一种交互式进化规划及其在供应链问题中的应用

基于适应度函数相对误差和若干参数的选择,提出了一种交互式的进化规划·这种交互方式利于群体初始解和变异,选择操作中方差参数的选取·将此算法应用在一个供应链优化问题中并进行问题的仿真应用·结果表明对于数据量庞大的非线性模型的优化求解,改进后的进化规划算法具有更强和更广泛的适用性     

一种求解多目标数值优化问题的实值量子演化算法

基于多目标优化原理和量子计算原理,提出了一种求解多目标数值优化问题的实值量子演化算法. 该算法除保留求解单目标优化问题的实值量子演化算法的特点外,还有三个主要特征:首先,根据多目标优化特点,使用多目标密度比较算子对种群进行排序和筛选;其次,应用非均匀变异算子保持解的收敛性和提高局部搜索能力;再次,使用多样性保持算子来保持解的多样性. 实验中使用多目标优化测试集并且同NSGA-II算法相比较,结果表明了算法的有效性.     

基于思维进化算法的变压器局部放电源定位新方法

为准确对变压器局部放电源的位置定位,寻求先进的优化计算方法是解决该问题的有效途径。把思维进化算法(MEC)用于变压器局部放电源"声-声"定位。实验结果表明,与基于标准遗传定位算法比较,基于思维进化方法的定位具有精度高、收敛速度快、避免早熟等特点,做到对放电源位置实时在线定位。     

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。     

An improved Cuckoo search algorithm for multi-objective optimization

The recently proposed Cuckoo search algorithm is an evolutionary algorithm based on probability. It surpasses other algorithms in solving the multi-modal discontinuous and nonlinear problems. Searches made by it are very efficient because it adopts Levy flight to carry out random walks. This paper proposes an improved version of cuckoo search for multi-objective problems (IMOCS). Combined with nondominated sorting, crowding distance and Levy flights, elitism strategy is applied to improve the algorithm. Then numerical studies are conducted to compare the algorithm with DEMO and NSGA-II against some benchmark test functions. Result shows that our improved cuckoo search algorithm convergences rapidly and performs efficienly.     

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...     

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.     

一种新的群搜索优化实现算法

针对群搜索优化（GSO）算法存在的不足,提出一种新的GSO实现算法（NRGSO）.采用5个300维和7个30维的测试函数对NRGSO算法进行数值实验,并将其与GSO算法、微粒群优化（PSO）算法、遗传算法（GA）、进化规划（EP）、进化策略（ES）进行比较.结果表明,NRGSO算法的性能优于GSO算法;而在解决高维和多模态函数的优化问题方面,其性能优于PSO、GA、EP和ES等算法.NRGSO算法改进了群搜索优化原实现方法的不足,提高了算法的搜索性能,不仅在高维函数的优化中表现卓越,还能有效地避免陷入局部次优,并且在实际的优化问题中应用方便.     

无网格数值模拟的并行算法研究

对无网格数值模拟的并行算法进行了详细研究.包括使用并行桶搜索算法进行节点搜索,使用并行几何搜索算法进行样点搜索,并行计算无网格形函数及其导数,边界条件的并行处理,使用并行预处理共轭梯度法求解方程组以及负载平衡等.最后给出了无网格数值模拟并行计算的实施流程和计算实例.计算结果表明,无网格数值模拟具有很高的并行性和很好的并行效率,计算规模越大,并行效率越高.     

基于QPSO和MATLAB优化资金投资组合

量子粒子群优化算法(QPSO)是一种基于粒子群优化算法(PSO)的进化算法,它收敛速度快、规则简单、易于编程实现;Matlab是国际控制界公认的标准计算软件。采用QPSO对资金组合投资的多目标问题进行优化,使用Matlab编程,解决了传统方法难以解决的问题,仿真实验表明采用本方法能对资金投资组合问题提出较好的优化决策。     

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     

基于乌鸦搜索算法的新型特征选择算法

基于元启发式算法--乌鸦搜索算法（CrSA）, 提出一种改进的基于乌鸦搜索算法的特征选择算法（IFSCrSA）, 以解决目前特征选择问题中存在的不足. 通过与传统的机器学习特征选择算法和基于进化计算的特征选择算法进行比较, 结果表明, IFSCrSA能在数据集中选择辨识度较强的特征, 不仅大幅度降低了特征子集的规模, 而且提高了分类准确率.     

基于乌鸦搜索算法的新型特征选择算法

基于元启发式算法--乌鸦搜索算法（CrSA）, 提出一种改进的基于乌鸦搜索算法的特征选择算法（IFSCrSA）, 以解决目前特征选择问题中存在的不足. 通过与传统的机器学习特征选择算法和基于进化计算的特征选择算法进行比较, 结果表明, IFSCrSA能在数据集中选择辨识度较强的特征, 不仅大幅度降低了特征子集的规模, 而且提高了分类准确率.     

