基于异构多种群策略的动态概率粒子群优化算法

结合动态概率粒子群优化算法(DPPSO)特点,针对传统的单种群粒子群优化算法易陷入局部最优、收敛速度较慢的缺点,文中提出一种基于异构多种群策略的DPPSO.该算法在进化过程中保持多个子种群,每个子种群以不同的DPPSO变体进行进化,子种群之间根据一定规律进行通信,从而保持整个种群内部的信息交流,进而协调DPPSO的勘探和开采能力.通过典型的Benchmark函数优化问题测试并分析基于异构多种群策略的DPPSO性能,结果显示,使用该策略的算法收敛速度较快,稳定性有较显著提高,具有较强的全局搜索能力.     

基于自适应正交遗传算法的多故障诊断研究

提出了能根据当前进化的种群状态自适应调整局部搜索空间大小的正交局部搜索算子,并与遗传算法相结合形成了自适应正交遗传算法.在概率因果模型的基础上,将自适应正交遗传算法成功用于电路的多故障诊断.实验进一步证明了算法的有效性.     

自适应动态重组多目标粒子群优化算法

提出一种自适应动态重组粒子群优化算法. 该算法采用凝聚的层次聚类算法, 将种群分成若干个子群体, 用一个精英集对非支配解进行存储; 根据贡献度和多样性, 对各子群体的粒子和整个种群进行自适应动态重组; 同时引入扰动算子对精英集存储的非支配解进行扰动, 实现对精英集进行动态调整. 利用具有不同特点的测试函数进行验证并与同类算法相比较, 结果表明, 所提出的算法可加快收敛速度, 提高种群的可进化能力.

     

一种动态分组的粒子群优化算法

针对粒子群优化算法易陷入局部最优的问题,提出一种动态分组的粒子群优化算法.通过对鸟群习性的研究,给出交互粒子的概念,并在粒子群优化过程中引入动态分组机制,将种群动态划分成多个子种群,且每次划分的子种群数目是从特定集合中随机选取,从而增加交互粒子划分到同一子种群的概率.每个子种群在收敛进化的同时,利用环拓扑结构提高种群多样性及算法搜索全局最优解的能力.实验结果表明,与其他粒子群优化算法相比,该算法具有更好的稳定性、寻优性能以及更高的收敛精度.     

包含交叉和变异操作的交互式遗传算法

传统交互式遗传算法在优化隐式性能指标时会使用户产生疲劳,影响优化质量与优化效率。为此,提出一种改进的交互式遗传算法。采用二元排序确定适应值评价的不确定度,根据评价序列的最大信息差异计算种群的收敛率,通过收敛率衡量种群进化状态,基于适应值不确定度和种群收敛率设计自适应交叉算子和变异算子,给出交叉概率和变异概率的计算公式,利用包含用户偏好信息的遗传策略引导进化,从而使进化结果更加客观。将该算法应用于服装进化设计系统,结果表明,与传统交互式遗传算法( T-IGA)相比,该算法可获取更多的满意解,提高了优化效率。     

基于递减概率初始点选择K中心点进化算法

粒子群优化(PSO)的K-Medoids进化聚类算法中初始种群是随机产生的,导致选择的初始中心点有可能位于同一类簇中.为提高聚类准确性,提出一种采用递减概率化初始点选择的PSO与K-Medoids结合新算法.根据样本的分布密度设置对应的选择概率,并由轮盘赌策略依次选择中心点,使获得的中心点位于密度较高区域且在不同的簇中,同时又实现了初始种群的多样性.在人工和UCI真实数据集上的实验结果表明,改进后的算法有更快的收敛速度,提高了聚类准确率和稳定性.     

自适应学习集成优化算法及矩阵特征值求解

为了增强数值优化算法的高效性和鲁棒性,提出一种基于自适应学习的集成算法(self-adaptivelearning-based ensemble algorithm,SALBEA).在SALBEA中,采用贪婪繁殖算子、进化搜索策略学习算子、X进化算子、种群多样性维持算子改进算法进化结构.此外,SALBEA通过引入概率选择模型和自适应学习机制集成了4种有效的进化搜索策略.首先,为了评估所提算法的性能,采用26个测试函数进行算法对比测试,实验结果表明SALBEA比同类算法具有更好的高效性和鲁棒性.最终,将SALBEA用于求解矩阵特征值这一数值计算问题,结果表明该算法求解精度较高,具有较好的应用前景.     

