Evolution strategies with cumulative step length adaptation on the noisy parabolic ridge

This paper presents an analysis of the performance of the (μ/μ,λ)-ES with isotropically distributed mutations and cumulative step length adaptation on the noisy parabolic ridge. Several forms of dependency of the noise strength on the distance from the ridge axis are considered. Closed form expressions are derived that describe the mutation strength and the progress rate of the strategy in high-dimensional search spaces. It is seen that as for the sphere model, larger levels of noise present lead to cumulative step length adaptation generating increasingly inadequate mutation strengths, and that the problem can be ameliorated to some degree by working with larger populations.     

Performance analysis of evolutionary optimization with cumulative step length adaptation

Iterative algorithms for continuous numerical optimization typically need to adapt their step lengths in the course of the search. While some strategies employ fixed schedules, others attempt to adapt dynamically in response to the outcome of trial steps or the history of the search process. Evolutionary algorithms are of the latter kind. A control strategy that is commonly used in evolution strategies is the cumulative step length adaptation approach. This paper presents a theoretical analysis of that adaptation strategy. The analysis includes the practically relevant case of noise interfering in the optimization process. Recommendations are made with respect to choosing appropriate population sizes.     

Step length adaptation on ridge functions

Step length adaptation is central to evolutionary algorithms in real-valued search spaces. This paper contrasts several step length adaptation algorithms for evolution strategies on a family of ridge functions. The algorithms considered are cumulative step length adaptation, a variant of mutative self-adaptation, two-point adaptation, and hierarchically organized strategies. In all cases, analytical results are derived that yield insights into scaling properties of the algorithms. The influence of noise on adaptation behavior is investigated. Similarities and differences between the adaptation strategies are discussed.     

Harris Hawk Optimization Algorithm Based on Cauchy Distribution Inverse Cumulative Function and Tangent Flight Operator

Harris Hawk Optimization (HHO) algorithm is a new population-based and nature-inspired optimization paradigm, which has strong global search ability, but its diversified local search strategies easily make it fall into local optimum. In order to enhance its search mechanism and speed of convergence, an new improved HHO algorithm based on the inverse cumulative function operator of Cauchy distribution and tangent flight operator was proposed. The proposed two operators are used as scale factors to control the step size. The walk path of Cauchy inverse cumulative integral function shows that its trajectory step length is relative to the average, which can further enhance the search stability of the algorithm. The Tangent flight has the function of balanced exploitation and exploration, and enhances the convergence ability of the algorithm. In order to verify the performance of the proposed algorithm, the 30 benchmark functions of the 2017 Institute of Electrical and Electronic Engineers (IEEE) Conference on Evolutionary Computation (CEC2017) and two practical engineering design problems are adopted to carry out the simulation experiments. On the other hand, the covariance matrix adaptation evolutionary strategies (CMA-ES), arithmetic optimization algorithm (AOA), butterfly optimization algorithm (BOA), bat algorithm (BA), whale optimization algorithm (WOA), sine cosine algorithm (SCA), and the proposed HHO algorithms were used for comparison experiments. Simulation results show that the proposed the Cauchy-distribution and Tangent-Flight Harris Hawk Optimization (CTHHO) Algorithm has strong optimization capability.

     

引入Lévy flight和萤火虫行为的鱼群算法

针对人工鱼群算法(artificial fish-swarm algorithm,AFSA)和萤火虫算法(firefly algorithm,FA)在多维多极值函数寻优过程中易陷入局部最优和精度有待提高等问题,提出引入Lévy flight和萤火虫行为的鱼群算法(fish swarm algorithm with Lévy flight and firefly behavior).该算法将萤火虫算法中萤火虫个体的移动策略引入到鱼群的聚群,觅食两种行为模式中,进而将Lévy flight引入到鱼群的搜索策略中,使得鱼群的搜索更加高效.此外,采取一种基于动态参数的非线性变视野和变步长的策略来限定鱼群的搜索范围.仿真分析表明,新算法较其他测试算法具有更好的全局搜索能力和寻优精度.     

基于改进蜂群算法的多传感器目标分配方法

针对多传感器目标分配中参数多、计算复杂、实时性不强、全局搜索能力不高等问题,在分析其数学模型的基础上,基于改进人工蜂群算法,对目标函数进行优化并确定分配方案.改进后的算法中,跟随蜂采用双向轮盘赌的方式选择引领蜂,并采用萤火虫算法中的自适应步长策略来进行局部搜索,有助于提高算法收敛速度、增强算法的局部搜索能力.仿真结果表明,改进蜂群算法能够有效解决多传感目标分配问题,与基本蜂群算法相比,改进后的算法收敛速度加快、寻优能力得到进一步增强,具有较强的实用价值.     

