Approximating the nondominated front using the Pareto Archived Evolution Strategy

We introduce a simple evolution scheme for multiobjective optimization problems, called the Pareto Archived Evolution Strategy (PAES). We argue that PAES may represent the simplest possible nontrivial algorithm capable of generating diverse solutions in the Pareto optimal set. The algorithm, in its simplest form, is a (1 + 1) evolution strategy employing local search but using a reference archive of previously found solutions in order to identify the approximate dominance ranking of the current and candidate solution vectors. (1 + 1)-PAES is intended to be a baseline approach against which more involved methods may be compared. It may also serve well in some real-world applications when local search seems superior to or competitive with population-based methods. We introduce (1 + lambda) and (mu + lambda) variants of PAES as extensions to the basic algorithm. Six variants of PAES are compared to variants of the Niched Pareto Genetic Algorithm and the Nondominated Sorting Genetic Algorithm over a diverse suite of six test functions. Results are analyzed and presented using techniques that reduce the attainment surfaces generated from several optimization runs into a set of univariate distributions. This allows standard statistical analysis to be carried out for comparative purposes. Our results provide strong evidence that PAES performs consistently well on a range of multiobjective optimization tasks.     

Global localization for mobile robots using reference scan matching

This paper presents a new approach based on scan matching for global localization with a metric-topological hybrid world model. The proposed method aims to estimate relative pose to the most likely reference site by matching an input scan with reference scans, in which topological nodes are used as reference sites for pose hypotheses. In order to perform scan matching we apply the spectral scan matching (SSM) method that utilizes pairwise geometric relationships (PGR) formed by fully interconnected scan points. The SSM method allows the robot to achieve scan matching without using an initial alignment between two scans and geometric features such as corners, curves, or lines. The localization process is composed of two stages: coarse localization and fine localization. Coarse localization with 2D geometric histogram constructed from the PGR is fast, but not precise sufficiently. On the other hand, fine localization using the SSM method is comparatively slow, but more accurate. This coarse-to-fine framework reduces the computational cost, and makes the localization process reliable. The feasibility of the proposed methods is demonstrated by results of simulations and experiments.     

Adaptive weighted-sum method for bi-objective optimization: Pareto front generation

This paper presents a new method that effectively determines a Pareto front for bi-objective optimization with potential application to multiple objectives. A traditional method for multiobjective optimization is the weighted-sum method, which seeks Pareto optimal solutions one by one by systematically changing the weights among the objective functions. Previous research has shown that this method often produces poorly distributed solutions along a Pareto front, and that it does not find Pareto optimal solutions in non-convex regions. The proposed adaptive weighted sum method focuses on unexplored regions by changing the weights adaptively rather than by using a priori weight selections and by specifying additional inequality constraints. It is demonstrated that the adaptive weighted sum method produces well-distributed solutions, finds Pareto optimal solutions in non-convex regions, and neglects non-Pareto optimal solutions. This last point can be a potential liability of Normal Boundary Intersection, an otherwise successful multiobjective method, which is mainly caused by its reliance on equality constraints. The promise of this robust algorithm is demonstrated with two numerical examples and a simple structural optimization problem.     

双参考点切换的多移动机器人队形运动控制方法

为描述机器人队列的运动过程,从相对位姿的角度定义了多移动机器人的队形模型.在传统leader-following队形控制的基础上,引入切换控制思想,每对领路机器人与跟随机器人之间设计3个控制器,对应跟随机器人中轴线上两参考点分别设计两个运动子控制器,控制领路机器人与跟随机器人之间的相对位姿;切换控制器根据系统处于平衡状态时,跟随机器人线速度的符号切换运动控制器,从而保证队列收敛到目标队形.仿真实验结果表明,机器人队列表现出良好的整体一致性,队列运动更加平稳.     

Searching for a black hole in interconnected networks using mobile agents and tokens

We study the impact of the topological structure on the complexity of the Black hole search (Bhs) problem using mobile agents that communicate via tokens. First, we show that the token model can support the same cost as in the whiteboard model, despite the fact that communication between mobile agents is considerably more restricted (and complex) in a token model than in a whiteboard one. More precisely, in this paper, we focus on three specific topologies, namely: an asynchronous (i) hypercube, (ii) torus and (iii) complete network. With knowledge of which of these topologies is being used, we present token-based solutions for Bhs   where the number of moves executed by a team of two co-located anonymous agents can be reduced to Θ(n)$\Theta \left(n\right)$. These proposed solutions do not require the availability of a map and do not assume FIFO on either nodes or links.     

Estimation of the Pareto front in stochastic simulation through stochastic Kriging

Multi-objective optimization of simulated stochastic systems aims at estimating a representative set of Pareto optimal solutions and a common approach is to rely on metamodels to alleviate computational costs of the optimization process. In this article, both the objective and constraint functions are assumed to be smooth, highly non-linear and computationally expensive and are emulated by stochastic Kriging models. Then a novel global optimization algorithm, combing the expected hypervolume improvement of approximated Pareto front and the probability of feasibility of new points, is proposed to identify the Pareto front (set) with a minimal number of expensive simulations. The algorithm is suitable for the situations of having disconnected feasible regions and of having no feasible solution in initial design. Then, we also quantify the variability of estimated Pareto front caused by the intrinsic uncertainty of stochastic simulation using nonparametric bootstrapping method to better support decision making. One test function and an (s, S) inventory system experiment illustrate the potential and efficiency of the proposed sequential optimization algorithm for constrained multi-objective optimization problems in stochastic simulation, which is especially useful in Operations Research and Management Science.     