基于$pm3sigma$正态概率区间分族遗传蚁群算法的移动机器人路径规划

针对移动机器人路径规划问题,提出一种基于正态概率区间分族的家族遗传蚁群融合算法.首先提出初始种群优化及删除算子解决传统遗传蚁群融合算法中遗传阶段随机生成的初始种群质量低的问题;然后引入适应度值正态概率区间种群分族机制及家族混合交叉算子,解决传统遗传蚁群融合算法中易出现未成熟收敛的问题;最后引入混合变异策略以提高随机变异后生成的路径质量.将全局路径规划算法与局部路径规划算法-动态窗口算法相结合形成完整移动机器人运动规划.基于Matlab仿真平台与机器人操作系统平台进行实验分析,结果验证了所提出正态化概率分族遗传蚁群融合算法求解移动机器人路径规划问题的有效性.     

具有重组学习和混合变异的动态多种群粒子群优化算法

为解决粒子群优化算法中种群多样性与收敛性间的矛盾,提出一种具有重组学习和混合变异的动态多种群粒子群优化算法.该算法动态划分多种群并融入重构粒子作为引导因子,在增加种群多样性的同时保留优秀粒子的空间信息;在算法执行阶段对最优个体施加混合变异,基于时变概率实施反向学习策略或者邻域扰动操作,帮助粒子快速跳出局部困境,加强对附近区域内的精细搜索.基于14个多类型标准测试函数,并与其他的改进粒子群算法进行对比,验证了几种改进措施的有效性和叠加影响.为进一步探究概率性混合变异策略的敏感性,对变异方式及参数设置进行仿真实验,结果表明,所采用的极值扰动策略具有显著的优势,合理地控制学习强度可以充分发挥反向学习的作用,并给出影响参数的建议取值范围.实验结果还表明,所提出的算法能够更好地平衡种群的开发与勘探能力,提高求解精度和收敛性能.     

动态搜索和协同进化的鲸鱼优化算法

针对基本鲸鱼优化算法寻优精度低、收敛速度慢及容易陷入局部最优等缺陷,提出了一种动态搜索和协同进化的鲸鱼优化算法。首先,通过等价替换和Faure序列提高初始解的质量;其次,通过对种群进行分工,提高种群多样性并增强算法跳出局部最优解的能力;最后,根据种群进化信息动态调整搜索策略,从而提高算法的收敛速度和寻优精度。仿真实验结果表明,提出的改进算法相比基本鲸鱼优化算法和部分改进算法具有较好的寻优性能。     

Method for Restoring Consistency in Probabilistic Knowledge Bases

ABSTRACT

Ensuring consistency of knowledge systems is always one of the essential requirements because, without it, most of these systems become useless. Because of the importance, many studies have involved the restoration of consistency in knowledge systems. However, these approaches are only implemented on knowledge systems that are represented by logic or probabilistic logic, thus when we apply them to probabilistic knowledge systems, there are many inadequacies. To overcome these drawbacks, in this paper, we put forward a new model for restoring the consistency of a probabilistic knowledge base by focusing on changing the probabilities in this knowledge base via several inconsistency measures. To this end, a set of inconsistency measures is presented and a family of consistency restoring operators for probabilistic knowledge bases is introduced. Next, an axiomatic model consists of a set of axioms is built to characterize the desirable properties of the consistency restoring operators. Finally, the properties of each consistency restoring operator in the introduced family are investigated and discussed.     

多重概率粗糙集模型

基于多重集合,对Z.Pawlak粗集意义下的概率粗糙集模型的论域进行了扩展,提出了基于多重集的概率粗糙集模型,即多重概率粗糙集模型,给出了该模型的完整定义、相关定理和重要性质,其中包括多重论域定义、多重概率粗糙近似集的定义及其各种性质的证明、多重概率粗糙集的近似精度定义、可定义集与属性约简的定义、多重集意义下的粗糙近似算子之间的关系及其与Z.Pawlak意义下的粗糙近似算子之间的关系等。多重概率粗糙集可充分反映知识颗粒间的重叠性,对象的重要度差别及其多态性,这样有利于用粗糙集理论从保存在关系数据库中的具有一对多、多对多依赖性的且具有不完全性或存在统计性的数据中挖掘知识。     

Some methods for strategic decision‐making problems with immediate probabilities in Pythagorean fuzzy environment

In this article, a new decision‐making model with probabilistic information and using the concept of immediate probabilities has been developed to aggregate the information under the Pythagorean fuzzy set environment. In it, the existing probabilities have been modified by introducing the attitudinal character of the decision maker by using an ordered weighted average operator. Based on it, we have developed some new probabilistic aggregation operator with Pythagorean fuzzy information, namely probabilistic Pythagorean fuzzy weighted average operator, immediate probability Pythagorean fuzzy ordered weighted average operator, probabilistic Pythagorean fuzzy ordered weighted average, probabilistic Pythagorean fuzzy weighted geometric operator, immediate probability Pythagorean fuzzy ordered weighted geometric operator, probabilistic Pythagorean fuzzy ordered weighted geometric, etc. Furthermore, we extended these operators by taking interval‐valued Pythagorean fuzzy information and developed their corresponding aggregation operators. Few properties of these operators have also been investigated. Finally, an illustrative example about the selection of the optimal production strategy has been given to show the utility of the developed method.     