求解约束优化问题的一种新的进化算法

分析了现有的约束优化进化算法的一些不足之处，提出了一种处理约束优化问题的新算法。新算法将多目标优化思想与全局搜索和局部搜索机制有机地结合起来；在全局搜索过程中，作为一种小生态遗传算法，排挤操作利用Pareto优劣关系比较个体并接受具有相似性的父代个体和予代个体中的优胜者；在局部搜索过程中，首先对局部群体中的个体赋予Pareto强度，然后根据Pareto强度选择个体。通过一个复杂高维多峰测试函数验证了新算法的有效性。     

基于个体强度的自适应差分多目标免疫算法

考虑到支配解可能携带有利于算法搜索到最优解的信息, 在克隆阶段选择一部分非支配解和支配解克隆以提高种群多样性和避免算法早熟收敛。在进化阶段, 先采用自适应差分进化算子交叉变异, 然后用多项式变异算子进行扰动以有效地平衡算法的全局搜索和局部搜索。基于个体强度建立外部文档储存一定数量的较好解, 并让这些较好解在每次迭代中参与进化且被更新。对10个标准测试函数进行仿真实验, 并与其他5种算法进行比较, 结果表明所提算法在收敛性和解的分布性方面均表现出明显优势。     

Asynchronous parallel evolutionary algorithms for constrained optimizations

Recently Guo Tao proposed a stochastic search algorithm in his PhD thesis for solving function optimization problems. He combined the subspace search method (a general multi-parent recombination strategy) with the population hill-climbing method. The former keeps a global search for overall situation, and the latter keeps the convergence of the algorithm. Guo's algorithm has many advantages, such as the simplicity of its structure, the higher accuracy of its results, the wide range of its applications, and the robustness of its use. In this paper a preliminary theoretical analysis of the algorithm is given and some numerical experiments has been done by using Guo's algorithm for demonstrating the theoretical results. Three asynchronous parallel evolutionary algorithms with different granularities for MIMD machines are designed by parallelizing Guo's Algorithm. National Laboratory for Parallel and Distributed Processing Foundation item: Supported by the Natonal Natural Science Foundation of China (No. 70071042, 50073043), the National 863 Hi-Tech Project of China (No. 863-306-ZT06-06-3) and the National Laboratory for Parallel and Distributed Processing. Biography: Kang Li-shan (1934-), male, Professor, research interests: parallel computing and evolutionary computation.     

Trajectory P system

Membrane computing is a branch of natural computing aiming to abstract computing ideas for the structure and the functioning of living cells as well as from the way the cells are organized in tissues or higher-order structures. Trajectories are used as a tool for modeling language operations and other related objects. A trajectory P system consists of a membrane structure in which the object in each membrane is a collection of words and the evolutionary rules are given in terms of trajectories. In this paper, we present some properties of trajectory P systems.     

一种改进的自适应蚁群算法及其应用研究

蚁群算法作为一种新型的模拟进化算法,具有分布计算和信息正反馈等优点,但蚁群算法与其他进化算法一样存在收敛速度慢,易陷于局部最优等缺陷。针对这一问题,提出一种改进的蚁群算法,结合遗传算法和图论中的最邻近算法,并自适应地初始化信息素和限定信息素的大小范围。将该算法应用于旅行商问题(traveling salesman problem,TSP)求解,与基本蚁群算法比较,数值实验结果表明,这种改进算法能有效抑制算法陷入局部最优的缺陷,从而提高了解的全局搜索能力和解的质量。     

A surface-simplex swarm evolution algorithm

In the paper, a particle surface-simplex search (PSSS) is designed based on particle surface-simplex and particle surface- simplex neighborhood. Using PSSS and an evolutionary strategy of multi-states swarm, a surface-simplex swarm evolution (SSSE) algorithm for numerical optimization is proposed. In the experiments, SSSE is applied to solve 17 benchmark problems and compared with the other intelligent optimization algorithms. In the application, SSSE is used to analyze the three intrinsic independent components of gravity earth tide. The results demonstrate that SSSE can accurately find optima or close-to-optimal solutions of the complex functions with high-dimension. The performance of SSSE is stable and efficient.     

An improved group search optimizer for mechanical design optimization problems

This paper presents an improved group search optimizer (iGSO) for solving mechanical design optimization problems. In the proposed algorithm, subpopulations and a co-operation evolutionary strategy were adopted to improve the global search capability and convergence performance. The iGSO is evaluated on two optimization problems of classical mechanical design: spring and pressure vessel. The experimental results are analyzed in comparison with those reported in the literatures. The results show that iGSO has much better convergence performance and is easier to implement in comparison with other existing evolutionary algorithms.