基于混合搜索算法的图像稀疏分解

为了提高图像稀疏分解的效果,降低其计算时间,提出一种基于量子进化算法(quantum-inspired evolutionary algorithm,QIEA)和改进差分进化算法(improved differential evolution,IDE)的混合搜索算法,并应用到图像稀疏分解中。该方法将IDE引入到QIEA中,前期进行QIEA寻优,当寻优搜索到的最优解经过多次进化后没有变化时,引入IDE以提高搜索解的精度和质量。图像稀疏分解的仿真实验结果表明,与QIEA和IDE相比,混合搜索算法的图像稀疏分解方法获得的重构图像具有最好的图像视觉质量和最高的峰值信噪比,且具有相对较低的计算时间。     

基于混合搜索算法的图像稀疏分解

为了提高图像稀疏分解的效果,降低其计算时间,提出一种基于量子进化算法(quantum-inspired evolutionaryalgorithm,QIEA)和改进差分进化算法(improved differential evolution,IDE)的混合搜索算法,并应用到图像稀疏分解中.该方法将1DE引入到QIEA中...     

Some massively parallel algorithms from nature

We introduced the work on parallel problem solvers from physics and biology being developed by the research team at the State Key Laboratory of Software Engineering, Wuhan University. Results on parallel solvers include the following areas: Evolutionary algorithms based on imitating the evolution processes of nature for parallel problem solving, especially for parallel optimization and model-building; Asynchronous parallel algorithms based on domain decomposition which are inspired by physical analogies such as elastic relaxation process and annealing process, for scientific computations, especially for solving nonlinear mathematical physics problems. All these algorithms have the following common characteristics: inherent parallelism, self-adaptation and self-organization, because the basic ideas of these solvers are from imitating the natural evolutionary processes. Foundation item: Supported by the National Natural Science Foundation of China (No. 60133010, No. 70071042, No. 60073043) and National Laboratory for Parallel and Distributed Processing Biography: Li Yan (1974-), female, Ph. D candidate, research direction: evolutionary computation.     

基于多目标混合进化算法的多台TCSC控制器协调设计

将多台可控串联补偿器(TCSC)之间的协调运行问题转化为多目标优化问题,详细介绍了一种基于进化规划和粒子群优的多目标混合进化算法(MOEPPSO),提出了基于MOEPPSO的协调控制器设计方法.采用多目标混合进化算法优化控制器参数,得到一组Pareto参数解集,为运行人员提供更丰富、准确的信息.在装有两台TCSC的IEEE典型四机两区域系统研究实例中,非线性时域仿真验证了所提方法的有效性.与单独设计控制器的方法相比较,所提方法能够更好地提高互联系统的稳定性.     

A parallel global-local mixed evolutionary algorithm for multimodal function optimization based on domain decomposition

This paper presents a parallel two-level evolutionary algorithm based on domain decomposition for solving function optimization problem containing multiple solutions. By combining the characteristics of the global search and local search in each sub-domain, the former enables individual to draw closer to each optima and keeps the diversity of individuals, while the latter selects local optimal solutions known as latent solutions in sub-domain. In the end, by selecting the global optimal solutions from latent solutions in each sub-domain, we can discover all the optimal solutions easily and quickly. Foundation item: Supported by the National Natural Science Foundation of China (60133010,60073043,70071042) Biography: Wu Zhi-jian(1963-), male, Associate professor, research direction: parallel computing, evolutionary computation.     

基于量子的免疫进化算法及收敛性

分析和探讨了量子计算的特点及免疫进化机制,并结合免疫系统的动力学模型和免疫细胞在自我进化中的亲和度成熟机理,提出了一种基于量子计算的免疫进化算法。该算法使用量子比特表达染色体,通过免疫克隆、记忆细胞产生和抗体相似性抑制等进化机制可最终找出最优解,它比传统的量子进化算法具有更好的种群多样性、更快的收敛速度和全局寻优能力。在此不仅从理论上证明了该算法的收敛,而且通过仿真实验表明了该算法的优越性。     

连续变量函数全局优化算法—列队竞争算法

提出了一种全局优化搜索新算法——列队竞争算法．算法在模拟进化过程中,始终保持着独立并行进化的家族,通过家族内部的生存竞争和家族间的地位竞争这两种不同的竞争方式,使群体快速进化到最优或接近最优的区域．根据家族的目标函数值大小排成列队,并按家族在列队中的地位不同获得不同的竞争推动力,使得各个家族在列队中的位置发生动态的变化,从而使得局部搜索与全局搜索达到均衡．数值计算结果表明,列队竞争算法具有在复杂搜索空间内迅速搜索到最优解的能力     

