自适应并行进化策略在配电网络重构中的应用

针对目前利用计算机技术进行中压电网运行方案优化问题,构造了网络运行方案优化的数学模型,提出了自适应多种群并行进化策略模型,采用多种群连接节点逐步扩展法求解问题线路对应的串位进化变异,并对算法进行了实现。算例分析证明,这种策略能够极大地降低网损,提高经济效益。     

Analysis of convergence of an evolutionary algorithm with self-adaptation using a stochastic Lyapunov function

This paper analyses the convergence of evolutionary algorithms using a technique which is based on a stochastic Lyapunov function and developed within the martingale theory. This technique is used to investigate the convergence of a simple evolutionary algorithm with self-adaptation, which contains two types of parameters: fitness parameters, belonging to the domain of the objective function; and control parameters, responsible for the variation of fitness parameters. Although both parameters mutate randomly and independently, they converge to the "optimum" due to the direct (for fitness parameters) and indirect (for control parameters) selection. We show that the convergence velocity of the evolutionary algorithm with self-adaptation is asymptotically exponential, similar to the velocity of the optimal deterministic algorithm on the class of unimodal functions. Although some martingale inequalities have not be proved analytically, they have been numerically validated with 0.999 confidence using Monte-Carlo simulations.     

通用进化算法的收敛性分析

传统进化算法的收敛性专注于具体算法,对应的研究成果也仅仅适用于具体算法。为了研究所有进化算法的收敛性问题,提出了一种包含所有操作类型算子的通用进化算法,建立了一套概率空间用于研究算法的收敛性,所有有关算法的术语都用严格的数学语言加以定义。在概率空间中,有七个算法收敛性定理被完整地证明,其中之一找到了算法依概率收敛的充分必要条件。更为重要的是,这些定理适用所有进化算法。它建立了一个体系,用来指导进化算法的设计,从理论上判断进化算法的收敛性。     

Neutrality and self-adaptation

Neutral genotype-phenotype mappings can be observed in natural evolution and are often used in evolutionary computation. In this article, important aspects of such encodings are analyzed.First, it is shown that in the absence of external control neutrality allows a variation of the search distribution independent of phenotypic changes. In particular, neutrality is necessary for self-adaptation, which is used in a variety of algorithms from all main paradigms of evolutionary computation to increase efficiency.Second, the average number of fitness evaluations needed to find a desirable (e.g., optimally adapted) genotype depending on the number of desirable genotypes and the cardinality of the genotype space is derived. It turns out that this number increases only marginally when neutrality is added to an encoding presuming that the fraction of desirable genotypes stays constant and that the number of these genotypes is not too small.     

Convergence performance comparison of quantum-inspired multi-objective evolutionary algorithms

In recent research, we proposed a general framework of quantum-inspired multi-objective evolutionary algorithms (QMOEA) and gave one of its sufficient convergence conditions to the Pareto optimal set. In this paper, two Q-gate operators, H gate and R&N gate, are experimentally validated as two Q-gate paradigms meeting the convergence condition. The former is a modified rotation gate, and the latter is a combination of rotation gate and NOT gate with the specified probability. To investigate their effectiveness and applicability, several experiments on the multi-objective 0/1 knapsack problems are carried out. Compared to two typical evolutionary algorithms and the QMOEA only with rotation gate, the QMOEA with H gate and R&N gate have more powerful convergence ability in high complex instances. Moreover, the QMOEA with R&N gate has the best convergence in almost all of the experimental problems. Furthermore, the appropriate ε value regions for two Q-gates are verified.     

Rainbow: architecture-based self-adaptation with reusable infrastructure

While attractive in principle, architecture-based self-adaptation raises a number of research and engineering challenges. First, the ability to handle a wide variety of systems must be addressed. Second, the need to reduce costs in adding external control to a system must be addressed. Our rainbow framework attempts to address both problems. By adopting an architecture-based approach, it provides reusable infrastructure together with mechanisms for specializing that infrastructure to the needs of specific systems. The specialization mechanisms let the developer of self-adaptation capabilities choose what aspects of the system to model and monitor, what conditions should trigger adaptation, and how to adapt the system.     

Completely derandomized self-adaptation in evolution strategies

This paper puts forward two useful methods for self-adaptation of the mutation distribution - the concepts of derandomization and cumulation. Principle shortcomings of the concept of mutative strategy parameter control and two levels of derandomization are reviewed. Basic demands on the self-adaptation of arbitrary (normal) mutation distributions are developed. Applying arbitrary, normal mutation distributions is equivalent to applying a general, linear problem encoding. The underlying objective of mutative strategy parameter control is roughly to favor previously selected mutation steps in the future. If this objective is pursued rigorously, a completely derandomized self-adaptation scheme results, which adapts arbitrary normal mutation distributions. This scheme, called covariance matrix adaptation (CMA), meets the previously stated demands. It can still be considerably improved by cumulation - utilizing an evolution path rather than single search steps. Simulations on various test functions reveal local and global search properties of the evolution strategy with and without covariance matrix adaptation. Their performances are comparable only on perfectly scaled functions. On badly scaled, non-separable functions usually a speed up factor of several orders of magnitude is observed. On moderately mis-scaled functions a speed up factor of three to ten can be expected.     

