应用精英反向学习的多目标烟花爆炸算法

现实中的多目标优化问题越来越多,而且日益复杂.受混合多目标优化算法设计思想的启发,将烟花爆炸方法和精英反向学习机制引入至多目标优化领域,提出一种应用精英反向学习的多目标烟花爆炸算法(Multi-Objective Fireworks Optimization Algorithm Using Elite Opposition-Based Learning,MOFAEOL).该算法利用精英反向学习策略加强算法的全局搜索能力,利用烟花爆炸方法增强算法的局部搜索能力并提高求解的精度.这两种搜索机制相互协同以更好地平衡算法的全局勘探和局部开采的能力.MOFAEOL算法与另外5种代表性多目标优化算法一同在由ZDT系列和DTLZ系列组成的测试集上进行性能比较.实验表明,MOFAEOL算法在收敛性、多样性和稳定性方面均优于或部分优于其他对比算法.     

基于灰狼算法的抗干扰跳频序列设计

为了提高跳频通信系统的抗干扰能力,本文提出一种结合干扰信息综合考虑跳频序列的汉明相关性、均匀性、被干扰概率和跳频增益的抗干扰跳频序列优化设计方法。使用改进灰狼算法求解优化问题,提出新型反向学习策略和使用莱维飞行的个体信息交流机制,提升灰狼算法性能。通过基本测试函数的仿真实验表明本文的改进灰狼算法比其他灰狼算法有更好的收敛速度和求解精度。本文的抗干扰跳频序列优化设计结果表明,在多种干扰情况下,本文方法设计的跳频序列相比于传统跳频序列和宽间隔跳频序列具有更强的抗干扰能力。      

基于改进灰狼算法的铁路隧道射线跟踪模型校正

针对灰狼优化（grey wolf optimization，GWO）算法易陷入局部最优和收敛精度差的问题，提出了一种基于对立搜索和Levy飞行策略的改进灰狼优化算法——OLGWO算法.在算法初始化阶段，采用对立搜索策略以缩小可行解范围；在灰狼位置更新过程中，为避免算法陷入局部最优采用了Levy飞行策略.4个标准测试函数的仿真实验表明，所提OLGWO算法在收敛速度及求解精度方面均优于GWO算法，可以较快且准确地搜索到目标函数的最优值.基于OLGWO算法对隧道射线跟踪传播模型进行校正的结果表明，校正后的模型在均方根误差和线性相关性方面具有较优的性能，能够实现铁路隧道环境中信号接收功率的精确预测.     

一种精英反向学习的粒子群优化算法

为解决传统粒子群优化算法易出现早熟的不足,提出了精英反向学习策略,引入精英粒子,采用反向学习生成其反向解,扩大搜索区域的范围,可增强算法的全局勘探能力.同时,为避免最优粒子陷入局部最优而导致整个群体出现搜索停滞,提出了差分演化变异策略,采用差分演化算法搜索最优粒子的邻域空间,可增强算法的局部开采能力.在14个测试函数上将本文算法与多种知名的PSO算法进行对比,实验结果表明本文算法在解的精度与收敛速度上更优.     

精英反向黄金正弦鲸鱼算法及其工程优化研究

针对鲸鱼优化算法（Whale Optimization Algorithm,WOA）存在的收敛速度慢、寻优稳定性不足等问题,本文提出了精英反向学习的黄金正弦鲸鱼优化算法（Elite Opposition-Based Golden-Sine Whale Optimization Algorithm,EGolden-SWOA）.利用精英反向学习策略提高种群的多样性和质量可以有效提升算法的收敛速度,同时引入黄金分割数优化WOA的寻优方式,从而协调算法的全局探索与局部开发能力.对20个单模态和多模态测试函数进行寻优实验,并与RLPSO（Reverse-learning and Local-learning Particle Swarm Optimization）、IWOA（Improved Whale Optimization Algorithm based on nonlinear convergence factor）等多个算法进行对比,实验结果表明EGolden-SWOA具有更好的寻优精度和稳定性.进一步对EGolden-SWOA进行求解大规模问题的实验,实验结果表明EGolden-SWOA可以有效解决大规模优化问题.最后将EGolden-SWOA应用于压力容器和蝶形弹簧设计优化问题,结果表明EGolden-SWOA在工程优化方面的性能优于RCSA（Rough Crow Search Algorithm）、CPSO（Co-evolutionary Particle Swarm Optimization）等改进算法,可以有效运用于实际工程优化问题.     

