带变异算子的非线性惯性权重 PSO 算法

为了克服粒子群优化算法容易陷入局部最优、早熟收敛的缺点,提出了一种带有变异算子的非线性惯性权重粒子群优化算法.该算法以粒子群算法为基础,首先采用非线性递减策略对惯性权重进行调整,平衡粒子群优化算法的全局和局部搜索能力.当出现早熟收敛时,再引入变异算子,对群体粒子的最优解做随机扰动提高算法跳出局部极值的能力.用三种经典测试函数进行测试,试验结果表明,改进算法与粒子群算法相比,能够摆脱局部最优,得到全局最优解,同时具有较高的收敛精度和较快的收敛速度     

混合量子差分进化算法及应用

量子进化算法基于量子旋转门更新量子比特状态影响了算法搜索性能．提出一种差分进化（DE）与和声搜索（Hs）相结合更新量子比特状态的混合量子差分进化算法（HQDE）．该方法采用实数量子角形式编码染色体,设计一种由差分进化计算更新量子位状态的量子差分进化算法（QDE）和一种由和声搜索更新量子位状态的量子和声搜索（QHS）,并相互机制融合,采用两种不同进化策略共同作用产生种群新量子个体以克服常规算法中早熟及收敛速度慢等缺陷;在此基础上,算法还引入量子非门算子对当前最劣个体以一定概率选中的量子比特位进行变异操作增强算法跳出局部最优解能力．理论分析证明该算法收敛于全局最优解．0／1背包问题及旅行商问题实例测试结果验证了该方法有效性．     

基于自适应免疫进化算法的逻辑电路设计*

针对现有进化算法在进行逻辑电路设计时存在的进化缓慢和容易陷入局部解等问题,提出一种自适应免疫进化算法（adaptive immune evolutionary algorithm,AIEA）。该算法引入了免疫记忆机制和抗体差异调节算子,能够很好地保证个体的多样性,有利于跳出局部最优解;通过采用自适应交叉率和变异率,提高了算法的搜索能力和收敛速度。通过与多目标进化算法（MOEA）、简单免疫算法（SIA）的实验比较,证明了该自适应免疫进化算法的有效性。     

带变异算子的非线性惯性权重PSO算法

为了克服粒子群优化算法容易陷入局部最优、早熟收敛的缺点,提出了一种带有变异算子的非线性惯性权重粒子群优化算法。该算法以粒子群算法为基础,首先采用非线性递减策略对惯性权重进行调整,平衡粒子群优化算法的全局和局部搜索能力。当出现早熟收敛时,再引入变异算子,对群体粒子的最优解做随机扰动提高算法跳出局部极值的能力。用三种经典测试函数进行测试,试验结果表明,改进算法与粒子群算法相比,能够摆脱局部最优,得到全局最优解,同时具有较高的收敛精度和较快的收敛速度。     

自适应二次变异差分进化算法

提出一种基于群体适应度方差自适应二次变异的差分进化算法．该算法在运行过程中根据群体适应度方差的大小,增加一种新的变异算子对最优个体和部分其他个体同时进行变异操作,以提高种群多样性,增强差分进化算法跳出局部最优解的能力．对几种典型Benchmarks函数进行了测试,实验结果表明,该方法能有效避免早熟收敛,显著提高算法的全局搜索能力。     

多策略自适应变异的差分进化算法及其应用

针对传统DE算法在求解复杂函数时会出现早熟收敛、收敛精度低、收敛速度慢等缺陷，提出了一种多策略自适应变异的差分进化算法MsA-DE。将3种变异策略两两结合，随机分配所占比重，以增加种群的多样性；通过引入进化程度阈值，自适应地选择最合适的变异策略，平衡算法的全局搜索和局部搜索能力；对越界的变异个体进行处理，保证种群的多样性和有效性。加入扰动机制提高算法跳出局部最优的能力，同时提高最优解的精度。将该算法用于14个测试函数的优化中，结果表明，MsA-DE算法与其它4种算法相比具有更高的收敛精度和跳出局部最优的能力。将该算法应用于铁路功率调节器RPC的容量优化问题中，结果表明，该算法能够减小RPC补偿装置的容量，提高装置的经济性。     