基于蜜蜂进化型遗传算法的电力系统无功优化

采用蜜蜂进化机制与遗传算法相结合的蜜蜂进化型遗传算法(bee evolutionary genetic algo-rithm,BEGA)对电力系统进行无功优化计算.该算法以一定概率将蜂王(最优个体)与雄蜂(被选的个体)2部分进行交叉,因此对最优个体包含信息的开采能力得以增强.随机种群的引入,降低了算法出现过早收敛的可能性,保持了种群多样性.应用BEGA对IEEE6节点系统进行无功优化计算的结果表明:较其他算法,BEGA具有更强的全局寻优能力和更快的收敛速度.     

A Multi-objective optimal evolutionary algorithm based on tree-ranking

Multi-objective optimal evolutionary algorithms (MOEAs) are a kind of new effective algorithms to solve Multi-objective optimal problem (MOP). Because ranking, a method which is used by most MOEAs to solve MOP, has some shortcomings, in this paper, we proposed a new method using tree structure to express the relationship of solutions. Experiments prove that the method can reach the Pare-to front, retain the diversity of the population, and use less time. Foundation item: Supported by the National Natural Science Foundation of China(60073043, 70071042, 60133010) Biography: Shi Chuan( 1978-), male, Master candidate, research direction; intellective computation, evolutionary computation.     

关于多目标演化算法的策略分析

基于Pareto排序算法的多目标演化算法是多目标演化算法所采用的重要方法,本文叙述了多目标演化算法(MOEAs)的有关概念,在分析已有算法的一些性能和特征的基础上,结合演化算法的有关概念,重点基于Pareto排序算法分析了影响多目标演化算法性能的两大方面:求解过程中解集合的多样性、均匀性分布的保持与维护以及解的收敛性,分析了MOEAs设计中需要注意的策略问题以及今后研究的重点.     

A new evolutionary algorithm based on the decimal coding

Traditional Evolutionary Algorithm (EAs) is based on the binary code, real number code, structure code and so on. But these coding strategies have their own advantages and disadvantages for the optimization of functions. In this paper a new Decimal Coding Strategy (DCS), which is convenient for space division and alterable precision, was proposed, and the theory analysis of its implicit parallelism and convergence was also discussed. We also redesign several genetic operators for the decimal code. In order to utilize the historial information of the existing individuals in the process of evolution and avoid repeated exploring, the strategies of space shrinking and precision alterable, are adopted. Finally, the evolutionary algorithm based on decimal coding (DCEAs) was applied to the optimization of functions, the optimization of parameter, mixed-integer nonlinear programming. Comparison with traditional GAs was made and the experimental results show that the performances of DCEAS are better than the tradition GAs. Foundation item: Supported by the National Natural Science Foundation of China (No. 69703011) Biography: Dong Wen-yong (1973-), male Ph. D. candidate, research direction: parallel algorithms, evolutionary computation, computer simulation.     

Solving a kind of high complexity multi-objective problems by a fast algorithm

A fast algorithm is proposed to solve a kind of high complexity multi-objective problems in this paper. It takes advantages of both the orthogonal design method to search evenly, and the statistical optimal method to speed up the computation. It is very suitable for solving high complexity problems, and quickly yields solutions which converge to the Pareto-optimal set with high precision and uniform distribution. Some complicated multi-objective problems are solved by the algorithm and the results show that the algorithm is not only fast but also superior to other MOGAS and MOEAs, such as the currently efficient algorithm SPEA, in terms of the precision, quantity and distribution of solutions. Foundation item: Supported by the National Natural Science Foundation of China (60204001, 70071042, 60073043, 60133010) and Youth Chengguang Project of Science and Technology of Wuhan City (20025001002). Biography: Zeng San-you ( 1963-), male, Associate professor, research direction: evolutionary computing, parallel computing     

基于进化计算的机器人操作器等强度优化设计

机器人操作器等强度优化设计是一个强非线性优化设计问题，文章将遗传算法与进化编程有机结合起来，形成一种既能处理复杂约束条件，又能收敛于全局最优点的优化方法；并将该方法应用于机器人操作器的等强度优化设计问题，得到操作器等强度设计的最优形状。     