Convergence analysis of some multiobjective evolutionary algorithms when discovering motifs

An important issue in multiobjective optimization is the study of the convergence speed of algorithms. An optimization problem must be defined as simple as possible to minimize the computational cost required to solve it. In this work, we study the convergence speed of seven multiobjective evolutionary algorithms: DEPT, MO-VNS, MOABC, MO-GSA, MO-FA, NSGA-II, and SPEA2; when solving an important biological problem: the motif discovery problem. We have used twelve instances of four different organisms as benchmark, analyzing the number of fitness function evaluations required by each algorithm to achieve reasonable quality solutions. We have used the hypervolume indicator to evaluate the solutions discovered by each algorithm, measuring its quality every 100 evaluations. This methodology also allows us to study the hit rates of the algorithms over 30 independent runs. Moreover, we have made a deeper study in the more complex instance of each organism. In this study, we observe the increase of the archive (number of non-dominated solutions) and the spread of the Pareto fronts obtained by the algorithm in the median execution. As we will see, our study reveals that DEPT, MOABC, and MO-FA provide the best convergence speeds and the highest hit rates.     

An empirical time analysis of evolutionary algorithms as C programs

This article presents an empirical study devoted to characterize the computational efficiency behavior of an evolutionary algorithm (usually called canonical) as a C program. The study analyzes the effects of several implementation decisions on the execution time of the resulting evolutionary algorithm. The implementation decisions studied include: memory utilization (using dynamic vs. static variables and local vs. global variables), methods for ordering the population, code substitution mechanisms, and the routines for generating pseudorandom numbers within the evolutionary algorithm. The results obtained in the experimental analysis allow us to conclude that significant improvements in efficiency can be gained by applying simple guidelines to best program an evolutionary algorithm in C. Copyright © 2013 John Wiley & Sons, Ltd.     

Convergence analysis of a self-adaptive multi-objective evolutionary algorithm based on grids

Evolutionary algorithms have been successfully applied to various multi-objective optimization problems. However, theoretical studies on multi-objective evolutionary algorithms, especially with self-adaption, are relatively scarce. This paper analyzes the convergence properties of a self-adaptive (μ+1)-algorithm. The convergence of the algorithm is defined, and general convergence conditions are studied. Under these conditions, it is proven that the proposed self-adaptive (μ+1)-algorithm converges in probability or almost surely to the Pareto-optimal front.     

A self-adaptation scheme for workflow management in multi-agent systems

Business processes, operational environment, variability of resources and user needs may change from time to time. An effective workflow management software system must be able to accommodate these changes. The ability to dynamically adapt to changes is a key success factor for workflow management systems. Holonic multi-agent systems (HMS) provide a flexible and reconfigurable architecture to accommodate changes based on dynamic organization and collaboration of autonomous agents. Although HMS provides a potential architecture to accommodate changes, the dynamic organization formed in HMS poses a challenge in the development of a new software development methodology to dynamically compose the services and adapt to changes as needed. This motivates us to study and propose a methodology to design self-adaptive software systems based on the HMS architecture. In this paper, we formulate a workflow adaptation problem (WAP) and propose an interaction mechanism based on contract net protocol (CNP) to find a solution to WAP to compose the services based on HMS. The interaction mechanism relies on a service publication and discovery scheme to find a set of task agents and a set of actor agents to compose the required services in HMS. We propose a viable self-adaptation scheme to reconfigure the agents and the composed services based on cooperation of agents in HMS to accommodate the changes in workflow and capabilities of actors. We propose architecture for our design methodology and present an application scenario to illustrate our idea.     

A new hybrid differential evolution algorithm with self-adaptation for function optimization

In this study, a new hybrid algorithm, hDEBSA, is proposed with the aid of two evolutionary algorithms, Differential Evolution (DE) and Backtracking Search Optimization Algorithm (BSA). The control parameters of both algorithms are simultaneously considered as a self-adaptation basis such that the values of the parameters update automatically during the optimization process to improve performance and convergence speed. To validate the proposed algorithm, twenty-eight CEC2013 test functions are considered. The performance results of hDEBSA are validated by comparing them with several state-of-the-art algorithms that are available in literature. Finally, hDEBSA is applied to solve four real-world optimization problems, and the results are compared with the other algorithms, where it was found that the hDEBSA performance is better than that of the other algorithms.     