基于免疫算法的 TD-SCDMA 网络基站选址优化

针对已有3G基站选址优化算法的不足和TD-SCDMA网络的特点,提出了一种基于免疫算法的TD-SCDMA网络基站选址优化方案。建立了基站选址问题的数学模型,设计了基于反学习的种群初始化方案和精英交叉策略,给出了免疫优化算法框架。实验结果表明,该算法不仅能够以较小的建站代价获得较高的网络覆盖率,而且算法具有较好的收敛性。     

一种邻域重心反向学习的粒子群优化算法

粒子群优化算法使用反向学习技术可以提高性能.然而,现有的反向学习粒子群优化算法仅采用粒子最大最小边界计算反向解,没有充分利用群体搜索经验.针对此问题,提出了一种邻域重心反向学习策略,使用邻域重心作为参考点计算反向解,充分吸收群体搜索经验的同时保持种群多样性;采用收缩因子拓展反向解搜索范围,增加找到更高质量解的机率.在典型的基准测试函数、CEC'13测试函数和一个实际工程优化问题上进行验证,实验结果说明了邻域重心反向学习策略的有效性和本文算法的竞争力.     

Opposition-based particle swarm optimization with adaptive elite mutation and nonlinear inertia weight

An opposition-based particle swarm optimization with adaptive elite mutation and nonlinear inertia weight (OPSO-AEM＆NIW) was proposed to overcome the drawbacks, such as falling into local optimization, slow convergence speed of opposition-based particle swarm optimization. Two strategies were introduced to balance the contradiction be-tween exploration and exploitation during its iterations process. The first one was nonlinear adaptive inertia weight (NIW), which aim to accelerate the process of convergence of the algorithm by adjusting the active degree of each parti-cle using relative information such as particle fitness proportion. The second one was adaptive elite mutation strategy (AEM), which aim to avoid algorithm trap into local optimum by trigging particle's activity. Experimental results show OPSO-AEM＆NIW algorithm has stronger competitive ability compared with opposition-based particle swarm optimiza-tions and its varieties in both calculation accuracy and computation cost.     

An Improved Grasshopper Optimization Algorithm for Global Optimization

We proposes an improved grasshopper algorithm for global optimization problems. Grasshopper optimization algorithm (GOA) is a recently proposed meta-heuristic algorithm inspired by the swarming behav-ior of grasshoppers. The original GOA has some drawbacks, such as slow convergence speed, easily falling into local optimum, and so on. To overcome these shortcomings, we proposes a grasshopper optimization algorithm based on a logistic Chaos maps opposition-based learning strategy and cloud model inertia weight (CCGOA). CCGOA is divided into three stages. The chaos opposition learning initialization strategy is used to initialize the population, so that the population can be evenly distributed in the feasible solution space as much as possible, so as to improve the uniformity and diversity of the initial population distribution of the grasshopper algorithm. The inertia weight cloud model is introduced into the grasshopper algorithm, and different inertia weight strategies are used to adjust the convergence speed of the algorithm. Based on the principle of chaotic logistic maps, local depth search is carried out to reduce the probability of falling into local optimum. Fourteen benchmark functions and an engineering example are used for simulation verification. Experimental results show that the proposed CCGOA algorithm has superior performance in determining the optimal solution of the test function problem.     

蝙蝠优化的二维Tsallis熵多阈值SAR图像分割

针对智能优化SAR图像分割算法存在计算量大、易陷入局部最优、分割精度不够等问题,融合蝙蝠算法和二维Tsallis熵多阈值,提出了一种蝙蝠优化的二维Tsallis熵多阈值SAR图像分割算法。算法利用立方映射均匀化初始蝙蝠种群,引入Levy飞行特征加强算法跳出局部最优能力,使用Powell局部搜索加快算法收敛等3方面改进蝙蝠算法;同时将二维Tsallis熵单阈值分割方法扩展到多阈值分割,建立基于多阈值的选取方法,并结合改进的蝙蝠算法,将二维Tsallis熵多阈值应用于SAR图像分割中。仿真结果表明,与其他智能优化分割算法相比,本分割算法在边缘处理和分割精度上都有明显优势。     