一种新颖的函数优化进化算法

进化算法在求解全局优化问题时易陷入局部最优且收敛速度慢. 为了解决这一问题, 设计了一个基于下降尺度函数的杂交算子, 利用下降尺度函数与种群的关系来寻找实值函数的下降方向. 为了提高非均匀变异算子在进化后期的搜索能力, 通过均衡算子的局部搜索和全局搜索能力使其在算法后期仍能跳出局部最优. 在此基础上给出了一种新的进化算法. 最后将其与9个现有的算法进行了比较, 数值实验表明新算法快速有效.     

加权变异策略动态差分进化算法

针对差分进化算法在解决高维优化问题时易早熟收敛、求解精度低和参数设置麻烦等问题,提出一种加权变异策略动态差分进化算法（WMDDE）。为了动态平衡全局搜索与局部搜索能力,跳出局部最优,将标准差分进化算法的变异策略DE/rand/1和DE/best/1进行加权组合,提出两种新的随机扰动加权变异算子。提出一种动态自适应调整缩放因子和交叉概率因子的策略,避免参数设置的麻烦,提高算法的稳定性。在11个Benchmark函数上的测试结果表明,新算法能有效避免早熟收敛,全局寻优能力强,且在高维时寻优速度、求解精度和稳定性均优于4种DE进化算法。     

基于模拟退火的差分变异群搜索优化算法

标准群搜索优化算法易陷入局部最优。为此,引入模拟退火策略和差分进化算子,使算法跳出局部极值点,变异和迭代同时进 行,并保持前期搜索速度快的特性。测试结果证明,改进算法的全局收敛能力明显提高,个体具有良好的人工智能性,能够真实模拟群体行为。     

带自变异算子的粒子群优化算法

针对粒子群优化算法中出现的早熟收敛问题,论文提出了一种带自变异算子的粒子群优化算法。该算法在运行过程中增加了随机变异算子,通过对当前最佳粒子进行随机变异来增强粒子群优化算法跳出局部最优解的能力。对几种典型函数的测试结果表明,新算法的全局搜索能力有了显著提高,并且能够有效避免早熟收敛问题。     

演化硬件技术及其在硬件设计中的应用

本文给出了基于演化算法和可编程逻辑器件的演化硬件的实现原理，并结合一个应用 实例加以说明，分析了该方法与传统的硬件设计方法相比的优越性，最后扼要介绍了演化硬件的研究方法以及它的应用现状和发展方向。     

On the impact of objective function transformations on evolutionary and black-box algorithms

Different objective functions characterize different problems. However, certain fitness transformations can lead to easier problems although they are still a model of the considered problem. In this article, the class of not worsening transformations for a simple population-based evolutionary algorithm (EA) is described completely. That is the class of functions that transfers easy problems in easy ones and difficult problems in difficult ones. Surprisingly, this class for the rank-based EA equals that for all black-box algorithms. The importance of the black-box algorithms' knowledge of the transformation is also pointed out. Hence, a comparison with the class of not worsening transformations for a similar EA which applies fitness-proportional selection, shows that is a proper superset of . Moreover, is a proper subset of the corresponding class for random search. Finally, the minimal and maximal classes of not worsening transformations are described completely, too.     

基于(μ+1)演化策略的多目标优化算法

使用(μ 1)演化策略求解多目标优化问题，利用群体中个体间的距离定义拥挤密度函数以衡量群体中个体的密集程度，个体适应值定义为个体的Pareto强度值和拥挤密度值之和。通过对测试函数的实验，验证了算法的可行性和有效性，该算法具有简单、稳健等特点。     

Hybrid evolutionary algorithm and application to structural optimization

This paper introduces a new evolutionary algorithm with a globally stochastic but locally heuristic search strategy. It is implemented by incorporating a modified micro-genetic algorithm with two local optimization operators. Performance tests using two benchmarking functions demonstrate that the new algorithm has excellent convergence performance when applied to multimodal optimization problems. The number of objective function evaluations required to obtain global optima is only 3.5–3.7% of that of using the conventional micro-genetic algorithm. The new algorithm is used to optimize the design of an 18-bar truss, with the aim of minimizing its weight while meeting the stress, section area, and geometry constraints. The corresponding optimal design is obtained with considerably fewer computational operations than required for the existing algorithms.     