A flexible cluster-oriented alternative clustering algorithm for choosing from the Pareto front of solutions

Supervised alternative clustering is the problem of finding a set of clusterings which are of high quality and different from a given negative clustering. The task is therefore a clear multi-objective optimization problem. Optimizing two conflicting objectives at the same time requires dealing with trade-offs. Most approaches in the literature optimize these objectives sequentially (one objective after another one) or indirectly (by some heuristic combination of the objectives). Solving a multi-objective optimization problem in these ways can result in solutions which are dominated, and not Pareto-optimal. We develop a direct algorithm, called COGNAC, which fully acknowledges the multiple objectives, optimizes them directly and simultaneously, and produces solutions approximating the Pareto front. COGNAC performs the recombination operator at the cluster level instead of at the object level, as in the traditional genetic algorithms. It can accept arbitrary clustering quality and dissimilarity objectives and provides solutions dominating those obtained by other state-of-the-art algorithms. Based on COGNAC, we propose another algorithm called SGAC for the sequential generation of alternative clusterings where each newly found alternative clustering is guaranteed to be different from all previous ones. The experimental results on widely used benchmarks demonstrate the advantages of our approach.

     

移动机器人前向单目视觉的建模研究

为了实现目标对象的快速、精确识别,对移动机器人前向单目视觉进行了建模.首先,给出了前向单目视觉成像模型,包括软、硬件设计,并结合目标特征属性得到目标中心在图像上的坐标;然后,通过视觉标定模型实现前向单目视觉的目标定位,得到目标对象在机器人体坐标系中的坐标;最后,利用参数估计的方法计算真实检测结果与理想检测结果的误差分布模型的参数,得到前向单目视觉的观测模型.实验结果表明,前向单目视觉模型建立精确,且实时性好.     

A min-max method with adaptive weightings for uniformly spaced Pareto optimum points

This work aims at obtaining uniformly spaced Pareto optimum points in the objective space when multicriteria optimization problems are solved. An original adaptive scheme is proposed to update automatically weighting coefficients involved in the min-max method. By means of a novel bilevel approach, it is shown that with the calculation of the tangent and normal directions of the Pareto curve, Pareto optimum points can be obtained sequentially with a uniformly spaced distribution. Meanwhile, the distance between two adjacent Pareto optimum points is controllable depending upon the prescribed step length along the tangent direction. To validate the method, numerical bicriteria examples are solved to show its effectiveness.     

Adaptive weighted sum method for multiobjective optimization: a new method for Pareto front generation

This paper presents an adaptive weighted sum (AWS) method for multiobjective optimization problems. The method extends the previously developed biobjective AWS method to problems with more than two objective functions. In the first phase, the usual weighted sum method is performed to approximate the Pareto surface quickly, and a mesh of Pareto front patches is identified. Each Pareto front patch is then refined by imposing additional equality constraints that connect the pseudonadir point and the expected Pareto optimal solutions on a piecewise planar hypersurface in the -dimensional objective space. It is demonstrated that the method produces a well-distributed Pareto front mesh for effective visualization, and that it finds solutions in nonconvex regions. Two numerical examples and a simple structural optimization problem are solved as case studies. Presented as paper AIAA-2004-4322 at the 10th AIAA-ISSMO Multidisciplinary Analysis and Optimization Conference, Albany, New York, August 30–September 1, 2004     

Convergence of multi-objective evolutionary algorithms to a uniformly distributed representation of the Pareto front

In evolutionary multi-objective optimization (EMO), the convergence to the Pareto set of a multi-objective optimization problem (MOP) and the diversity of the final approximation of the Pareto front are two important issues. In the existing definitions and analyses of convergence in multi-objective evolutionary algorithms (MOEAs), convergence with probability is easily obtained because diversity is not considered. However, diversity cannot be guaranteed. By combining the convergence with diversity, this paper presents a new definition for the finite representation of a Pareto set, the B-Pareto set, and a convergence metric for MOEAs. Based on a new archive-updating strategy, the convergence of one such MOEA to the B-Pareto sets of MOPs is proved. Numerical results show that the obtained B-Pareto front is uniformly distributed along the Pareto front when, according to the new definition of convergence, the algorithm is convergent.     