改进的量子遗传算法及其在WMSN覆盖优化中的应用

对量子遗传算法进行了研究。量子遗传算法只使用一个最优染色体来指导种群的进化,极易陷入局部最优,本文对此进行了改进,提出使用多个精英染色体来指导整个种群的进化。讨论了精英染色体的产生、维护与作用,并在此基础之上提出了一种基于精英组的量子遗传算法(elite groupbased quantum genetic algorithm,EQGA)。最后,将EQGA应用到无线多媒体传感器网络的覆盖优化问题中。对比测试表明,EQGA求出的解比遗传算法和量子遗传算法求出的解都要好。     

基于改进型鲸鱼优化算法和最小二乘支持向量机的炼钢终点预测模型研究

终点碳含量是决定钢质量的关键因素,是转炉炼钢过程中需要控制的核心变量之一.本文建立了一种基于莱维飞行的鲸鱼优化算法（Levy Whale Optimization Algorithm,LWOA）和最小二乘向量机（Least Squares Support Vector Machine,LSSVM）的钢水终点碳含量综合预测模型.通过莱维飞行代替了传统鲸鱼优化算法（Whale Optimization Algorithm,WOA）参数的随机选择,优化了鲸鱼算法中跳出局部最优的能力;借助改变鲸鱼算法的系数向量收敛方式明显提高了鲸鱼优化算法的泛化能力、预测精度和收敛速度.数据仿真结果表明,所提出的LWOA-LSSVM预测模型,不仅能够克服局部寻优获取全局最优解,而且具有快速的收敛速度和更高的预测精度,得出预测结果的均方根误差、平均绝对误差和平均绝对百分比误差与遗传算法BP神经网络、遗传算法最小二乘支持向量机和传统鲸鱼算法最小二乘支持向量机相比均有着明显提高.同时,通过调整目标命中率和训练输入样本量验证了预测模型具有更好的鲁棒性.     

基于改进鲸鱼优化策略的贝叶斯网络结构学习算法

针对当前贝叶斯网络结构学习算法易陷入局部最优和寻优效率低的问题,该文提出一种基于改进鲸鱼优化策略的贝叶斯网络结构学习算法。该算法首先提出一种新的方法建立较优的初始种群,然后利用不产生非法结构的交叉变异算子构建适用于贝叶斯网络结构学习的改进捕食行为,同时采用动态调节参数增强算法个体寻优的能力,通过适应度排序更新种群,最终获得最优的贝叶斯网络结构。仿真结果表明,该算法具有全局收敛性,寻优效率高,精确率高于其它同类优化算法。     

基于变因子加权学习与邻代维度交叉策略的改进CSA算法

针对乌鸦搜索算法（CSA）优化高维问题时存在寻优精度低、局部极值逃逸能力弱等问题,提出一种耦合多个体变因子加权学习机制与最优个体邻代维度交叉策略的改进乌鸦搜索算法（ICSA）.该算法随迭代进程动态修正模型控制参数（感知概率和飞行长度）,利用多个体的变因子加权学习机制保证子代个体同时继承跟随乌鸦与上代最优个体的位置信息以避免单个体继承的过快种群同化并减小陷入局部极值的风险;同时构建历史最优个体的邻代维度交叉策略,并按维度绝对差异大的优先替换原则更新最优个体位置,以保留历代最优维度信息并提高算法的局部极值逃逸能力.数值实验结果分别验证了模型参数对CSA算法性能的一定影响,加权学习因子不同递变形式对ICSA算法性能改善的有效性与差异性以及改进算法的优越寻优性能.     

改进海鸥算法的多阈值图像分割算法北大核心CSCD

为进一步提高图像分割精度,改善传统多阈值图像分割方法计算量大、分割慢的问题,提出了改进海鸥算法(improved seagull optimization algorithm,ISOA)的多阈值图像分割方案。针对原始海鸥算法(seagull optimization algorithm,SOA)存在早熟、寻优精度不足的问题,首先,采用cubic混沌映射优化初始解,提高搜索效率;其次,引入鹰栖息优化算法(eagle perching optmizer,EPO)的缩放因子和疯狂算子进行扰动,并与麻雀搜索算法(sparow search algorithm,SSA)警戒者的位置更新相结合,优化寻优精度和收敛速度,避免陷入局部最优。利用6种基准测试函数对ISOA进行寻优性能测试。最后,将ISOA与图像分割的最优阈值选取相结合,进行基于Otsu的多阈值图像分割,并与现有分割算法进行对比。仿真结果表明,ISOA在基于Otsu的图像分割中,100%取得了最优值,且80.9%的结果优于其余算法,使图像的分割精度和质量均得到了优化。     