Probabilistic linguistic information fusion: A survey on aggregation operators in terms of principles,definitions, classifications,applications, and challenges

The probabilistic linguistic term set is a flexible and efficient tool to represent the cognitive complex information of experts. It has attracted many scholars’ attention since it was proposed. Information fusion over the cognitive complex information is a significant issue for decision-making problems. Over the past years, more than 40 aggregation operators have been proposed to fuse the probabilistic linguistic term sets. The aim of this paper is to survey the existing probabilistic linguistic aggregation operators from the perspectives of principles, definitions, classifications, and applications. To do so, first, we summarize the present normalization techniques and operations of probabilistic linguistic term sets. Afterward, this study classifies the existing probabilistic linguistic aggregation operators into 12 kinds. Then, the application areas of these probabilistic linguistic aggregation operators are outlined. Future research directions with interests are proposed to tackle present challenges.     

多Agent信念逻辑及其在概率意义下的推广

首先建立了一种多Agent信念逻辑MBL(multi-agentbelieflogic),在经典信念逻辑基础上增加了普遍信念算子和公共信念算子,给出MBL的Kripke语义与广义Aumann语义,讨论了两者的等价性,证明了MBL对于上述两种语义的可靠性和完备性.其次,建立了一种多Agent概率信念逻辑MPBL(multi-agentprobabilisticbelieflogic),通过在广义Aumann语义基础上引入概率空间,给出了MPBL的概率Aumann语义,证明了它的可靠性,并给出MPBL的一些推论.     

Latent Variable Model for Estimation of Distribution Algorithm Based on a Probabilistic Context-Free Grammar

Estimation of distribution algorithms are evolutionary algorithms using probabilistic techniques instead of traditional genetic operators. Recently, the application of probabilistic techniques to program and function evolution has received increasing attention, and this approach promises to provide a strong alternative to the traditional genetic programming techniques. Although a probabilistic context-free grammar (PCFG) is a widely used model for probabilistic program evolution, a conventional PCFG is not suitable for estimating interactions among nodes because of the context freedom assumption. In this paper, we have proposed a new evolutionary algorithm named programming with annotated grammar estimation based on a PCFG with latent annotations, which allows this context freedom assumption to be weakened. By applying the proposed algorithm to several computational problems, it is demonstrated that our approach is markedly more effective at estimating building blocks than prior approaches.      

Wavelet modeling of contour deformations in Sobolev spaces for fitting and tracking applications

This paper proposes a new model for contour deformations using wavelets. This model uses Sobolev spaces to control the smoothness of the contour deformation. This formulation defines a probabilistic model that induces a prior distribution for contour deformation. Based on this distribution, the fitting problem is solved in Bayesian terms. The deformation model is also used to generate a prior dynamic model for contour evolution in time. This probabilistic model is then applied to solve the tracking problem. Computational results for several real-image problems are given for both the Kalman and condensation filters.     

Behavioural equivalences of a probabilistic pi-calculus

Although different kinds of probabilistic π-calculus have been introduced and found their place in quantitative verification and evaluation,their behavioural equivalences still lack a deep investigation.We propose a simple probabilistic extension of the π-calculus,π p,which is inspired by Herescu and Palamidessi’s probabilistic asynchronous π-calculus.An early semantics of our π p is presented.We generalise several classic behavioural equivalences to probabilistic versions,obtaining the probabilistic(strong) barbed equivalence and probabilistic bisimulation for π p.Then we prove that the coincidence between the barbed equivalence and bisimilarity in the π-calculus is preserved in the probabilistic setting.     

串行概率粗糙集近似

经典的概率粗糙集模型是基于等价关系和条件概率提出的。但在实际应用中,知识库存在多种不确定性因素,使得对象间的关系未必满足等价关系。因此在保证条件概率有意义的情况下,将等价关系推广到串行二元关系,讨论了串行关系下的概率粗糙集近似;研究了当目标概念发生变化时,串行概率粗糙下、上近似的性质;进一步,通过调整两个阈值,给出了对应的串行概率粗糙下、上近似的变化趋势。     

战场环境中多无人机动态任务调度

采用了基于合同网的分布式规划方法，研究了战场环境中多无人机动态任务调度问题，并建立了数学模型，提出了分布式的任务调度体系结构，设计了一种基于代价变换的概率路标图路径规划算法，该算法能够在任务调度阶段，快速预估无人机执行不同任务的飞行航路，扩展了合同网协议，可在一次拍卖中“并发”进行多次交易，提高了任务调度的效率。通过多种合同类型的综合，解决了复杂战场态势下的任务调度问题。