一个基于硬件计数器的程序性能测试与分析工具

在Intel P6系列处理器与Microsoft Windows NT平台上开发了一个工具软件PTracker，它利用处理器中的硬件性能计数器来获取程序性能数据，并结合机器体系结构参数对数据进行分析。它无需用户编程，与应用程序所使用的编程语言无关，使用很方便。它不仅能够通过性能计数器获得精确的性能参数，而且还能通过对测试得到的性能数据的分析，揭示程序高层次的性能特征，对程序性能评价与优化具有一定的指导作用。本文介绍了PTracker的技术背景、设计与系统实现，并给出了一个应用实例。     

Recognizing Visual Focus of Attention From Head Pose in Natural Meetings

We address the problem of recognizing the visual focus of attention (VFOA) of meeting participants based on their head pose. To this end, the head pose observations are modeled using a Gaussian mixture model (GMM) or a hidden Markov model (HMM) whose hidden states correspond to the VFOA. The novelties of this paper are threefold. First, contrary to previous studies on the topic, in our setup, the potential VFOA of a person is not restricted to other participants only. It includes environmental targets as well (a table and a projection screen), which increases the complexity of the task, with more VFOA targets spread in the pan as well as tilt gaze space. Second, we propose a geometric model to set the GMM or HMM parameters by exploiting results from cognitive science on saccadic eye motion, which allows the prediction of the head pose given a gaze target. Third, an unsupervised parameter adaptation step not using any labeled data is proposed, which accounts for the specific gazing behavior of each participant. Using a publicly available corpus of eight meetings featuring four persons, we analyze the above methods by evaluating, through objective performance measures, the recognition of the VFOA from head pose information obtained either using a magnetic sensor device or a vision-based tracking system. The results clearly show that in such complex but realistic situations, the VFOA recognition performance is highly dependent on how well the visual targets are separated for a given meeting participant. In addition, the results show that the use of a geometric model with unsupervised adaptation achieves better results than the use of training data to set the HMM parameters.     

Local performance of the (1 + 1)-ES in a noisy environment

While noise is a phenomenon present in many real world optimization problems, the understanding of its potential effects on the performance of evolutionary algorithms is still incomplete. This paper investigates the effects of fitness proportionate Gaussian noise for a (1 + 1)-ES with isotropic normal mutations on the quadratic sphere in the limit of infinite search-space dimensionality. It is demonstrated experimentally that the results provide a good approximation for finite space dimensionality. It is shown that overvaluation as a result of failure to re-evaluate parental fitness leads to both reduced success probabilities and improved performance. Implications for mutation strength adaptation rules are discussed and optimal re-sampling rates are computed     

一种改进的人工鱼群算法

人工鱼群算法是一种基于动物行为的群体智能优化算法。该文提出一种改进的人工鱼群算法,在觅食行为中让人工鱼直接移动到较优位置,以加快算法的搜索速度,动态调整人工鱼的视野和步长,使其在算法运行初期保持最大值,并逐渐由大变小。该算法较好地 平衡了全局搜索能力和局部搜索能力,提高了算法运行效率和精度。仿真结果表明,改进的人工鱼群算法收敛性能比原有算法提高了1倍 以上。     

Behaviors classification based distance measuring system for pedestrians via a foot‐mounted inertial sensor

In this paper, we develop a foot‐mounted pedestrian navigation system prototype with the emphasis on distance measuring with an inertial measurement unit (IMU) which implies the characteristics of pedestrian gait cycle and thus can be used as a crucial step indicator for distance calculation. Conventional methods for step detection and step length estimation cannot adapt well to the general pedestrian applications since the parameters in these methods may vary for different persons and motions. In this paper, an adaptive time‐ and frequency‐domains joint distance measuring method is proposed by utilizing the means of behaviors classification. Two key issues are studied: step detection and step length determination. For the step detection part, first behavior classification along with state transition strategy is designed to identify typical pedestrian behaviors including standing still, walking, running and irregular swing. Then a four‐stage step detection method is proposed to adaptively determine both step frequency and threshold in a flexible window. Based on the behavior classification results, a two‐segment functional based step length model is established to adapt the walking and running behaviors. Finally, real experiments are carried out to verify our proposed step detection method and step length model. The results show that the proposed method outperforms the existing representative methods and it exhibits the merits of accuracy and adaptability for different persons in real time and significantly improves the accuracy of distance measuring.     