Tracking via Square Grid of RFID Reader Positioning and Diffusion Algorithm

The study and analysis of RFID (radio frequency identification) reader positioning is important for RFID large-scale deployment. Therefore, the purpose of this research is to investigate the art of RFID reader positioning in order to develop a highly accurate positioning and tracking system for usage inside a building and also to optimize the tracking performance that can be applied to different active and passive RFID standards. This study involves design of square grid RFID reader network and position calculation using diffusion algorithm. Square grid network presents the solution of placement pattern of RFID readers, hence optimal number of required readers and guaranteed coverage can be achieved. The proposed diffusion algorithm makes use of distance information between the reader and the tag to estimate the RFID tag position. This study has also introduced a probabilistic mathematical model to maintain coverage of the unit square region and to ensure connectivity of the proposed square grid network. The effectiveness of the diffusion algorithm is evaluated in this study and the obtained results show satisfactory performance. The obtained results show that the proposed positioning system can achieve average positioning error below 1 m with different RFID reading ranges and in some cases accuracy improvement of about 65% can be reached than the results obtained by known positioning system. It is also found that the proposed diffusion algorithm together with square grid can bring approximately 50–85% improvement on raw accuracy provided by only square grid method.     

基于改进麻雀算法的最大2维熵分割方法

为了提高最大2维熵分割的性能,提出了基于改进麻雀算法的最大2维熵分割方法,可减小运算量并且缩短计算时间.首先,融合反向学习策略和自适应t分布变异,引入精英粒子,以扩大算法搜索范围,增加算法后期局部搜索能力;其次,使用萤火虫机制,对最优解进行扰动变异,进一步增加种群多样性;最后,采用提出的改进麻雀算法寻找图像最大2维熵,...     

SINR概率感知下的无人机覆盖优化

针对多无人机网络辅助灾区用户通信的场景,构建了一种基于信干噪比(Signal-to-Interference plus Noise Ratio,SINR)检测的概率感知模型,旨在最大化无人机服务区域的覆盖率,同时降低无人机额外能耗,并提升网络吞吐量。在该模型下,提出了两种改进的麻雀搜索算法,分别为Logistic高斯麻雀搜索算法(Logistic Gaussian Sparrow Search Algorithm,LGSSA)和Logistic柯西麻雀搜索算法(Logistic Cauchy Sparrow Search Algorithm,LCSSA)。首先使用Logistic混沌序列产生初始种群,以丰富种群的多样性,提高算法的全局搜索能力;然后,在LGSSA和LCSSA中分别引入高斯变异和柯西变异因子,以改善局部最优解。仿真结果表明改进后的算法可以有效地优化无人机的空中部署,大幅度提升无人机网络的覆盖率。     

基于微分进化的信息物理融合系统网关优化部署方法

为解决信息物理融合系统下无线传感器网络接入互联网的问题,该文提出一种基于微分进化的网关优化部署方法。该方法以最小化网关覆盖半径和负载平衡水平为优化目标,利用微分进化算法寻找网关优化部署方案,通过自适应反向搜索、动态参数调整等方法保障寻优过程的种群多样性,解决几何 中心下的网关部署问题。仿真实验结果表明,该方法具备良好的全局寻优能力和收敛速度,能够在缩小网关集覆盖半径的前提下,进一步平衡各网关之间的负载水平,提高信息物理融合系统的网络QoS水平。     

Hybrid gray wolf optimization-cuckoo search algorithm for RFID network planning

In recent years, with the rapid development of Internet of things (IoT) technology, radio frequency identification (RFID) technology as the core of IoT technology has been paid more and more attention, and RFID network planning(RNP) has become the primary concern. Compared with the traditional methods, meta-heuristic method is widely used in RNP. Aiming at the target requirements of RFID, such as fewer readers, covering more tags, reducing the interference between readers and saving costs, this paper proposes a hybrid gray wolf optimization-cuckoo search (GWO-CS) algorithm. This method uses the input representation based on random gray wolf search and evaluates the tag density and location to determine the combination performance of the reader's propagation area. Compared with particle swarm optimization ( PSO) algorithm, cuckoo search( CS) algorithm and gray wolf optimization ( GWO) algorithm under the same experimental conditions, the coverage of GWO-CS is 9.306% higher than that of PSO algorithm, 6.963% higher than that of CS algorithm, and 3.488% higher than that of GWO algorithm. The results show that the GWO-CS algorithm cannot only improve the global search range, but also improve the local search depth.