混合性能指标优化问题的进化优化方法及应用

针对混合性能指标优化问题的普遍性及其处理过程中的特点，提出一种混合性能指标优化问题的进化优化方法,首先给出混合性能指标优化问题的定义；然后．确定不同类型和标度的性能指标的转换策略、混合性能指标的个体适应度的赋值方法、以及混合性能指标优化问题的进化优化流程；最后．通过服装设计这一典型的混合性能指标优化问题的仿真验证了算法的有效性．     

动态环境中的进化算法

目前关于进化算法（EA）的研究主要局限于静态优化问题,然而很多现实世界中的问题是动态的,对于这类时变的优化问题通常并不是要求EA发现极值点,而是需要EA能够尽可能紧密地跟踪极值点在搜索空间内的运行轨迹.为此,综述了使EA适用于动态优化问题的各种方法,如增加种群多样性、保持种群多样性、引入某种记忆策略和采用多种群策略等.     

A cascade evolutionary algorithm for the bodyguard allocation problem

The bodyguard allocation problem (BAP) is an optimization problem that illustrates the behavior of processes with contradictory individual goals in some distributed systems. The objective function of this problem is the maximization of a parameter called the social welfare. Although the main method proposed to solve this problem, known as CBAP, is simple and time efficient, it lacks the ability to generate a diverse set of solutions, which is one of the most important feature to improve the chances to reach the global optimum. To overcome this drawback, we address the BAP with an evolutionary algorithm, the EBAP. Later, we take advantage of the best properties of both algorithms, EBAP and CBAP, to generate a two-stage cascade evolutionary algorithm called FFC-BAP. Extensive experimental results show that the algorithm FFC-BAP outperforms both the EBAP and the CBAP, in terms of quality of solutions.     

遗传算法采用进化方向算子的几种策略

提出了采用实数编码情况下应用进化方向算子的几种策略，包括单亲进化方向算子、双亲进化方向算子以及无轮盘赌选择的双亲进化方向算子策略，并进行了数值仿真。仿真结果表明，灵活使用方向进化算子以及遗传操作可大大提高遗传算法的全局搜索能力。     

Hybrid evolutionary learning for synthesizing multi-class pattern recognition systems

This paper describes one aspect of a machine-learning system called HELPR that blends the best aspects of different evolutionary techniques to bootstrap-up a complete recognition system from primitive input data. HELPR uses a multi-faceted representation consisting of a growing sequence of non-linear mathematical expressions. Individual features are represented as tree structures and manipulated using the techniques of genetic programming. Sets of features are represented as list structures that are manipulated using genetic algorithms and evolutionary programming. Complete recognition systems are formed in this version of HELPR by attaching the evolved features to multiple perceptron discriminators. Experiments on datasets from the University of California at Irvine (UCI) machine-learning repository show that HELPR’s performance meets or exceeds accuracies previously published.     

XtalOpt version r7: An open-source evolutionary algorithm for crystal structure prediction

A new version of XtalOpt, a user-friendly GPL-licensed evolutionary algorithm for crystal structure prediction, is available for download from the CPC library or the XtalOpt website, http://xtalopt.openmolecules.net. The new version now supports four external geometry optimization codes (VASP, GULP, PWSCF, and CASTEP), as well as three queuing systems: PBS, SGE, SLURM, and “Local”. The local queuing system allows the geometry optimizations to be performed on the user?s workstation if an external computational cluster is unavailable. Support for the Windows operating system has been added, and a Windows installer is provided. Numerous bugfixes and feature enhancements have been made in the new release as well.