基于属性约简的应用服务器优化算法改进

优化参数配置是优化应用服务器性能的重要方面;基于传统参数调节的优化策略耗时耗力缺乏系统性和规律性;利用模块化思想针对目标决策函数对应用服务器参数进行分类,可构建条件属性约简模型;基于属性约简的应用服务器优化算法,可去除对于目标决策函数相对不重要的参数,并获得相对重要的参数,从而达到锁定目标重点调节,快速提高系统性能的目的;现有的约简模型优化算法多基于经典粗糙集理论,在等价关系的基础上构造分类,容易造成大量的信息破坏和流失;文章通过拓展等价关系到一般二元关系,利用广义粗糙集理论改良了基于模块化思想和属性约简模型的应用服务器优化算法,通过定义辨识函数对条件属性进行约简,再结合依赖度计算,得到最终目标参数。     

改进人工鱼群算法在SVM参数优化中的应用

支持向量机的参数优化一直是一个重要的研究方向。参数的好坏很大程度上决定了支持向量机的分类精度和泛化能力。针对人工鱼群算法优化支持向量机参数时,容易在后期徘徊于最优解附近、难以逼近的问题,提出了人工鱼群加速算法,使用速度参数代替人工鱼步长,从而求得最优目标并得到SVM的最优参数组合。仿真实验结果表明：该算法收敛速度快,求解数值精度高,对初值的依赖程度低,在SVM参数优化中具有更好的性能、更高的分类准确率,是一个极其有效的参数优化方法。     

Exponential stability of general tracking algorithms

Tracking and adaptation algorithms are, from a formal point of view, nonlinear systems which depend on stochastic variables in a fairly complicated way. The analysis of such algorithms is thus quite complicated. A first step is to establish the exponential stability of these systems. This is of interest in its own right and a prerequisite for the practical use of the algorithm. It is also a necessary starting point to analyze the performance in terms of tracking and adaptation because that is how close the estimated parameters are to the time-varying true ones. In this paper we establish some general conditions for the exponential stability of a wide and common class of tracking algorithms. This includes least mean squares, recursive least squares, and Kalman filter based adaptation algorithms. We show how stability of an averaged (linear and deterministic) equation and stability of the actual algorithm are linked to each other under weak conditions on the involved stochastic processes. We also give explicit conditions for exponential stability of the most common algorithms. The tracking performance of the algorithms is studied in a companion paper     

多策略的郊狼优化算法

郊狼优化算法（coyote optimization algorithm,COA）是最近提出的一种群智能优化算法,具有独特的搜索结构和较好的优化性能。为了进一步提高COA的优化性能,提出了一种多策略的郊狼优化算法（multi-strategy COA,MSCOA）。首先,对于组内最优郊狼,采用一种全局最优郊狼引导的成长策略提高其社会适应能力,对于组内最差郊狼,采用一种最优郊狼引导强化策略强化最差郊狼的能力;其次,对于组内其他郊狼采用一种动态调整信息交流的组内成长策略提升组内郊狼之间的信息共享程度,并将这种组内成长策略与一种改进的迁移策略融合,更进一步提升搜索能力;最后采用动态分组策略减少参数手动设置,提高算法的可操作性。以上多种策略的使用更好地平衡了探索与开采,使算法的性能最大化。大量来自CEC2014测试集的复杂函数实验结果表明,与COA相比,MSCOA具有更强搜索能力、更快的运行速度和更高的搜索效率,与其他优秀优化算法相比,具有更明显的优势。     

An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization

Differential evolution (DE) is one of the most powerful stochastic real parameter optimizers of current interest. In this paper, we propose a new mutation strategy, a fitness-induced parent selection scheme for the binomial crossover of DE, and a simple but effective scheme of adapting two of its most important control parameters with an objective of achieving improved performance. The new mutation operator, which we call DE/current-to-gr_best/1, is a variant of the classical DE/current-to-best/1 scheme. It uses the best of a group (whose size is q% of the population size) of randomly selected solutions from current generation to perturb the parent (target) vector, unlike DE/current-to-best/1 that always picks the best vector of the entire population to perturb the target vector. In our modified framework of recombination, a biased parent selection scheme has been incorporated by letting each mutant undergo the usual binomial crossover with one of the p top-ranked individuals from the current population and not with the target vector with the same index as used in all variants of DE. A DE variant obtained by integrating the proposed mutation, crossover, and parameter adaptation strategies with the classical DE framework (developed in 1995) is compared with two classical and four state-of-the-art adaptive DE variants over 25 standard numerical benchmarks taken from the IEEE Congress on Evolutionary Computation 2005 competition and special session on real parameter optimization. Our comparative study indicates that the proposed schemes improve the performance of DE by a large magnitude such that it becomes capable of enjoying statistical superiority over the state-of-the-art DE variants for a wide variety of test problems. Finally, we experimentally demonstrate that, if one or more of our proposed strategies are integrated with existing powerful DE variants such as jDE and JADE, their performances can also be enhanced.     