利用基因算法实现参数优化的研究

优化问题的实质是搜索空间中寻找一个点，在满足一定的条件下，使其给定的性质指标达以最大值（或最小值），基因算法（ＧＡ）是一种新型的基于遗传学理论，模拟生命进化机制来帝现搜索和优化的算法，该文以一定的目标函数进行了研究，结果表明，该处法能以很大的概率趋于全局最优解。     

Applying evolutionary algorithm to public key cryptosystems

A best algorithm generated scheme is proposed in the paper by making use of the thought of evolutionary algorithm, which can generate dynamically the best algorithm of generating primes in RSA cryptography under different conditions. Taking into account the factors of time, space and security integrated, this scheme possessed strong practicability. The paper also proposed a model of multi-degree parallel evolutionary algorithm to evaluate synthetically the efficiency and security of the public key cryptography. The model contributes to designing public key cryptography system too. Foundation item: Supported by the Hi-Tech Research and Development Foundation item: Supported by the Hi-Tech Research and Development Biography; Tu Hang (1975-), male, Ph. D candidate, research directions: Biography; Tu Hang (1975-), male, Ph. D candidate, research directions:     

车间生产调度中动态小生境协同进化算法

车间生产调度是企业生产的重要环节。为避免遗传算法在求解多车间协同调度时早期成熟和陷入局部最优解,以及收敛速度慢的问题,特引入一种基于动态小生境集的多种群协同进化模型。在基于工序的染色体编码方法的基础上,利用交叉算子和变异算子调整加工顺序和多工艺路线选择。融合动态小生境集技术和多种群协同进化方法,实现多工艺路线下多车间协同生产调度的优化求解。实验表明,该方法具有良好的优越性。     

Two-phase genetic algorithm applied in the optimization of multi-modal function

This paper presents a two-phase genetic algorithm (TPGA) based on the multi-parent genetic algorithm (MPGA). Through analysis we find MPGA will lead the population’s evolvement to diversity or convergence according to the population size and the crossover size, so we make it run in different forms during the global and local optimization phases and then forms TPGA. The experiment results show that TPGA is very efficient for the optimization of low-dimension multi-modal functions) usually we can obtain all the global optimal solutions. Foundation item: Supported by the National Natural Science Foundation of China (70071042, 60073043,60133010) Biography: Huang Yu-zhen ( 1977-), female, Master candidate, research direction; evolution computation.     

A two-level subspace evolutionary algorithm for solving multi-modal function optimization problems

In this paper, a new algorithm for solving multimodal function optimization problems-two-level subspace evolutionary algorithm is proposed. In the first level, the improved GT algorithm is used to do global recombination search so that the whole population can be separated into several niches according to the position of solutions; then, in the second level, the niche evolutionary strategy is used for local search in the subspaces gotten in the first level till solutions of the problem are found. The new algorithm has been tested on some hard problems and some good results are obtained. Foundation item: Supported by the National Natural Science Foundation of China (70071042, 60073043, 60133010). Biography: Li Yan( 1974-), female, Ph. D candidate, research interest: evolutionary computation.     

The evolutionary computation techniques for protein structure prediction: A survey

In this paper, the applications of evolutionary al gorithm in prediction of protein secondary structure and tertiary structures are introduced, and recent studies on solving protein structure prediction problems using evolutionary algorithms are reviewed, and the challenges and prospects of EAs applied to protein structure modeling are analyzed and discussed. Foundation item: Supported by the National Natural Science Foundation of China( 60133010,70071042,60073043) Biography: Zou Xiu-fen ( 1966-), female, Associate professor, research direction:evolutionary computing, parallel computing, bioinformatics.     

A genetic algorithm on multiple sequences alignment problems in biology

The study and comparison of sequences of characters from a finite alphabet is relevant to various areas of science, notably molecular biology. The measurement of sequence similarity involves the consideration of the possible sequence alignments in order to find an optimal one for which the “distance” between sequences is minimum. In biology informatics area, it is a more important and difficult problem due to the long length (100 at least) of sequence, this cause the compute complexity and large memory require. By associating a path in a lattice to each alignment, a geometric insight can be brought into the problem of finding an optimal alignment, this give an obvious encoding of each path. This problem can be solved by applying genetic algorithm, which is more efficient than dynamic programming and hidden Markov model using commomly now. Foundation item: Supported by Zi-qiang Foundation of Wuhan University and Open Foundation of the State Key-Laboratory of Software Engineering, Wuhan University Biography: Shi Feng(1966-), male, Associate professor, research direction: bioinformatics.