Multi-constraints self-adaptation for reconfigurable multimedia embedded systems

The application of multimedia in embedded systems (ES), such as Virtual reality and 3-D imaging, represents the current trend in ES development. Coupling multimedia with ES has raised new multimedia-related challenges that have been added to the common ES constraints. These challenges deal with the real-time, quality, performance and efficient processing requirements of multimedia applications. The integration of self-adaptation in ES development has been, for many years, a paramount solution to cope with these issues. Although there has been extensive research on the topic of ES self-adaptation, the related works still lack global approaches that better deal with multimedia-related constraints. Coordinating different adaptation mechanisms, monitoring multiple system constraints and supporting multi-application contexts are still underexplored. The aim of the present work is to fill in these gaps by providing a global adaptation approach that offers better adaptation decisions with fair resource sharing among competing multimedia applications. With the above challenges in mind, we propose a multi-constraints combined adaptation approach that targets multimedia ES. It addresses four critical system constraints: maximizing the overall system‘s Quality of Application (QoA) under the real-time constraint, the remaining system energy and the available network bandwidth. It coordinates the adaptation at both application and architecture levels. To test and validate the proposed technique, a videophone system is designed on a Xilinx FPGA development board. It executes two complex multimedia applications. The validation results show the aptitude of the proposed system to successfully reconfigure itself at run-time in response to its constraints.

     

基于SARSA在线规划的软件体系结构自适应

提出基于SARSA算法的在线规划软件体系结构自适应方法,用来解决由于环境内在固有的不确定性、复杂性和不可预见性而产生的离线规划的局限性。在线规划方法指可以根据当前的环境状况自动选择行动的规划方法。结合Robocode的实例详细阐述了实现基于SARSA算法的在线规划方法的三个关键问题和过程策略;为解决自适应的状态和行动表述、适应度和可受理集合关键问题,提出了自适应在线规划的策略。最后用Robocode的坦克战斗实例,证明了基于SARSA在线规划软件体系结构自适应方法的可行性和有效性。     

A software architecture centric self-adaptation approach for Internetware

Being one of the basic features of Internetware, self-adaptation means that the software system can monitor its runtime state and behavior and adjust them when necessary according to pre-defined policies. Focusing on the three fundamental issues of self-adaptation, including the scope, operability and trustworthiness, a software architecture （SA） centric approach for Internetware＇s self-adaptation is presented in this paper. All of the self-adaptive actions, i.e. monitoring, analyzing, planning and executing, are performed based on SA. In detail, runtime state and behavior of Internetware are represented and changed in the form of runtime soft- ware architecture. The knowledge for self-adaptation is captured, organized and reasoned in the form of SA so that automatic analysis and decision-making are achieved.     

面向软件模糊自适应的UML用例扩展

现有统一建模语言(UML)设施及一般软件自适应工具难以直接支持软件模糊自适应(SFSA)需求分析与设计阶段的建模,为此,提出一种基于UML用例扩展的SFSA需求分析与设计方法--Fuzzy Case。该方法结合SFSA的概念模型,应用UML扩展机制引入新的构造型和标记值,建立了Fuzzy Case的一般模型;同时定义了Fuzzy Case的语法结构,并用对象约束语言(OCL)定义了其语义描述,形成了完整的SFSA建模设施。实例验证表明,与传统方法相比,Fuzzy Case能更清晰地表达SFSA的结构,准确定义软件的内部语义,建模过程更加简单方便,能有效提高SFSA的开发效率。     

自适应不良网页过滤模式的研究与实践

基于语义的文本过滤方法往往具有较大的时空开销,为了设计实用快捷而有效的过滤系统,创造性地提出了一个基于字频统计和数据挖掘技术的自适应不良网页过滤模式.以校园网作为研究环境,利用自适应语料库构建智能化的分类模式,通过模式识别过滤校园网中的不良网页.实验结果表明,该模式能以较快的速度和较为满意的准确率过滤测试文档集中的不良网页,具有较强的实用性.     

基于无线网络GIS自适应传输模型研究

GIS空间数据传输与存储技术正在经历从有线到无线的发展过程。针对无线网络环境下存在的网络带宽不高、频繁断接、资源有限等不足带来的GIS空间数据传输效率降低问题,论述了无线网络环境下空间信息数据传输的具体方法,根据无线GIS技术特点,通过分析自适应理论和无线传输特征模型,设计了基于无线网络的自适应空间数据传输模型,探讨了自适应传输的最优化原则,并通过具体试验验证了该方法的可行性和有效性。     

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.     

基于贝叶斯网络的自适应系统建构

以认知学习理论为依据,运用贝叶斯网络建立学习者模型,提出了一种学习内容自适应呈现算法。学习内容的呈现适合学习者认知发展水平及个性特征,实现了一种智能化、个性化网络学习的自适应系统,为学习者提供一种更优化的学习途径。