结果表明该算法在测试数据的自动生成上优于基本的粒子群算法,提高了效率。     

A neural networks-based negative selection algorithm in fault diagnosis

Inspired by the self/nonself discrimination theory of the natural immune system, the negative selection algorithm (NSA) is an emerging computational intelligence method. Generally, detectors in the original NSA are first generated in a random manner. However, those detectors matching the self samples are eliminated thereafter. The remaining detectors can therefore be employed to detect any anomaly. Unfortunately, conventional NSA detectors are not adaptive for dealing with time-varying circumstances. In the present paper, a novel neural networks-based NSA is proposed. The principle and structure of this NSA are discussed, and its training algorithm is derived. Taking advantage of efficient neural networks training, it has the distinguishing capability of adaptation, which is well suited for handling dynamical problems. A fault diagnosis scheme using the new NSA is also introduced. Two illustrative simulation examples of anomaly detection in chaotic time series and inner raceway fault diagnosis of motor bearings demonstrate the efficiency of the proposed neural networks-based NSA.     

一种净化过程钻离子浓度的混合智能预测方法

针对锌湿法冶炼净化过程的复杂性,提出了一种结合粒子群算法和案例推理方法的净化过程Ⅱ段出口钴离子浓度混杂预测模型.考虑到不同时期案例所起的作用不一样,提出了一种综合加权相似函数.针对案例推理方法中属性权重选择和近邻个数的选取问题,提出了带有变异的惯性权重自适应粒子群算法优化方法,优化最近邻算法中特征权重矢量和近邻数,提高案例的检索精度.以净化过程生产数据进行实验验证和对比分析,计算结果表明改进的案例推理模型精度优于神经网络模型,模型预测结果可以作为过程信息用于净化过程的优化控制.     

基于混沌变异的自适应双粒子群优化

针对粒子群优化在解决高维优化问题时收敛性差、搜索效率不高的问题,在对粒子群优化算法收敛性分析的基础上,提出了混沌变异对极值进行扰动的方法,以增强算法摆脱局部最优解的能力.采用自适应惯性权重和局部邻域搜索保持较高的局部搜索性能,并结合双粒子群协同进化的方法,综合平衡优化算法的全局搜索和局部搜索能力.通过对4个典型测试函数进行的对比实验,表明了所提出的算法能大大提高粒子群优化的搜索效率和收敛精度.     

Accurate sub-swarms particle swarm optimization algorithm for service composition

Service composition (SC) generates various composite applications quickly by using a novel service interaction model. Before composing services together, the most important thing is to find optimal candidate service instances compliant with non-functional requirements. Particle swarm optimization (PSO) is known as an effective and efficient algorithm, which is widely used in this process. However, the premature convergence and diversity loss of PSO always results in suboptimal solutions. In this paper, we propose an accurate sub-swarms particle swarm optimization (ASPSO) algorithm by adopting parallel and serial niching techniques. The ASPSO algorithm locates optimal solutions by using sub-swarms searching grid cells in which the density of feasible solutions is high. Simulation results demonstrate that the proposed algorithm improves the accuracy of the standard PSO algorithm in searching the optimal solution of service selection problem.     

基于改进PSO算法的机器人路径规划及实验

针对粒子群优化（PSO）算法存在的优化精度低以及早熟的缺点,提出一种改进的PSO算法用于机器人路径规划．根据梯度下降法中变量沿负梯度方向变化的原则,提出了改进的粒子速度更新模型．为了提高粒子的搜寻效率及精度,增加了自适应粒子位置更新系数．引入ε贪心策略设计了改进的粒子群优化算法．在部分优化测试函数上的多次试验结果表明,所提算法较其他算法模型搜索精度至少提高2倍,收敛速度也有大幅度的提升．将所提算法和改进的DC-HPSO（动态聚类混合粒子群优化）算法应用于静态障碍物下的路径规划仿真和实际试验,结果表明所提模型具有高精度、高效率、高成功率的优点．     

改进粒子群算法优化支持向量机的短期负荷预测

针对支持向量回归机在预测建模中的参数选取问题,提出一种基于混沌自适应策略的粒子群优化支持向量回归机参数的方法.采用混沌映射算法和聚合度自适应判断策略,增强种群的全局寻优性能,提升粒子的多样性,从而避免种群过早收敛.充分考虑天气、节假日、居民消费等因素的影响,提出一种改进的支持向量回归机预测模型并与粒子群算法的支持向量回...     

一种检测器长度可变的非选择算法

检测器生成是非选择算法的关键步骤.已有检测器生成算法在生成检测器时存在"漏洞"区域和冗余检测器问题.提出了一种检测器长度可变的检测器生成算法,不仅可以消除"漏洞"区域,还可以通过相应的检测器优化算法减少冗余检测器,进而提高检测器生成效率和检测效率.对算法进行了分析和实验证明,结果表明,该算法比传统的非选择算法及r可变的非选择算法具有更好的性能.     

基于相似日理论和IPSO-Elman模型的短期光伏发电功率预测

为提高光伏发电功率预测精度，提出一种基于相似日理论和改进的IPSO-Elman神经网络模型的短期光伏发电功率预测方法。将历史数据细分为不同季节不同天气类型的多个子集，通过灰色关联度和余弦相似度组合而成的综合关联度指标筛选相似日。针对标准粒子群算法的缺陷，提出一种改进的自适应混沌变异粒子群算法(IPSO)来优化Elman神经网络，将优化得出的最优权值和阈值作为初始值建立IPSO-Elman神经网络模型，对3种不同季节和天气类型条件下的光伏发电功率分别预测。选用甘肃省某光伏电站2014年数据进行实例分析，结果表明，IPSO-Elman模型在不同天气类型条件下的功率预测效果都有明显提高。