A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization

New challenges in engineering design lead to multiobjective (multicriteria) problems. In this context, the Pareto front supplies a set of solutions where the designer (decision-maker) has to look for the best choice according to his preferences. Visualization techniques often play a key role in helping decision-makers, but they have important restrictions for more than two-dimensional Pareto fronts. In this work, a new graphical representation, called Level Diagrams, for n-dimensional Pareto front analysis is proposed. Level Diagrams consists of representing each objective and design parameter on separate diagrams. This new technique is based on two key points: classification of Pareto front points according to their proximity to ideal points measured with a specific norm of normalized objectives (several norms can be used); and synchronization of objective and parameter diagrams. Some of the new possibilities for analyzing Pareto fronts are shown. Additionally, in order to introduce designer preferences, Level Diagrams can be coloured, so establishing a visual representation of preferences that can help the decision-maker. Finally, an example of a robust control design is presented - a benchmark proposed at the American Control Conference. This design is set as a six-dimensional multiobjective problem.     

交通事故现场摄影测量标定点识别

对交通事故现场测量的现场标定点进行自动识别是提高交通事故摄影测量速度的重要技术。在自然光照条件下,标定点区域不可避免地存在阴影等干扰,采用无限脉冲响应(IIR)递归滤波器对脊线边缘进行检测,可以有效排除阴影干扰。在进行直线提取和聚类的基础上,对标定直线进行识别和排序,并以此为基础对标定点进行识别和排序。直线分类采用动态聚类方法。为了保证聚类的鲁棒性,对传统动态聚类方法的聚类中心生成方法进行了改进,用中位数代替传统方法中的均值,并采用最大、最小距离算法确定初始聚类中心。     

Nonlinear model-based track guidance of user-defined points at the vehicle front

Preventive pedestrian protection systems are validated by means of fully automated driving tests reproducing safety-critical traffic situations on a proving ground. In order to assess these preventive safety systems, a precise and reproducible collision of a pedestrian dummy with a specific point at the vehicle front, e.g., the left corner of the vehicle, must be ensured. Hence, a track guidance of this specific point is required. Beyond the state of the art a new nonlinear model describing the lateral deviation of any point at the vehicle front to a predefined path is proposed in this paper. Based on this model the method of input–output linearization is used to design a flexible lateral guidance system for an easy application in different vehicles. Furthermore, the closed-loop stability is proven and experimental results are presented.     

Automated enforcement for relaxed information release with reference points

Language-based information ftow security is a promising approach for enforcement of strong security and protection of the data confidentiality for the end-to-end communications. Here, noninterference is the standard and most restricted security property that completely forbids confidential data from being released to public context. Although this baseline property has been extensively enforced in various cases, there are still many programs, which are considered secure enough, violating this property in some way. In order to control the information release in these programs, the predetermined ways should be specified by means of which confidential data can be released. These intentional releases, also called declassifications, are regulated by several more relaxed security properties than noninterference. The security properties for controlled declassification have been developed on different dimensions with declassification goals. However, the mechanisms used to enforce these properties are still unaccommodating, unspecific, and insufficiently studied. In this work, a new security property, the Relaxed Release with Reference Points （R3P）, is presented to limit the information that can be declassified in a program. Moreover, a new mechanism using reachability analysis has been proposed for the pushdown system to enforce R3P on programs. In order to show R3P is competent for use, it has been proved that it complies with the well-known prudent principles of declassification, and in addition finds some restrictions on our security policy. The widespread usage, precision, efficiency, and the influencing factors of our enforcement have been evaluated.     

Comparative study of algorithms for matching sets of reference points

Experimental investigation of the algorithms for matching the sets of reference points in the problem on registration of images of fine art paintings is presented in this paper. The experiments are carried out using synthetic and real data sets. The decision about the possibility of applying the examined algorithms to image registration is made. The most suitable algorithm is chosen, and the development of a procedure for eliminating false correspondences is stated to be vital.     

Diversity of Pareto front: A multiobjective genetic algorithm based on dominating information

In this paper, the diversity information included by dominating number is analyzed, and the probabilistic relationship between dominating number and diversity in the space of objective function is proved. A ranking method based on dominating number is proposed to build the Pareto front. Without increasing basic Pareto method’s computation complexity and introducing new parameters, a new multiobjective genetic algorithm based on proposed ranking method (MOGA-DN) is presented. Simulation results on function optimization and parameters optimization of control system verify the efficiency of MOGA-DN.     

A prediction strategy based on center points and knee points for evolutionary dynamic multi-objective optimization

In real life, there are many dynamic multi-objective optimization problems which vary over time, requiring an optimization algorithm to track the movement of the Pareto front (Pareto set) with time. In this paper, we propose a novel prediction strategy based on center points and knee points (CKPS) consisting of three mechanisms. First, a method of predicting the non-dominated set based on the forward-looking center points is proposed. Second, the knee point set is introduced to the predicted population to predict accurately the location and distribution of the Pareto front after an environmental change. Finally, an adaptive diversity maintenance strategy is proposed, which can generate some random individuals of the corresponding number according to the degree of difficulty of the problem to maintain the diversity of the population. The proposed strategy is compared with four other state-of-the-art strategies. The experimental results show that CKPS is effective for evolutionary dynamic multi-objective optimization.     

Searching regions of interest on vector local homogeneous scenes

A generalized solution of the problem of searching a scene’s fragments, called the regions of interest, for the case when a set of spatial superposed images serves as the initial information about the scene, is proposed. A vector homogeneous random field is taken to be the scene model.