New version program summary

Program title:XtalOptCatalogue identifier: AEGX_v2_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEGX_v2_0.htmlProgram obtainable from: CPC Program Library, Queen?s University, Belfast, N. IrelandLicensing provisions: GPL v2.1 or later [1]No. of lines in distributed program, including test data, etc.: 125 383No. of bytes in distributed program, including test data, etc.: 11 607 415Distribution format: tar.gzProgramming language: C++Computer: PCs, workstations, or clustersOperating system: Linux, MS WindowsClassification: 7.7External routines: Qt [2], Open Babel [3], Avogadro [4], and one of: VASP [5], PWSCF [6], GULP [7], CASTEP [8]Catalogue identifier of previous version: AEGX_v1_0Journal reference of previous version: Comput. Phys. Comm. 182 (2011) 372Does the new version supersede the previous version?: YesNature of problem: Predicting the crystal structure of a system from its stoichiometry alone remains a grand challenge in computational materials science, chemistry, and physics.Solution method: Evolutionary algorithms are stochastic search techniques which use concepts from biological evolution in order to locate the global minimum of a crystalline structure on its potential energy surface. Our evolutionary algorithm, XtalOpt, is freely available for use and collaboration under the GNU Public License. See the original publication on XtalOpt?s implementation [11] for more information on the method.Reasons for new version: Since XtalOpt?s initial release in June 2010, support for additional optimizers, queuing systems, and an operating system has been added. XtalOpt can now use VASP, GULP, PWSCF, or CASTEP to perform local geometry optimizations. The queue submission code has been rewritten, and now supports running any of the above codes on ssh-accessible computer clusters that use the Portable Batch System (PBS), Sun Grid Engine (SGE), or SLURM queuing systems for managing the optimization jobs. Alternatively, geometry optimizations may be performed on the user?s workstation using the new internal “Local” queuing system if high performance computing resources are unavailable. XtalOpt has been built and tested on the Microsoft Windows operating system (XP or later) in addition to Linux, and a Windows installer is provided. The installer includes a development version of Avogadro that contains expanded crystallography support [12] that is not available in the mainline Avogadro releases. Other notable new developments include:

• • 

  LIBSSH [10] is distributed with the XtalOpt sources and used for communication with the remote clusters, eliminating the previous requirement to set up public-key authentication;

• • 

  Plotting enthalpy (or energy) vs. structure number in the plot tab will trace out the history of the most stable structure as the search progresses A read-only mode has been added to allow inspection of previous searches through the user interface without connecting to a cluster or submitting new jobs;

• • 

  The tutorial [13] has been rewritten to reflect the changes to the interface and the newly supported codes. Expanded sections on optimizations schemes and save/resume have been added;

• • 

  The included version of SPGLIB has been updated. An option has been added to set the Cartesian tolerance of the space group detection. A new option has been added to the Progress table?s right-click menu that copies the selected structure?s POSCAR formatted representation to the clipboard;

• • 

  Numerous other small bugfixes/enhancements.

Summary of revisions: See “Reasons for new version” above.Running time: User dependent. The program runs until stopped by the user.References:

•  [1] 

  http://www.gnu.org/licenses/gpl.html.

•  [2] 

  http://www.trolltech.com/.

•  [3] 

  http://openbabel.org/.

•  [4] 

  http://avogadro.openmolecules.net.

•  [5] 

  http://cms.mpi.univie.ac.at/vasp.

•  [6] 

  http://www.quantum-espresso.org.

•  [7] 

  https://www.ivec.org/gulp.

•  [8] 

  http://www.castep.org.

•  [9] 

  http://spglib.sourceforge.net.

• [10] 

  http://www.libssh.org.

• [11] 

  D. Lonie, E. Zurek, Comp. Phys. Comm. 182 (2011) 372–387, doi:10.1016/j.cpc.2010.07.048.

• [12] 

  http://davidlonie.blogspot.com/2011/03/new-avogadro-crystallography-extension.html.

• [13] 

  http://xtalopt.openmolecules.net/globalsearch/docs/tut-xo.html.