Dynamic Gaussian bare-bones fruit fly optimizers with abandonment mechanism: method and analysis

The Fruit Fly Optimization Algorithm (FOA) is a recent algorithm inspired by the foraging behavior of fruit fly populations. However, the original FOA easily falls into the local optimum in the process of solving practical problems, and has a high probability of escaping from the optimal solution. In order to improve the global search capability and the quality of solutions, a dynamic step length mechanism, abandonment mechanism and Gaussian bare-bones mechanism are introduced into FOA, termed as BareFOA. Firstly, the random and ambiguous behavior of fruit flies during the olfactory phase is described using the abandonment mechanism. The search range of fruit fly populations is automatically adjusted using an update strategy with dynamic step length. As a result, the convergence speed and convergence accuracy of FOA have been greatly improved. Secondly, the Gaussian bare-bones mechanism that overcomes local optimal constraints is introduced, which greatly improves the global search capability of the FOA. Finally, 30 benchmark functions for CEC2017 and seven engineering optimization problems are experimented with and compared to the best-known solutions reported in the literature. The computational results show that the BareFOA not only significantly achieved the superior results on the benchmark problems than other competitive counterparts, but also can offer better results on the engineering optimization design problems.

     

一种高跟踪质量保证的预先节点选择策略

为保证传感器网络节点高质量的目标跟踪效果,采用在目标移动的方向上提前选择一些节点进行监控的策略,保证当目标移动到新的位置时总有监测节点进行持续的监测,使这些监测节点总是离目标最近,感知质量最高;该策略依据网络能量消耗的不均衡性,在能量有剩余的区域提前唤醒更多的节点,而在能量消耗紧张的区域少唤醒节点,与其他策略相比并没有降低网络寿命。通过实验性能分析对比表明：该策略可以在不降低网络寿命的前提下大幅度提升跟踪质量。     

基于动态参数的人工搜索群算法

人工搜索群算法(Artificial search swarm algorithm,ASSA)是受人类士兵通过信息交流完成目标搜索行为及过程启发而设计的一种仿生智能算法.针对基本人工搜索群算法中固定参数可能导致算法过早陷入局部最优解的缺陷,提出一种动态参数改进人工搜索群算法(Improved artificial search swarm algorithm,IASSA).通过引入全局权重系数改善侦查行为中搜索群的历史经验位置,从而加强全局最优个体对整个种群的引导作用;利用动态协同参数提高士兵协同行为的搜索概率,以加强种群之间局部信息交流;采用动态步长策略提高算法的收敛速度和收敛精度;为了检验改进算法的优化性能,采用15个测试函数进行仿真实验.实验结果表明,所提出的改进算法可有效避免早熟现象,在收敛速度和收敛精度上较基本人工搜索群算法和若干同类优化算法有显著提高.     

领域对齐对抗的无监督跨领域文本情感分析算法

在实际应用场景中,情感分析技术为自动判别文本情感极性提供了有效的决策及解决方案,但是文本情感分析技术依赖于大量的标定样本.为了减小对人工标注的依赖,有研究者提出了基于领域自适应的跨领域情感分析技术.该技术面向跨领域文本情感分析任务,将经由标定样本训练的源领域模型,迁移至无标定的目标领域.然而目前的领域自适应技术仅从单个角度进行迁移,即减小领域专有特征差异或提取领域不变特征.因此考虑到跨领域文本数据同时包含领域专有特征和领域不变特征的特点,提出了一种领域对齐对抗的无监督跨领域文本情感分析算法.该算法通过渐进式的迁移策略,逐层减小不同语义层的领域差异,并在高层语义子空间通过协同优化的领域自适应算法,实现跨领域文本数据的领域知识迁移.在2个公开跨领域文本情感数据集上的24组跨领域文本情感分类实验结果表明,与4类领域自适应算法中代表性的和当前表现最优的方法相比,领域对齐对抗的无监督跨领域文本情感分析算法在24组实验中取得了最高的平均分类准确率,同时结合迁移性能分析结果和特征分布可视化结果,证明该算法一定程度上提升了现有无监督跨领域文本情感分析算法的分类性能和迁移性能.